Lorena Chanes

Ana B Chica

Romain Quentin

Dr Antoni Valero-Cabre
email: avalerocabre@gmail.com

Keywords: Frontal Eye Fields, Oscillation Phase, Visual Discrimination, Conscious Visual Perception, Visual Performance Enhancements, Rhythmic Transcranial Magnetic Stimulation, Plasticity, Visual Cognition

CHAPTER I: Introduction and Aims I.I. Visual perception I.II. Neural bases of cognitive processes involved in visual performance I.II.1. Neural bases of visuo-spatial attention I.II.2. Neural bases of conscious access I.III. Neural dynamics of visuo-spatial attention and conscious access I.III.1. Neural dynamics of visuospatial attention I.III.2. Neural dynamics of conscious access I.IV. Noninvasive brain stimulation methods and approaches I.IV.1. Transcranial magnetic stimulation (TMS) I.IV.2. TMS and the visual system I.IV.3. TMS and brain oscillations I.V. The frontal eye fields, the intraparietal sulcus and their manipulation through noninvasive brain stimulation I.VI.

SUMMARY

Frontal and parietal areas have been shown to subtend different cognitive processes such as attentional orienting, decision making and access to consciousness, with bearing on visual performance. In spite of prior evidence supporting an implication of those regions in visual cognition, their contributions to the processing of low-contrast unmasked stimuli and the characteristic spatiotemporal activity patterns underlying them remain to be fully explored and causation is lacking. We here addressed a thorough exploration of such contributions in humans, with an emphasis on the dynamics of neural activity and visual performance enhancements as probed by patterns of noninvasive manipulation of local brain oscillatory activity. To this end, we tested in healthy participants the effects of either single pulses or short bursts of active vs. sham transcranial magnetic stimulation (TMS), delivered to the frontal eye field (FEF) and the intraparietal sulcus (IPS) prior to the presentation of a lateralized low-contrast near-threshold Gabor stimulus, on the visual discrimination and conscious detection of such stimulus. In Chapter II we show that single TMS pulses delivered to the right FEF alone or in combination with visuo-spatial cues have the ability to increase perceptual sensitivity, effects that proved strongly modulated by cue validity. In Chapter III we report that induced pre-target high-beta (30 Hz) activity in this same region selectively enhances perceptual sensitivity (d') whereas gamma band (50 Hz) bursts selectively shift response criterion (beta). This result shows that neural oscillations could be a general mechanism to multiplex functions, with specific behavioral effects, in the same region or network. In Chapter IV we provide evidence of stochastic facilitation of perceptual performance, showing perceptual sensitivity enhancements by the induction of neural noise to the left FEF, and demonstrate that the left and right hemispheres modulate visual performance through different coding strategies possibly reflecting different cognitive processes. In Chapter V we show that the effects of right-FEF high-beta activity on perceptual sensitivity are phase independent and that additional perceptual phase-dependent effects can be observed. Finally, in Chapter VI, we report for the right IPS similar frequency-specific effects to the ones observed in the right FEF as well as additional perceptual modulations when the pulses are delivered in a short time window. Our findings contribute to better substantiate the oscillatory basis of visual cognition and its associated behaviors and to set the stage for the development of novel therapies based on noninvasive manipulation of dysfunctional brain oscillatory activity.

RESUME

Les aires cérébrales frontales et pariétales sont impliquées dans différents processus cognitifs importants pour la performance visuelle, tels que l'attention ou la conscience. Malgré les preuves existantes en faveur d'une implication de ces régions dans la cognition visuelle, leurs contributions dans le traitement de stimuli non masqués de faible contraste ainsi que l'activité spatio-temporelle sous-tendant ces contributions restent largement inexplorées, tout particulièrement en termes de causalité. Nous avons mené une exploration approfondie de ces contributions chez l'humain, en mettant l'accent sur la dynamique de l'activité neurale et les améliorations perceptives potentielles qui peuvent résulter de la manipulation non invasive de l'activité cérébrale. À cette fin, nous avons testé chez des sujets sains les effets d'impulsions simples ou de rafales courtes de stimulation magnétique transcrânienne (SMT) réelle versus fausse, délivrée sur le champ oculomoteur frontal ou le sillon intrapariétal avant la présentation d'un filtre de Gabor de faible contraste, sur la discrimination et la détection consciente de ce filtre de Gabor. Nos résultats montrent que chez l'humain, la distribution spatio-temporelle de l'activité frontale et pariétale joue un rôle causal dans la performance visuelle. Nos recherches contribuent à mieux comprendre les bases oscillatoires de la cognition visuelle et les comportements associés et à préparer le terrain pour le développement de nouvelles thérapies basées sur la manipulation non-invasive de l'activité cérébrale oscillatoire avec, pour objectif ultime, l'amélioration des pathologies neuropsychiatriques.

Chapter I

Introduction and Aims

At every moment of our lives, our senses are hit by much more information than we can treat. To succeed in using sensory inputs to act, cognitive processes such as attentional orienting help us prioritize those data that are behaviorally meaningful, selecting stimuli involuntarily because of their physical saliency or voluntarily according to their granted relevance to achieve our behavioral goals in a given context. Moreover, information that accesses consciousness will be able to be largely stored and strategically used and, in order to produce goal-directed behavior, evidence will need to be flexibly translated into motor actions through decisionmaking processes. All these operations shape the way we act and perform.

In this introduction we will present a brief overview of the current knowledge on the neural basis of visual perception, and some of the processes relevant for perceptual performance such as attention and conscious access. We will then address how noninvasive neurostimulation techniques can be used to manipulate activity in different brain regions and their associated networks and provide new insights on their role and processing features.

I.I. Visual perception

Vision is one of the main senses in humans and it has been extensively studied in virtually all organisms. In order to produce a visual percept, light reaches the retina, which contains a large number of photoreceptors. These photoreceptors convert this incoming radiation into electrical signals that are conveyed to the brain through the optic nerve. Most part of the retinal ganglionar cell axons (about 90%) relay on the lateral geniculate nucleus (LGN) of the thalamus to then reach the primary visual cortex (V1 or striate cortex). A few projections (about 10%) relay on the superior colliculus in the midbrain [START_REF] Perry | Retinal ganglion cells that project to the superior colliculus and pretectum in the macaque monkey[END_REF], which is known as the indirect retino-tectal pathway. The superior colliculus influences the visual and posterior parietal regions indirectly through the pulvinar nucleus of the thalamus and receives modulatory projections from the primary visual cortex [START_REF] Shipp | The brain circuitry of attention[END_REF]).

The primary visual cortex is retinotopically organized [START_REF] Hubel | Receptive fields of single neurones in the cat's striate cortex[END_REF] and devoted to the processing of basic visual features, as for example spatial frequency (De Valois et al. 1982a;[START_REF] Maffei | The visual cortex as a spatial frequency analyser[END_REF][START_REF] Tootell | Spatial frequency columns in primary visual cortex[END_REF], line orientation (De Valois et al. 1982b) and stimulus contrast [START_REF] Hawken | Contrast sensitivity and orientation selectivity in lamina IV of the striate cortex of Old World monkeys[END_REF]. From V1 information is transmitted to the extrastriate cortex (V3, V4 and MT/V5), where higher order processing takes place [START_REF] Orban | Higher order visual processing in macaque extrastriate cortex[END_REF]. Lesions to V1 involve a decrease of visually evoked activity in extrastriate visual areas and temporal cortices [START_REF] Gross | Contribution of striate cortex and the superior colliculus to visual function in area MT, the superior temporal polysensory area and the inferior temporal cortex[END_REF][START_REF] Rocha-Miranda | Visual activation of neurons in inferotemporal cortex depends on striate cortex and forebrain commissures[END_REF]. Moreover, visual perception is thought to involve feedback and feed-forward activations within and amongst V1 and higher order visual areas [START_REF] Hupe | Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons[END_REF].

Two pathways have been identified for the flow of information from the primary visual cortex to higher order areas: the dorsal and the ventral streams. The ventral stream (also known as the "what" stream) travels from the occipital to the inferior temporal cortex, including V2 and V4 and it is associated with the visual recognition and categorization of objects and faces [START_REF] Kanwisher | The fusiform face area: a module in human extrastriate cortex specialized for face perception[END_REF][START_REF] Kourtzi | Cortical regions involved in perceiving object shape[END_REF]. Lesions to the ventral visual stream result in visual agnosias (Ffytche et al. 2010). For example, in the early 90s an influential study described the case of a patient with a lesion of the ventral stream who presented a severe visual form agnosia [START_REF] Goodale | A neurological dissociation between perceiving objects and grasping them[END_REF]. This patient, however, had preserved guidance of hand movements with regards to objects the qualities of which (e.g. orientation) he failed to perceive. This preserved visuomotor ability would be coded in the dorsal visual stream. Indeed, the dorsal stream (also known as the "where"/"how" stream) is associated with spatial localization and visuomotor control and projects from the occipital to the posterior parietal cortex, including areas V2, V3a, middle temporal (MT) and middle superior temporal (MST) cortices. Lesions to the dorsal visual stream result in a variety of cognitive dysfunctions including spatial neglect (Heilman et al. 1993), which involves inability to perceive and/or attend to stimuli contralateral to the lesion, and optic ataxia [START_REF] Battaglia-Mayer | Optic ataxia as a result of the breakdown of the global tuning fields of parietal neurones[END_REF][START_REF] Pisella | An 'automatic pilot' for the hand in human posterior parietal cortex: toward reinterpreting optic ataxia[END_REF][START_REF] Pisella | Optic ataxia and the function of the dorsal stream: contributions to perception and action[END_REF], which involves failure to integrate visual and motor information and thus to perform adapted visuomotor plans. The two streams are thought to interact to contribute to action and perception [START_REF] Goodale | An evolving view of duplex vision: separate but interacting cortical pathways for perception and action[END_REF][START_REF] Milner | Delayed reaching and grasping in patients with optic ataxia[END_REF][START_REF] Westwood | Perceptual illusion and the real-time control of action[END_REF]. For example, a patient with a bilateral lesion of the ventral stream presenting visual form agnosia, has impaired ability to adjust hand aperture to different object sizes [START_REF] Goodale | Differences in the visual control of pantomimed and natural grasping movements[END_REF]) and increased pointing errors to remembered target positions [START_REF] Milner | A paradoxical improvement of misreaching in optic ataxia: new evidence for two separate neural systems for visual localization[END_REF]) when the action is delayed for a few seconds. Conversely, dorsal-stream lesion patients with optic ataxia improve when there is a delay between target and movement onset (Himmelbach and Karnath 2005;[START_REF] Milner | Delayed reaching and grasping in patients with optic ataxia[END_REF][START_REF] Milner | A paradoxical improvement of misreaching in optic ataxia: new evidence for two separate neural systems for visual localization[END_REF][START_REF] Revol | Pointing errors in immediate and delayed conditions in unilateral optic ataxia[END_REF]. It has been suggested that such impairment and enhancement are mediated by the spared stream.

I.II. Neural bases of cognitive processes involved in visual performance

Cognitive processes such as attention and conscious access shape our performance with regards to sensory stimuli. In the last decades, research at different levels, from invasive single neuron recordings in nonhuman primates to noninvasive neuroimaging studies, have focused on mapping the brain systems of these complex processes, which tend to overlap at least partially and thus are difficult to dissect out.

I.II.1. Neural bases of visuo-spatial attention

Attentional orienting is crucial for an adaptive interaction of organisms with their environment, allowing them to pursue their behavioral goals while being capable of responding to unexpected but behaviorally relevant events. For example, the allocation of attentional resources in a particular region of the space improves the processing of stimuli presented in such region [START_REF] Carrasco | Spatial covert attention increases contrast sensitivity across the CSF: support for signal enhancement[END_REF]Yeshurun and Carrasco 1998). Attention can be oriented in space either endogenously (voluntarily or top-down) or exogenously (involuntarily or bottom-up), and both types interact dynamically to allow appropriate perceptual behavior. In the mid-eighties, Posner and collaborators made popular the cue-target paradigm, which since then has been widely used to study visuospatial attention [START_REF] Posner | Orienting of attention[END_REF]Posner et al. 1980;[START_REF] Posner | Effects of parietal injury on covert orienting of attention[END_REF]. In this paradigm, participants have to detect or discriminate, as fast and as accurately as possible, a visual target presented on a screen that is preceded by a spatial cue displayed either centrally (e.g. an arrow) or peripherally (e.g. a salient dot near one of the potential locations of the target) and informative or not about target location. Peripheral non-informative cues engage exogenous attention, since the cue captures attention by its physical saliency, whereas central informative cues can be used strategically by participants to endogenously orient their attention towards the expected location of the target. All these cues typically decrease reaction times when they correctly predict the location of the target (valid trials) as compared to when target location is incorrectly predicted (invalid trials).

Early evidence from neurological patients and physiological studies monkeys has indicated the importance of frontal and parietal regions for visuospatial attention (e.g. [START_REF] Goldberg | Cerebral cortical activity associated with the orientation of visual attention in the rhesus monkey[END_REF][START_REF] Mesulam | A cortical network for directed attention and unilateral neglect[END_REF][START_REF] Posner | Effects of parietal injury on covert orienting of attention[END_REF]). More recently, investigators have searched for the neural basis of spatial attention in the healthy human brain using correlational techniques such as functional MRI (fMRI). One of the first studies to dissociate the brain activity related to attention from the activity related to the processing of the target revealed the superior frontal, inferior parietal and superior temporal cortices as the regions activated by the cues (Hopfinger et al. 2000). Taken together, neuroimaging studies have led to the proposal of two cortical networks that are involved in visuospatial attention (Corbetta et al. 2002). According to this model, a bilateral dorsal fronto-parietal network, including the intraparietal sulcus (IPS) and the frontal eye fields (FEF), would be in charge of orienting our attention in space. Indeed, the FEF and the IPS have been shown in humans and non-human primates to reflect the locus of attention (Armstrong et al. 2009;[START_REF] Bisley | Neuronal activity in the lateral intraparietal area and spatial attention[END_REF][START_REF] Kelley | Cortical mechanisms for shifting and holding visuospatial attention[END_REF][START_REF] Thompson | Dissociation of visual discrimination from saccade programming in macaque frontal eye field[END_REF][START_REF] Thompson | Neuronal basis of covert spatial attention in the frontal eye field[END_REF]. A right-lateralized ventral network, including the temporoparietal junction (TPJ) and the inferior frontal gyrus (IFG), would be involved in the reorientation of attention towards unexpected and task-relevant events (Corbetta et al. 2008).

Recent studies have pointed out the importance of white matter tracts linking these cortical regions, in particular the superior longitudinal fascicle (SLF) and the inferior fronto-occipital fascicle (IFOF), for spatial attention. The SLF appears to present a dorsal to ventral increase in lateralization, with its dorsal-most branch (SLF I), overlapping with the dorsal attentional network, being symmetrically distributed in both hemispheres and its most ventral branch (SLF III), overlapping the ventral attentional network, being right-lateralized (Thiebaut de Schotten et al. 2011). It has also been hypothesized that the middle branch of the SLF (SLF II), overlapping with the anterior portion of the dorsal network and the posterior portion of the ventral network, would link both networks (Thiebaut de Schotten et al. 2011). Spatial attention has also been shown to involve subcortical regions, particularly the superior colliculus (SC) and the pulvinar nucleus of the thalamus [START_REF] Shipp | The brain circuitry of attention[END_REF]. The SC contains a retinotopic map of the external world. It receives direct input from the frontal, parietal and visual cortices and it is thought to be involved in both endogenous and exogenous attention (Fecteau et al. 2004;[START_REF] Kustov | Shared neural control of attentional shifts and eye movements[END_REF][START_REF] Rafal | Orienting of visual attention in progressive supranuclear palsy[END_REF]). Moreover, it has recently been shown to present sustained activity in those neurons coding for the attended location [START_REF] Ignashchenkova | Neuron-specific contribution of the superior colliculus to overt and covert shifts of attention[END_REF]). The pulvinar is widely interconnected with the cortex and has been proposed to coordinate activity within multiple cortical regions [START_REF] Petersen | Contributions of the pulvinar to visual spatial attention[END_REF][START_REF] Saalmann | The pulvinar regulates information transmission between cortical areas based on attention demands[END_REF][START_REF] Snow | Impaired attentional selection following lesions to human pulvinar: evidence for homology between human and monkey[END_REF].

I.II.2. Neural bases of conscious access

In the last decades, a growing body of experimental work has revealed that sensory stimuli that do not access consciousness are however treated by the brain and can influence our behavior. As an early example, in the seventies, it was discovered that some patients with lesions to the primary visual cortex and associated visual deficits could perform over chance levels in simple tasks on stimuli presented in the blind part of his visual field where they reported not to see anything [START_REF] Poppel | Leter: Residual visual function after brain wounds involving the central visual pathways in man[END_REF][START_REF] Weiskrantz | Visual capacity in the hemianopic field following a restricted occipital ablation[END_REF]. Weiskrantz and colleagues named this condition blindsight [START_REF] Weiskrantz | Visual capacity in the hemianopic field following a restricted occipital ablation[END_REF]).

More recently, several investigators have studied the cerebral processing of stimuli that do not access consciousness in healthy participants using different paradigms that allow rendering stimuli subliminal and study conscious vs. unconscious processing, including near-threshold stimuli, masking, bistable images, binocular rivalry, inattentional blindness and change blindness. Using masked stimuli, it has been shown for example that the emotional valence of subliminal stimuli can be treated by the amydgala [START_REF] Whalen | Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge[END_REF]. This evidence has been supported by further data in a cortically blind patient (named G.Y.) who had massive damage to the left occipital lobe, sparing the occipital pole only, as well as a destruction of the optic radiation [START_REF] Barbur | Conscious visual perception without V1[END_REF]. This patient was capable of discriminating over chance levels emotional facial expressions presented to his blind visual field [START_REF] De Gelder | Non-conscious recognition of affect in the absence of striate cortex[END_REF]) and showed activation of the amygdala (without activation of the fusiform face area) for fear vs. happy faces presented in such region [START_REF] Morris | Differential extrageniculostriate and amygdala responses to presentation of emotional faces in a cortically blind field[END_REF].

Moreover, studies have reported the existence of subliminal priming at different levels, visual, (Dehaene et al. 2001), semantic [START_REF] Naccache | Unconscious semantic priming extends to novel unseen stimuli[END_REF][START_REF] Van Den Bussche | Masked primes can be genuinely semantically processed: a picture prime study[END_REF], and have shown that subliminal monetary incentives can influence subjects' motivation [START_REF] Pessiglione | How the brain translates money into force: a neuroimaging study of subliminal motivation[END_REF] indicating that subliminal information is not only treated by the brain but can also affect our behavior. Such influence is, however, potentially very limited (Dehaene and Changeux 2011;Dehaene and Naccache 2001). For example, it appears to decrease with time, as suggested by subliminal priming studies showing that the effect is observed only when the prime-to-target time is under 500 ms (Dupoux et al. 2008;[START_REF] Greenwald | Three cognitive markers of unconscious semantic activation[END_REF][START_REF] Mattler | Inhibition and decay of motor and nonmotor priming[END_REF], and it is often unable to flexibly modulate cognitive control (e.g. [START_REF] Kinoshita | Unconscious cognition isn't that smart: modulation of masked repetition priming effect in the word naming task[END_REF][START_REF] Kunde | Conscious control over the content of unconscious cognition[END_REF]. So, the brain treats sensory information that does not access consciousness and such information is able to affect our behavior.

Conscious access of sensory information is characterized by a subjective reportable experience, which constitutes an operational definition. We can consider that a sensory stimulus has gained access to consciousness when the subject is able to report it (Dehaene and Changeux 2011;[START_REF] Tallon-Baudry | On the neural mechanisms subserving consciousness and attention[END_REF]. Several fMRI studies have shown that the activation of cortical sensory regions is not sufficient for information to be conscious, but that conscious experience is often accompanied of an amplification of the activity in those regions [START_REF] Polonsky | Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry[END_REF][START_REF] Williams | Attenuation of neural responses in primary visual cortex during the attentional blink[END_REF], as well as fronto-parietal activations [START_REF] Carmel | Conscious awareness of flicker in humans involves frontal and parietal cortex[END_REF]Dehaene et al. 2001). EEG, MEG and intracortical recordings have helped characterize temporally conscious experience, revealing robust conscious correlates at late stages (>300 ms) of information processing [START_REF] Babiloni | Visuo-spatial consciousness and parieto-occipital areas: a high-resolution EEG study[END_REF]Del Cul et al. 2007;Fernandez-Duque et al. 2003;[START_REF] Lamy | Neural correlates of subjective awareness and unconscious processing: an ERP study[END_REF][START_REF] Niedeggen | Change blindness and time to consciousness[END_REF][START_REF] Pins | The neural correlates of conscious vision[END_REF][START_REF] Quiroga | Human single-neuron responses at the threshold of conscious recognition[END_REF][START_REF] Sergent | Timing of the brain events underlying access to consciousness during the attentional blink[END_REF], although early correlations have also been reported [START_REF] Pins | The neural correlates of conscious vision[END_REF]. Other studies have also suggested that consciousness is an all-or-none process [START_REF] Dehaene | A neuronal network model linking subjective reports and objective physiological data during conscious perception[END_REF][START_REF] Quiroga | Human single-neuron responses at the threshold of conscious recognition[END_REF][START_REF] Sergent | Timing of the brain events underlying access to consciousness during the attentional blink[END_REF][START_REF] Sergent | Neural processes underlying conscious perception: experimental findings and a global neuronal workspace framework[END_REF], meaning that there would be a sharp rather than continuous transition between unconscious and conscious perception. Interestingly, the above-mentioned studies in humans are in agreement with prior monkey data. In particular, recordings from several lower and higher visual areas during binocular rivalry showed that cells in those regions increase their firing rate when their preferred stimulus is perceived, supporting the idea of a distributed neural substrate for visual awareness [START_REF] Logothetis | What is rivalling during binocular rivalry?[END_REF][START_REF] Sheinberg | The role of temporal cortical areas in perceptual organization[END_REF]. A first neuronal response period is thought to reflect sensory evidence while a later response period has been shown to correlate with stimulus detection in the primary visual, inferotemporal and frontal cortices [START_REF] Kovacs | Cortical correlate of pattern backward masking[END_REF][START_REF] Lamme | Masking interrupts figure-ground signals in V1[END_REF][START_REF] Super | Two distinct modes of sensory processing observed in monkey primary visual cortex (V1)[END_REF]Thompson and Schall 1999;[START_REF] Thompson | Antecedents and correlates of visual detection and awareness in macaque prefrontal cortex[END_REF], supporting the idea of late correlates of conscious access.

Several models of conscious access have used reportability as an operational definition for consciousness. Of all of them, the global workspace remains one of the most influential ones [START_REF] Baars | A cognitive theory of consciousness[END_REF]. According to this model, a dominant coalition of specialized neural processers is selected amongst several that perform their operations in parallel due to its pertinence for the organism's behavioral goals. This coalition sends its result to the global workspace, from which it can be broadcasted to the rest of the system, what would constitute conscious access. Based on this idea, the global neuronal workspace model proposed a neural substrate for the workspace [START_REF] Dehaene | Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness[END_REF][START_REF] Dehaene | Conscious, preconscious, and subliminal processing: a testable taxonomy[END_REF]Dehaene et al. 1998;Dehaene and Naccache 2001;[START_REF] Dehaene | A neuronal network model linking subjective reports and objective physiological data during conscious perception[END_REF], which included long-range axonal projection neurons, densely distributed in the prefrontal and parietal cortices, capable of conveying information to distant regions. It is the sudden 'ignition' of such systems what would correspond to conscious access and they would then stay in a reverberant state that would allow retaining the information. Another model for which reverberant, reentering loops are a key element is Victor Lamme's proposal [START_REF] Lamme | Towards a true neural stance on consciousness[END_REF]. He proposes that it is precisely the reentering loops what creates consciousness. When information arrives at V1, it rapidly progresses anteriorly and then there is reverberation and that reverberation, particularly in ventral regions, creates an integrated state in which according to Lamme the information is already conscious. For Lamme this state would correspond to phenomenal consciousness, which precedes and exceeds reportability. Then that reverberation would extend to fronto-parietal regions and we would have reportability. Another influential model, the information integration theory [START_REF] Tononi | Consciousness as integrated information: a provisional manifesto[END_REF][START_REF] Tononi | Consciousness and the integration of information in the brain[END_REF], has focused on two general properties of conscious experience: integration (i.e. a conscious scene is unified) and differentiation or complexity (i.e., a conscious scene involves the selection of a given conscious 'state' among a huge repertoire). According to this model, differentiation (i.e. the availability of a rich and diverse repertoire of neural activity patterns) would be reflected by low-voltage, fast activity characteristic of waking and REM sleep, whereas integration would result from effective and rapid reentrant interactions in the thalamocortical system. This way, there would be a large functional complex cluster of neuronal groups that would constitute, on a temporal scale under the second, a unified neural process. They named this cluster the 'dynamic core', which would typically include posterior and anterior corticothalamic regions. Another proposal has suggested that conscious access involves the formation of a stable coalition of neurons (similar to the above mentioned 'dynamic core') and the key contribution of reverberating gamma oscillations, although in later versions they emphasized a role for connections with the prefrontal cortex [START_REF] Crick | Some reflections on visual awareness[END_REF]Crick and Koch 1995;[START_REF] Crick | A framework for consciousness[END_REF][START_REF] Crick | What is the function of the claustrum?[END_REF].

Other processes, such as decision-making, are also relevant for perceptual performance. Decision-making is a complex flexible process, e.g., the same sensory information can lead to different actions or different sensations can lead to the same action, depending on the individual's goals (Siegel et al. 2011). Research in monkeys has provided rich evidence on the involvement of sensory, parietal and frontal activity in the encoding of sensory evidence, its accumulation over time and the planning of motor action [START_REF] Glimcher | The neurobiology of visual-saccadic decision making[END_REF][START_REF] Gold | The neural basis of decision making[END_REF][START_REF] Kable | The neurobiology of decision: consensus and controversy[END_REF][START_REF] Romo | Flutter discrimination: neural codes, perception, memory and decision making[END_REF][START_REF] Schall | Neural basis of deciding, choosing and acting[END_REF]. Consistently with monkey studies, fMRI studies in humans in different sensory modalities have provided proof that sensory neurons encode the representation of sensory evidence used in decision-making [START_REF] Binder | Neural correlates of sensory and decision processes in auditory object identification[END_REF]Heekeren et al. 2004). As in monkeys, the accumulation of sensory evidence in regions like the dlPFC and the IPS also appear to occur in humans. Studies have shown that activity in the dlPFC may integrate the output from lower-level sensory regions and use the comparison between activity of selectively tuned neuronal populations to compute decisions (Heekeren et al. 2004;[START_REF] Krawczyk | Contributions of the prefrontal cortex to the neural basis of human decision making[END_REF]. Finally, studies in humans have also shown, similarly to the work in monkeys, that motor areas involved in motor response actions (e.g. the FEF in the case of an occulomotor response) are also implicated in decision-making processes (e.g. (Heinen et al. 2006) in the case of occulomotor reponses).

I.III. Neural dynamics of visuospatial attention and conscious access

The processes and subprocesses reviewed in the previous section involve highly co-localized cortical networks. For example, resources within regions such as the FEF or the IPS are involved in several operations. How do the same sets of brain regions subtend different functions and how are these coordinated? In the last years, the idea of dynamic functional circuits that arise to flexibly map brain functions, investigated by means of high temporal resolution techniques at different spatial scales (intractortical recordings, EEG and MEG), has gained weight, together with the notion that these networks and their interactions are regulated through cerebral oscillations [START_REF] Engel | Dynamic predictions: oscillations and synchrony in top-down processing[END_REF]Salinas and Sejnowski 2001;Varela et al. 2001).

Research using these correlational techniques has helped characterize the temporal dynamics of the perceptual relevant processes described in previous sections.

I.III.1. Neural dynamics of visuospatial attention

Local and long-range cerebral oscillations at different frequency bands, including alpha, beta and gamma, have been related to attentional processes. In particular, visuospatial attention has been shown in occipital and parieto-occipital cortices to decrease alpha activity contralaterally to attended locations and increase it contralaterally to unattended ones (Gould et al. 2011;Rihs et al. 2007;Sauseng et al. 2005;Thut et al. 2006;Worden et al. 2000). It has also been shown in visual areas that attention improves gamma oscillatory activity in humans (Doesburg et al. 2008;[START_REF] Tallon-Baudry | Attention modulates gamma-band oscillations differently in the human lateral occipital cortex and fusiform gyrus[END_REF] and monkeys [START_REF] Bichot | Parallel and serial neural mechanisms for visual search in macaque area V4[END_REF][START_REF] Fries | Modulation of oscillatory neuronal synchronization by selective visual attention[END_REF][START_REF] Womelsdorf | Gamma-band synchronization in visual cortex predicts speed of change detection[END_REF].

Long-range coherence between frontal, parietal and visual cortices and more specifically, increases of gamma band activity [START_REF] Khedr | Therapeutic trial of repetitive transcranial magnetic stimulation after acute ischemic stroke[END_REF][START_REF] Fregni | Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory[END_REF][START_REF] Silvanto | Making the blindsighted see[END_REF][START_REF] Ferrucci | Transcranial direct current stimulation improves recognition memory in Alzheimer disease[END_REF][START_REF] Valero-Cabre | Impact of repetitive transcranial magnetic stimulation of the parietal cortex on metabolic brain activity: a 14C-2DG tracing study in the cat[END_REF][START_REF] Wagner | Noninvasive human brain stimulation[END_REF][START_REF] Miniussi | Efficacy of repetitive transcranial magnetic stimulation/transcranial direct current stimulation in cognitive neurorehabilitation[END_REF][START_REF] Thompson | The detection of visual signals by macaque frontal eye field during masking[END_REF][START_REF] Armstrong | Selection and maintenance of spatial information by frontal eye field neurons[END_REF][START_REF] O'shea | Visual awareness: the eye fields have it?[END_REF][START_REF] Cul | Brain dynamics underlying the nonlinear threshold for access to consciousness[END_REF][START_REF] Ekstrom | Bottom-up dependent gating of frontal signals in early visual cortex[END_REF](47)(48)(49)(50)(51) on parieto-occipital areas by endogenous attention have been reported [START_REF] Gruber | Selective visual-spatial attention alters induced gamma band responses in the human EEG[END_REF]. Also, endogenous attention in a motion direction discrimination task selectively enhanced gamma-band synchronization between the posterior parietal cortex (PPC) and mediotemporal cortex (MT) and between the FEF and MT during the delay period for the hemisphere that processed the attended stimulus [START_REF] Siegel | Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention[END_REF]. This is in accordance with non-human primates data, reflecting enhanced oscillatory coupling in the gamma band between the FEF and V4 with endogenous attention (Gregoriou et al. 2009). In this study, the coupling appeared shifted by about 10 ms, being initiated by the FEF. Other studies in monkeys have also linked gamma-and beta-range oscillations to attention. Increases in the high-beta/low-gamma band coherence have been observed between posterior parietal cortex (LIP) and MT neuronal populations coding for the attended location [START_REF] Saalmann | Neural mechanisms of visual attention: how top-down feedback highlights relevant locations[END_REF].

Between frontal (FEF) and parietal (LIP) regions, activity has been shown to be enhanced at different frequency bands associated to different types of attentional orienting. Synchrony was enhanced in the beta-band [START_REF] Green | Signal detection theory and psychophysics[END_REF][START_REF] Macmillan | Detection theory: a user's guide[END_REF][START_REF] Paus | Location and function of the human frontal eye-field: a selective review[END_REF][START_REF] Thickbroom | Transcranial magnetic stimulation of the human frontal eye field[END_REF][START_REF] Chica | Spatial attention and conscious perception: the role of endogenous and exogenous orienting[END_REF][START_REF] Hilgetag | Enhanced visual spatial attention ipsilateral to rTMS-induced 'virtual lesions' of human parietal cortex[END_REF][START_REF] Waterston | Improved discrimination of visual stimuli following repetitive transcranial magnetic stimulation[END_REF][START_REF] Galea | Disruption of the dorsolateral prefrontal cortex facilitates the consolidation of procedural skills[END_REF][START_REF] Reis | Consensus: Can transcranial direct current stimulation and transcranial magnetic stimulation enhance motor learning and memory formation?[END_REF][START_REF] Gallate | Reducing false memories by magnetic pulse stimulation[END_REF][START_REF] Bolognini | Brain polarization of parietal cortex augments training-induced improvement of visual exploratory and attentional skills[END_REF][START_REF] Tadin | Improved motion perception and impaired spatial suppression following disruption of cortical area MT/V5[END_REF][START_REF] Oliveri | rTMS of the unaffected hemisphere transiently reduces contralesional visuospatial hemineglect[END_REF] in a visual search task requiring endogenous attention, whereas in a pop-out task in which a salient stimulus captured attention exogenously, activity was enhanced at higher oscillation frequencies (Buschman and Miller 2007). Moreover, recently, using diffusion tensor imaging to track pulvino-cortical networks and invasive electrophysiological recordings in monkeys performing a visuo-spatial attentional task, it has been suggested that synchrony between cortical areas according to attentional demands is regulated through the pulvinar [START_REF] Saalmann | The pulvinar regulates information transmission between cortical areas based on attention demands[END_REF].

I.III.2. Neural dynamics of conscious access

Non-invasive neuroimaging methods have shown that conscious access in humans involves local and long-range synchronization in different frequency bands. Indeed, in humans late (>300 ms) local and long-distance synchronization increases in the gamma band (>30 Hz) across occipital, parietal and prefrontal cortices have been observed [START_REF] Doesburg | Rhythms of consciousness: binocular rivalry reveals large-scale oscillatory network dynamics mediating visual perception[END_REF][START_REF] Gaillard | Converging intracranial markers of conscious access[END_REF][START_REF] Melloni | Synchronization of neural activity across cortical areas correlates with conscious perception[END_REF][START_REF] Rodriguez | Perception's shadow: long-distance synchronization of human brain activity[END_REF][START_REF] Schurger | Induced gamma-band oscillations correlate with awareness in hemianopic patient GY[END_REF]Wyart and Tallon-Baudry 2009). Intracortical recording evidence in monkeys is consistent with these findings (e.g. [START_REF] Panagiotaropoulos | Neuronal discharges and gamma oscillations explicitly reflect visual consciousness in the lateral prefrontal cortex[END_REF] for gamma oscillations increases in the prefrontal cortex). In the beta band, decrease of local power but increase of long-range phase synchronization has been reported [START_REF] Libedinsky | Role of prefrontal cortex in conscious visual perception[END_REF][START_REF] Lumer | Neural correlates of perceptual rivalry in the human brain[END_REF][START_REF] Moore | Selective gating of visual signals by microstimulation of frontal cortex[END_REF][START_REF] Moore | Control of eye movements and spatial attention[END_REF][START_REF] Capotosto | Frontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms[END_REF][START_REF] Ruff | Concurrent TMS-fMRI and psychophysics reveal frontal influences on human retinotopic visual cortex[END_REF][START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field: effects on visual perception and attention[END_REF][START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness[END_REF][START_REF] Silvanto | State-dependency in brain stimulation studies of perception and cognition[END_REF][START_REF] Silvanto | Neural adaptation reveals state-dependent effects of transcranial magnetic stimulation[END_REF][START_REF] Green | Signal detection theory and psychophysics[END_REF][START_REF] Macmillan | Detection theory: a user's guide[END_REF][START_REF] Paus | Location and function of the human frontal eye-field: a selective review[END_REF][START_REF] Thickbroom | Transcranial magnetic stimulation of the human frontal eye field[END_REF][START_REF] Chica | Spatial attention and conscious perception: the role of endogenous and exogenous orienting[END_REF][START_REF] Hilgetag | Enhanced visual spatial attention ipsilateral to rTMS-induced 'virtual lesions' of human parietal cortex[END_REF][START_REF] Waterston | Improved discrimination of visual stimuli following repetitive transcranial magnetic stimulation[END_REF][START_REF] Galea | Disruption of the dorsolateral prefrontal cortex facilitates the consolidation of procedural skills[END_REF][START_REF] Reis | Consensus: Can transcranial direct current stimulation and transcranial magnetic stimulation enhance motor learning and memory formation?[END_REF] [START_REF] Gaillard | Converging intracranial markers of conscious access[END_REF]Hipp et al. 2011).

Although some early increases of gamma power appearing 150-200 ms with regards to target onset have also been reported to correlate with conscious access (Fisch et al. 2009), the time window after 200 ms (300-500 ms) seems to be a more specific marker of conscious access.

Several studies have shown that gamma-band activity in the visual cortex depends on stimulus strength and features (Berens et al. 2008;[START_REF] Frien | Fast oscillations display sharper orientation tuning than slower components of the same recordings in striate cortex of the awake monkey[END_REF][START_REF] Henrie | LFP power spectra in V1 cortex: the graded effect of stimulus contrast[END_REF][START_REF] Hoogenboom | Localizing human visual gamma-band activity in frequency, time and space[END_REF][START_REF] Kayser | Stimulus locking and feature selectivity prevail in complementary frequency ranges of V1 local field potentials[END_REF]Liu and Newsome 2006;Siegel et al. 2007;[START_REF] Siegel | A functional gamma-band defined by stimulus-dependent synchronization in area 18 of awake behaving cats[END_REF] and that pre-stimulus gamma activity in lateral-occipital regions biases perceptual decisions (Wyart and Tallon-Baudry 2009). Taken together, these studies indicate that gamma frequencies in specialized regions of the sensory cortex reflect the encoding of sensory evidence during perceptual decisions (Siegel et al. 2011).

I.IV. Noninvasive brain stimulation methods and approaches

Non-invasive electrophysiological and neuroimaging methods such as EEG, MEG and fMRI have contributed important data to the neural bases of human cognition. However, these techniques lack the ability to demonstrate a causal involvement of specific brain regions and networks in different cognitive processes and behavioral outputs. Causal relationships between the contribution of specific brain areas and cognition have been traditionally established in patient lesion studies.

However, factors such as the lack of focality of the damage, the high degree of interindividual variability and the interfering role of neural reorganization and behavioral compensation (which are difficult to properly control for) limit the conclusions driven by these data.

In this context, noninvasive brain stimulation technologies have emerged in the last decades as novel methods to explore causal contributions of specific brain regions and their associated networks to behavior in healthy human participants and neuropsychiatric patients. Importantly, they also allow investigators to establish causal relationships between behavioral outputs and specific brain activity patterns and states. Last but not least, these same tools can be employed to characterize the functional connectivity of brain regions, probe its sensitivity and responsiveness to perturbations and the possibility to induce lasting changes in local and network activity, which has proven therapeutic in some conditions. Several techniques (which can be eventually combined with brain anatomical and functional methods like EEG, MEG, fMRI or DTI) have been developed in the last decades. The most established and widely used ones are transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS) and transcranial alternate current stimulation (tACS).

Moreover, TMS and tDCS are currently used already not only for research but also for clinical purposes to treat or potentially treat different neuropsychiatric conditions (e.g. [START_REF] Brunoni | Clinical research with transcranial direct current stimulation (tDCS): challenges and future directions[END_REF][START_REF] Loo | A review of the efficacy of transcranial magnetic stimulation (TMS) treatment for depression, and current and future strategies to optimize efficacy[END_REF]). More recently, the use of other techniques such as transcranial ultrasound stimulation (TUS), transcranial pulsed current stimulation (tPCS) and transcranial random noise stimulation (tRNS) has considerably increased (Dayan et al. 2013). Although all of these techniques have the potential to provide causal evidence on the involvement of cortical regions in behavior, they differ considerably in focality, temporal resolution, portability, safety and easiness of use. For example, techniques such as tDCS and tACS can be easily used and transported and have proved safe to date, but they have a relatively poor spatial resolution and cannot be used to precisely characterize neural processes in time. On the other hand, TMS, although more difficult to transport and rarely reported to induce epileptic crises, offers a relatively good focality and an excellent temporal resolution and can be used safely when doing it according to established guidelines.

I.IV.1. Transcranial magnetic stimulation (TMS)

Developed in the mid-eighties by Anthony Barker and collaborators, transcranial magnetic stimulation (TMS) is still today the most focal noninvasive brain stimulation technique, largely accepted and used both in fundamental research and clinical applications. Its operating principle is based on Faraday's law of electromagnetic induction. A stimulating coil is placed on the participant's scalp. When a short intense current is discharged through the coil, it generates a brief (0.1-1 ms) and intense (1-4 T) magnetic field perpendicular to the coil surface (Wagner et al. 2007). This pulsed field reaches the brain tissue placed under the coil and induces in it electric currents parallel to the coil surface. The spatial resolution of the technique for a standard 70 mm figure-of-eight coil is about 12 to 14 mm (Valero-Cabre et al. 2005) and the magnitude of the induced current depends on several factors including tissue conductivity, distance between the coil and the targeted brain region and coil design (Valero-Cabre et al. 2005;Wagner et al. 2007).

Different TMS modalities can generate immediate or long-lasting modulations of cortical activity and assess different aspects of brain function. Single TMS pulses, doublets or short bursts yield time-specific immediate "online" effects and are typically employed to probe the causal involvement of a given cortical area in a specific behavioral task and to assess the time window in which the contribution of that area is crucial. The delivery of a single pulse on a trial-by-trial basis at specific time windows over a particular brain area (single-pulse TMS) has been used to causally explore with high temporal resolution the contribution of specific brain regions to human behavior. The highly precise timing at which the pulse can be delivered with regards to specific task events during a trial makes this tool particularly suited to perform the so called chronometric studies. With the aim to disrupt cortical activity during longer but still specific periods of time, doublets or triplets of TMS pulses have been used in online or trial-by-trial TMS designs in many studies (e.g. [START_REF] Kalla | The timing of the involvement of the frontal eye fields and posterior parietal cortex in visual search[END_REF][START_REF] Koch | Modulation of excitatory and inhibitory circuits for visual awareness in the human right parietal cortex[END_REF]O'Shea et al. 2004;[START_REF] Silvanto | Double dissociation of V1 and V5/MT activity in visual awareness[END_REF]).

In contrast, long trains of TMS pulses, known as repetitive TMS (rTMS), yield long-lasting "offline" effects that outlast the duration of stimulation and reflect changes in cortical excitability. Repetitive TMS is typically used to probe the contribution of different areas to a certain brain process measured through a specific behavioral task without an emphasis on temporal resolution or the potential of the technique to yield long-lasting effects that could be relevant for therapy. The most commonly used patterns are 1 Hz as low frequency and 10 and 20 Hz as higher frequency. Newer patterns of rTMS (e.g. theta burst stimulation, TBS) have combined different frequencies (e.g. 3 pulses at 50 Hz embedded in 5 Hz for TBS) [START_REF] Huang | Theta burst stimulation of the human motor cortex[END_REF]. Finally, multiple sessions of long trains of stimulation have been shown to yield even longer lasting effects that can be useful for therapeutic purposes.

TMS is known to induce local but also connectivity mediated network effects when used both in the online (Ruff et al. 2006;[START_REF] Ruff | Hemispheric differences in frontal and parietal influences on human occipital cortex: direct confirmation with concurrent TMS-fMRI[END_REF]) and offline [START_REF] Valero-Cabre | Opposite impact on 14C-2deoxyglucose brain metabolism following patterns of high and low frequency repetitive transcranial magnetic stimulation in the posterior parietal cortex[END_REF]Valero-Cabre et al. 2005;Wagner et al. 2007) modalities and such network effects appear to depend on the strength of the interactions between areas (Valero-Cabre et al. 2005). Also, its impact has been proven dependent on the ongoing activity levels of the neurons within the stimulated region [START_REF] Cattaneo | Using state-dependency of transcranial magnetic stimulation (TMS) to investigate letter selectivity in the left posterior parietal cortex: a comparison of TMS-priming and TMSadaptation paradigms[END_REF][START_REF] Perini | Occipital transcranial magnetic stimulation has an activity-dependent suppressive effect[END_REF]Silvanto and Muggleton 2008b;Silvanto et al. 2007).

Accordingly, paradigms such as neural adaptation (Silvanto et al. 2007) or visuospatial cues (Armstrong et al. 2009), which shape the amount of activation of different subpopulations within a given brain area, could be used to shape the effects of TMS (online and offline) and increase its selectivity both in basic research and clinical applications, as well as to further investigate the mechanisms of action of this technique (Silvanto et al. 2008;Silvanto and Muggleton 2008a;Silvanto and Pascual-Leone 2008).

I.IV.2. TMS and the visual system

In the last years, TMS has widely contributed to the causal exploration of the visual system. Single-pulse TMS over the occipital pole has been shown to yield perceptual modulations. When delivered after the presentation of a visual target, a TMS pulse typically disrupts visual perception [START_REF] Amassian | Focal stimulation of human cerebral cortex with the magnetic coil: a comparison with electrical stimulation[END_REF][START_REF] Kastner | Transient visual field defects induced by transcranial magnetic stimulation over human occipital pole[END_REF][START_REF] Maccabee | Magnetic coil stimulation of human visual cortex: studies of perception[END_REF][START_REF] Miller | Transcranial magnetic stimulation: delays in visual suppression due to luminance changes[END_REF] although perceptual enhancements have also been reported [START_REF] Abrahamyan | Improving visual sensitivity with subthreshold transcranial magnetic stimulation[END_REF]. In contrast, when a single TMS pulse is delivered prior to target onset over occipital regions perceptual enhancements have been described [START_REF] Mulckhuyse | Enhanced visual perception with occipital transcranial magnetic stimulation[END_REF]). Perception has also been reported to be enhanced by 1-Hz rTMS over V1 (Waterston and Pack 2010). Moreover, the delivery of a single TMS to higher order visual areas can also modulate visual perception (e.g. for V5, [START_REF] Amassian | Transcranial magnetic stimulation in study of the visual pathway[END_REF][START_REF] Beckers | Cerebral visual motion blindness: transitory akinetopsia induced by transcranial magnetic stimulation of human area V5[END_REF][START_REF] Beckers | The consequences of inactivating areas V1 and V5 on visual motion perception[END_REF][START_REF] Hotson | Transcranial magnetic stimulation of extrastriate cortex degrades human motion direction discrimination[END_REF]). TMS has also been used to provide causal evidence for cortico-cortical loops relevant for visual awareness, showing for example that if activity in V1 is modulated using TMS right after V5 activation, visual awareness of motion is disrupted (Pascual-Leone and Walsh 2001). Several studies have used TMS to study the dorsal visual streams in healthy participants. For example, TMS over V5 alters motion perception [START_REF] Theoret | Repetitive transcranial magnetic stimulation of human area MT/V5 disrupts perception and storage of the motion aftereffect[END_REF]. The use of TMS for the study of the ventral stream has been importantly limited by the inaccessibility of the regions involved (e.g. the fusiform gyrus), and the presence of skeletal muscles, which make the stimulation uncomfortable obliging investigators to Finally, perceptual modulations can be also yielded by TMS over areas that are not necessarily part of the visual system but involved in processes that are known to be relevant for perceptual performance, such as attention and conscious access described in prior sections. Two particularly relevant regions for these processes, the FEF and the IPS, and the studies modulating perception from these cortical sites are described in section I.V.

I.IV.3. TMS and brain oscillations

In the last years, brain oscillations have been proven to be extremely important for cognition. As discussed above, neuronal synchronization at different frequency bands could be a general mechanism underlying brain function [START_REF] Fries | A mechanism for cognitive dynamics: neuronal communication through neuronal coherence[END_REF]). Brain oscillations have been typically studied using methods such as EEG and MEG, able to record brain activity with high temporal resolution. These studies have provided very valuable information on the dynamics of brain activity involved in different cognitive processes. However, causal relationships can be suggested but not established with these techniques.

Single-pulse TMS has been shown to modulate ongoing oscillations and it is likely that it does it through phase resetting [START_REF] Moliadze | Effect of transcranial magnetic stimulation on single-unit activity in the cat primary visual cortex[END_REF]. Repetitive TMS has also shown an ability to locally modulate oscillatory activity [START_REF] Brignani | Modulation of cortical oscillatory activity during transcranial magnetic stimulation[END_REF][START_REF] Fuggetta | Acute modulation of cortical oscillatory activities during short trains of high-frequency repetitive transcranial magnetic stimulation of the human motor cortex: a combined EEG and TMS study[END_REF]. Furthermore, the simultaneous stimulation of two cortical sources at a specific TMS frequency can result in lasting enhancements of longrange coupling [START_REF] Plewnia | Enhancement of long-range EEG coherence by synchronous bifocal transcranial magnetic stimulation[END_REF]) as indicated by EEG recordings performed after the stimulation. Although technically challenging, the concurrent use of TMS and EEG (TMS-EEG) has recently provided extremely valuable information on the impact of TMS on brain activity and its potential to manipulate oscillation patterns. The most important limitation of this technique is that each TMS pulse produces an artifact, which can last from hundreds of milliseconds [START_REF] Thut | Effects of single-pulse transcranial magnetic stimulation (TMS) on functional brain activity: a combined event-related TMS and evoked potential study[END_REF] to 5 ms [START_REF] Veniero | TMS-EEG co-registration: on TMSinduced artifact[END_REF], during which no usable EEG is recorded. Indeed, TMS-EEG requires the implementation of a system preventing the signal to go into the amplifier when the pulse is delivered to avoid its saturation and the consequent long artifact until the signal recovers. Moreover, a special EEG cap with flat electrodes is also required so that the coil does not stand too far from the scalp and the brain weakening the cortical impact of the stimulation. The combination of TMS and online EEG has provided insights on the relationship between brain oscillations and cortical excitability [START_REF] Romei | Spontaneous fluctuations in posterior alpha-band EEG activity reflect variability in excitability of human visual areas[END_REF][START_REF] Sauseng | Brain oscillatory substrates of visual short-term memory capacity[END_REF][START_REF] Zarkowski | EEG and the variance of motor evoked potential amplitude[END_REF] and shown that single TMS pulses modulate EEG oscillatory activity [START_REF] Komssi | Ipsi-and contralateral EEG reactions to transcranial magnetic stimulation[END_REF].

Moreover, recently, short bursts of TMS have been shown to entrain cerebral oscillations. In particular, it has been reported that TMS bursts at the parietal alpha oscillator preferred frequency entrains local alpha oscillations in a narrow band around it [START_REF] Dehaene | Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework[END_REF][START_REF] Latto | Visual field defects after frontal eye-field lesions in monkeys[END_REF][START_REF] Libedinsky | Role of prefrontal cortex in conscious visual perception[END_REF], reaching significance after the third of the five pulses of the burst (Thut et al. 2011). Whether the fact that the stimulation is delivered at the preferred frequency of the region plays a key role in the TMS-induced entrainment of oscillations or not remains unclear.

Bursts at different frequencies have been used to noninvasively explore oscillatory phenomena in the human brain and provide causal evidence of their involvement in different cognitive tasks with an acceptable chronometry. For example, occipito-parietal TMS at alpha but not theta or beta control frequencies have been shown to modulate target visibility in a visual detection task (Romei et al. 2010). Similarly, theta and beta TMS over the parietal cortex have been shown to favor processing of global versus local features, respectively (Romei et al. 2011). In spite of their undeniable interest, the comparison of effects across oscillation frequencies in these pioneering studies has been limited by the different duration of the trains. Indeed, trains at higher frequencies are significantly shorter than those with the same number of pulses at lower frequencies (e.g. 5 pulses at 20 Hz, interstimulus interval of 50 ms, total duration of 200 ms vs. 5 pulses at 10 Hz, interstimulus interval of 100 ms, total duration of 400 ms) and thus one cannot rule out the possibility that different effects result from the different amount of activity induced within a critical time rather than being specific to the frequency used. To the best of our knowledge there is to date a single study that has causally isolated the effect of oscillation frequency on behavior. The investigators of this study, performed in honeybees, pharmacologically induced desynchronization in sensory evidence encoding neurons while keeping unaffected their average firing rate, isolating this way the specific effect of frequency from the level of activation of those neurons.

They observed impaired odor discrimination [START_REF] Stopfer | Impaired odour discrimination on desynchronization of odour-encoding neural assemblies[END_REF], providing evidence on the role of frequency on behavior.

The use of short TMS bursts to probe the contributions of specific frequencies to behavior in relatively short time windows provides an interesting starting point to causally explore in humans the oscillatory basis of cognition. Furthermore, it opens the door to critically review under new light the results of past online TMS studies in which more than one TMS pulses (typically 2 or 3) were used with the purpose of disrupting activity during longer yet relatively short time windows. Since the pulses in such studies were equally distributed in time, activity was being induced rhythmically, thus if the frequency of the induced activity is proved relevant for behavior the effects could be reinterpreted in terms of oscillations.

Moreover, if local and interregional oscillatory activity proves to be a key element for brain function, pathologies could result from the impairment of these processes. Indeed, altered oscillations have been already associated with several neuropsychiatric diseases including Parkinson's (e.g. [START_REF] Brown | Oscillatory nature of human basal ganglia activity: relationship to the pathophysiology of Parkinson's disease[END_REF]), depression (e.g. (Linkenkaer-Hansen et al. 2005)) and schizophrenia (e.g. (Uhlhaas and Singer 2010)). New insights of the specific alterations involved have been revealed by means of the TMS-EEG approaches indicated above (e.g. for schizophrenia see (Farzan et al. 2010;Farzan et al. 2009;Ferrarelli et al. 2008)). This breadth of knowledge will pave the way for a future use of noninvasive neurostimulation to transiently restore normal oscillatory patterns in patients and, if this proved efficient, use such stimulation techniques in longer lasting modality regimes to improve their clinical condition (e.g. for schizophrenia see [START_REF] Barr | The effect of repetitive transcranial magnetic stimulation on gamma oscillatory activity in schizophrenia[END_REF].

I.V. The frontal eye fields, the intraparietal sulcus and their manipulation through noninvasive brain stimulation

Two key regions stand out as involved in the processes relevant for perceptual performance described above, the frontal eye field (FEF) and the intraparietal sulcus (IPS), which have been at the center of our investigations.

The FEF is a cortical area located in the prefrontal cortex of both hemispheres, between the pre-central sulcus and the superior frontal gyrus, and it has been defined according to its role in saccadic activity. Nonetheless, there is controversy with regards to its exact cortical location, which shows some level of interindividual variability and dependence on the mapping technique used (low-current intracortical stimulation studies, fMRI) and the task (sensory guided saccades, antisaccades, memory guided saccades, paradigms with or without a gap between fixation and target onset, lateralized spatial cues as signal for saccades) employed to define it [START_REF] Amiez | Preoperative functional magnetic resonance imaging assessment of higher-order cognitive function in patients undergoing surgery for brain tumors[END_REF][START_REF] Blanke | Location of the human frontal eye field as defined by electrical cortical stimulation: anatomical, functional and electrophysiological characteristics[END_REF][START_REF] Petit | Functional asymmetries revealed in visually guided saccades: an FMRI study[END_REF][START_REF] Rasmussen | Movement of head and eyes from stimulation of human frontal cortex[END_REF]. Some investigators have attempted a causal functional localization of this region as the cortical site in which TMS yields delays in saccade onset, and have reported the FEF to be located 6 cm lateral to the vertex and 0.2 cm posterior to the inter-aural line (Thickbroom et al. 1996) and 1.5 cm anterior to the motor hand area [START_REF] Olk | Modulation of antisaccades by transcranial magnetic stimulation of the human frontal eye field[END_REF][START_REF] Ro | Locating the human frontal eye fields with transcranial magnetic stimulation[END_REF]. A review of studies using cerebral blood-flow measures obtained with positron emission tomography (PET), localized this region in the vicinity of the precentral sulcus and/or the caudalmost region of the superior frontal sulcus (Paus 1996). More precisely, this study provided averaged Talairach coordinates for the right and the left FEF, which were x=31, y=-2, z=47 and x=-32, y=-2, z=46, respectively. These locations have been successfully employed in TMS studies demonstrating saccadic, attentional and awareness effects (Grosbras and Paus 2002;Grosbras and Paus 2003;[START_REF] Smith | Exogenous orienting of attention depends upon the ability to execute eye movements[END_REF]).

The frontal eye field is part of a rich brain network of cortical and subcortical sites, sustaining reciprocal connections with other eye field regions (supplementary eye field, parietal eye field), with the intraparietal sulcus (IPS) and the superior temporal sulcus (STS) [START_REF] Huerta | Frontal eye field as defined by intracortical microstimulation in squirrel monkeys, owl monkeys, and macaque monkeys. II. Cortical connections[END_REF][START_REF] Stanton | Topography of projections to the frontal lobe from the macaque frontal eye fields[END_REF][START_REF] Stanton | Topography of projections to posterior cortical areas from the macaque frontal eye fields[END_REF]Tian and Lynch 1996a;Tian and Lynch 1996b). It also receives afferent connections from the middle superior temporal area (MST) (Tian and Lynch 1996a;Tian and Lynch 1996b) and efferent connections to V2, V3, V4, the occipitotemporal cortex [START_REF] Stanton | Topography of projections to posterior cortical areas from the macaque frontal eye fields[END_REF], the midbrain (in particular the superior colliculus) and the pons [START_REF] Leichnetz | The prefrontal cortico-oculomotor trajectories in the monkey[END_REF]. The FEF plays a crucial role in the planning and control of eye movements [START_REF] Rivaud | Eye movement disorders after frontal eye field lesions in humans[END_REF][START_REF] Schiller | Deficits in eye movements following frontal eye-field and superior colliculus ablations[END_REF]. It is also known to contribute significantly in monkeys and humans to the orienting of visuospatial attention, even when eye movements are not involved (i.e. covert attention) (Corbetta et al. 2002;Corbetta et al. 2008) and some recent studies have emphasized a role in the interaction between attentional orienting and conscious access [START_REF] Chica | Neural bases of the interactions between spatial attention and conscious perception[END_REF].

Indeed, electrophysiological recordings in monkeys have shown modulations of neuronal activity in the FEF by attention independently on eye movements [START_REF] Kodaka | Neuronal activity in the frontal eye field of the monkey is modulated while attention is focused on to a stimulus in the peripheral visual field, irrespective of eye movement[END_REF]) and inactivation of the FEF affects covert attention [START_REF] Wardak | Contribution of the monkey frontal eye field to covert visual attention[END_REF]).

In humans, TMS studies have provided causal evidence of visuo-spatial attention disruption by modulations of FEF activity during cueing (Grosbras and Paus 2002;[START_REF] Smith | Exogenous orienting of attention depends upon the ability to execute eye movements[END_REF]) and visual search [START_REF] Muggleton | Human frontal eye fields and visual search[END_REF]. Moreover, the FEF has also been shown to modulate activity in visual areas and visual perception. In monkeys, the threshold for detection of a luminance change in an attended target is decreased by microstimulation of FEF neurons coding for the attended location (Moore and Fallah 2001) and this type of interventions enhances responses in striate (V1-V4) and extrastriate (MST, MT) visual and posterior parietal (LIP) areas [START_REF] Armstrong | Rapid enhancement of visual cortical response discriminability by microstimulation of the frontal eye field[END_REF][START_REF] Ekstrom | Bottom-up dependent gating of frontal signals in early visual cortex[END_REF]Ekstrom et al. 2009;Moore and Armstrong 2003). Similarly, using TMS in humans, studies have reported decreases of reaction time (Grosbras and Paus 2002) and awareness enhancements (Grosbras and Paus 2003) in visual detection tasks by pre-target FEF TMS and disruption of discrimination in a visual conjunction search (O'Shea et al. 2004). Used concurrently with fMRI or EEG, FEF TMS has been shown to modulate activity in parietal and visual areas (Capotosto et al. 2009;Ruff et al. 2006) and using occipital TMS evoked phosphenes as a measure of cortical excitability, changes have been reported when FEF was ipsilaterally stimulated 20-30 ms before the visual region (Silvanto et al. 2006). Finally, single TMS pulse in this region has been shown to improve high-beta oscillations around 30 Hz, a likely natural oscillatory activity taking place in this region [START_REF] Rosanova | Natural frequencies of human corticothalamic circuits[END_REF]).

The intraparietal sulcus (IPS) is located in the lateral surface of the posterior parietal cortex (PPC), demarcating the superior and the inferior parietal lobule.

Recent TMS studies have used the averaged Talairach coordinates (x=16, y=-63, z=47) provided by a fMRI paper (Kincade et al. 2005) to stimulate the IPS [START_REF] Bourgeois | Cortical control of inhibition of return: causal evidence for task-dependent modulations by dorsal and ventral parietal regions[END_REF]Chica et al. 2011).

Its likely homologue in monkeys, the lateral intraparietal area (LIP), has been shown to withhold reciprocal connections with several extrastriate visual regions including V3, V4, MT and MST, as well as with the FEF, the SC and the pulvinar [START_REF] Andersen | Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule[END_REF][START_REF] Grieve | The primate pulvinar nuclei: vision and action[END_REF][START_REF] Schall | Topography of visual cortex connections with frontal eye field in macaque: convergence and segregation of processing streams[END_REF]. It is involved in spatial representation and visuo-motor processes [START_REF] Culham | Neuroimaging of cognitive functions in human parietal cortex[END_REF][START_REF] Grefkes | The functional organization of the intraparietal sulcus in humans and monkeys[END_REF]. Like the FEF, the IPS (or the LIP) plays an important role in visuospatial attention (Corbetta et al. 2002;Corbetta et al. 2008). Moreover, microstimulation of this region has been recently shown to bias saccade direction [START_REF] Mirpour | Microstimulation of posterior parietal cortex biases the selection of eye movement goals during search[END_REF]. Similarly to the FEF, the IPS has been shown to modulate activity and cortical excitability of visual areas [START_REF] Ruff | Hemispheric differences in frontal and parietal influences on human occipital cortex: direct confirmation with concurrent TMS-fMRI[END_REF][START_REF] Saalmann | Neural mechanisms of visual attention: how top-down feedback highlights relevant locations[END_REF] and its disruption using TMS alters the occipital phosphene threshold [START_REF] Silvanto | The perceptual and functional consequences of parietal top-down modulation on the visual cortex[END_REF]. IPS TMS has also been shown to yield modulations of visual search [START_REF] Chambers | Fast and slow parietal pathways mediate spatial attention[END_REF], perception [START_REF] Bjoertomt | Spatial neglect in near and far space investigated by repetitive transcranial magnetic stimulation[END_REF]Kanai et al. 2008) and mental rotation (Feredoes and Sachdev 2006).

Finally, some degree of lateralization has been described for cortical networks and processes involving both the FEF and the IPS. For example, although the left and right FEF and the left and right IPS are part of a bilaterally distributed attentional network (Shulman et al. 2010;Thiebaut de Schotten et al. 2011), there is strong evidence of righ-hemisphere dominance for visuo-spatial processes (Bartolomeo et al. 2008;Grosbras and Paus 2002). Similarly, interhemispheric differences have been reported with regards to prefrontal contributions to conscious access (Del Cul et al. 2009;Rastelli et al.) and perceptual decision-making [START_REF] Heekeren | Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality[END_REF], with a relevant role of the left hemisphere in such processes.

I.VI. Aims of the present dissertation

Together, the research briefly reviewed in this introduction provides evidence that frontal and parietal areas influence neural activity in the visual cortex and perceptual performance through the different processes in which they are involved.

Although the FEF has been shown to be relevant for conscious access of masked stimuli, its causal implication in conscious access of low-contrast unmasked stimuli remains unexplored and conscious access is often associated with the parietal cortex. Moreover, the literature reviewed shows that the temporal dynamics of brain activity is a key element for brain function and that the development of novel procedures and methods to manipulate it can be very useful for exploratory or therapeutic purposes. In particular, available correlational data obtained with fMRI, MEG and EEG needs to be supplemented with causal evidence about the impact of cerebral oscillations in human behavior. In order to contribute to fill this gap, the experiments presented in this dissertation manipulated frontal and parietal activity and provided causal evidence of their relevance for perceptual performance. In particular, we studied how spatiotemporal activity patterns emerging from the FEF and the IPS (known to be involved in several processes such as visuospatial attention and conscious access) may ultimately modulate performance. We focused on visual perception because the visual system constitutes an excellent testing bench thanks to the broad existing wealth of knowledge on its anatomy and function and the possibility to put it to test by means of well-established behavioral paradigms and measures. We used TMS to induce transient selective non-invasive modulations of neural activity in the human FEF and IPS to provide causal evidence of their role in visual performance, particularly in visual discrimination and conscious visual detection. Importantly, we put special emphasis on the temporal dynamics of neural activity in such regions, by testing the impact of a series of trial-by-trial single pulses or short TMS patterns at specific frequencies and comparing their effects to those yielded by sham TMS, non-frequency-specific active patterns and 'random' noise stimulation.

Our overall hypothesis is that fronto-parietal areas contribute to perception through specific spatiotemporal activity patterns, likely to reflect different cognitive processes, and yield selective behavioral contributions to visual performance.

AIM 1: Chapter II: We aimed to explore the causal contribution of frontal pretarget activity to visual discrimination and conscious detection

The modulation of neural FEF activity using single-pulse TMS has been shown to decrease reaction times in response to suprathreshold visual stimuli and to facilitate the detection of masked stimuli. However, causal evidence for the contribution of such area to visual discrimination and conscious detection of lowcontrast near-threshold targets remained unclear. Moreover, neuroimaging studies have provided evidence that the orientation of spatial attention using visuo-spatial cues is likely to engage activity within a fronto-parietal network involving the FEF and the IPS. In monkeys, invasive electrophysiological recordings showed that neuronal subpopulations of the FEF coding for attended locations increased their activity and kept them increased during the cue to target period with regards to those units coding for other locations.

In a first experiment, single TMS pulses were delivered to the right FEF at different timings prior to the onset of a low-contrast near-threshold target which participants had to subsequently discriminate (line orientation discrimination) and consciously detect. We aimed to causally explore the chronometric contributions of this activity to visual performance. We hypothesized that pre-target FEF activity would be relevant for conscious perception and thus its modulation by means of single TMS pulses would yield perceptual performance modulations. In a second experiment, single TMS pulses were delivered after a peripheral predictive visuospatial cue engaging attention, likely to modulate FEF activity. An interaction of TMS effects with visuo-spatial attentional orienting, i.e. distinct effect in valid (cue signals target location) and invalid trials (cue does not signal target location) would constitute causal evidence of cue-induced differences in activation of FEF neuronal subpopulations coding for attended vs unattended locations.

AIM 2: Chapters III, IV and VI: We aimed to provide causal evidence of the role played by specific spatiotemporal activity patterns emerging from frontal and parietal areas to visual conscious detection and discrimination

In a scientific context characterized by growing interest in the oscillatory and synchrony basis of cognition, the specific contribution of oscillatory activity to different aspects of brain function is a topic of major significance and, often, the role for oscillatory phenomena in visual performance and consciousness remains to be explored causally.

In the experiments presented in these three chapters, we delivered 4-pulse TMS bursts at well-controlled interpulse intervals over two cortical regions, the FEF or the IPS, and measured their impact on the discrimination and conscious detection of low-contrast near-threshold targets. The use of frequency-specific patterns vs. non-frequency-specific ones with equivalent amount of activity induced, i.e. same number of pulses in the same time window at the same stimulation intensity, but slightly differently distributed in time, allowed us to isolate the effects of frequency from those resulting from the activation itself. We hypothesized that the dynamics of frontoparietal activity is a key factor in its ability to contribute to visual performance.

In a first study (Chapter III), we explored the frequency-specific oscillatory basis of visual performance. We delivered real or sham frequency-specific TMS bursts at high-beta (30 Hz) and gamma (50 Hz) frequencies, as well as control nonfrequency-specific patterns matched in duration and number of pulses, to the right FEF. We hypothesized that these two frequencies, known to reflect different types of attentional processes, would yield selective effects on visual performance that would not be observed by active non-frequency-specific matched patterns.

In a second study (Chapter IV), we explored potential hemisphere-specific basis of frontal perceptual performance modulations. Real or sham frequencyspecific or non-frequency-specific TMS bursts were delivered to the left FEF. We hypothesized that the different involvement of left and right homotopic frontal regions in perceptually relevant processes such as attention and conscious access could yield differences either in the behavioral effects that they yield or in the patterns proving able to do so.

Finally, in a third study (Chapter VI), we assessed the role of high-beta and gamma parietal activity in perceptual performance modulations. Following the same design of the first study we assessed the effects of 30 and 50 Hz in the right IPS on visual performance. We hypothesized that, since the parietal cortex is more directly and widely connected to visual areas, the effects could be yielded by wider frequency bands or additional effects could be observed. AIM 3: Chapter V: We aimed to causally explore the influence of the phase with regards to target onset of frontal high-beta oscillations on their ability to

modulate visual discrimination and conscious detection

Frequency is not the only temporal feature that has been proved to be important for the impact of neural activity on brain function but the phase has also been shown to be extremely relevant. For example, the phase of ongoing oscillations has been shown to predict visual detection and it has been causally linked to cortical excitability. Indeed, the oscillation phase would be defining periods of low and high excitability and thus the time windows in which a perturbation would be more likely to pass a threshold and trigger other processes. Furthermore, single TMS pulses have been shown to induce phase resetting. We used short frequency-specific TMS bursts at 30 Hz on the right FEF to test the behavioral impact of high-beta activity at different oscillation phases, defined as the fraction of cycle between the last TMS pulse of the burst and the visual target onset, on visual performance. We hypothesized that the phase of frontal oscillations could be a relevant factor in the ability of such oscillations to influence performance and thus some phases could be more effective than others in doing so.

Introduction

Since the pioneering studies by Posner and collaborators [START_REF] Posner | Attention and the detection of signals[END_REF], the ability of visuo-spatial attentional orienting to influence visual performance has been widely demonstrated. More recent work has specifically reported enhancements in several aspects of visual perception such as spatial resolution, contrast sensitivity and orientation discrimination in those regions of the visual field where attention is willfully focused or involuntarily captured [START_REF] Baldassi | Attention to locations and features: different topdown modulation of detector weights[END_REF][START_REF] Carrasco | Attention alters appearance[END_REF][START_REF] Yeshurun | Attention improves or impairs visual performance by enhancing spatial resolution[END_REF]. Such facilitatory phenomena are thought to be mediated by the ability of long-range connectivity from non-visual regions to reduce background noise, sharpen the tuning, boost the gain, or reduce the variance in firing activity of neuronal populations located within primary visual areas [START_REF] Liu | Feature-based attention modulates orientation-selective responses in human visual cortex[END_REF][START_REF] Reynolds | Attentional modulation of visual processing[END_REF].

Solid neuroimaging evidence of the human brain has so far helped identify a dorsal network involved in visuo-spatial attentional orienting, with the participation among others, of key cortical sites such as the right Intraparietal Sulcus (IPS) and the Frontal Eye Fields (FEF) [START_REF] Corbetta | The reorienting system of the human brain: from environment to theory of mind[END_REF]. This dorsal system would be supplemented by a ventral network, which would act as a ''circuit breaker'', allowing the re-orientation of attention to unexpected and task-relevant events [START_REF] Corbetta | The reorienting system of the human brain: from environment to theory of mind[END_REF][START_REF] Chica | Dorsal and Ventral Parietal Contributions to Spatial Orienting in the Human Brain[END_REF]. Interestingly, some of these sites appear to co-localize with the nodes of a distributed long-range connectivity network, which, according to theoretical models and neuroimaging data, might play an essential role in access to consciousness [START_REF] Crick | Are we aware of neural activity in primary visual cortex?[END_REF][START_REF] Dehaene | Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework[END_REF][START_REF] Latto | Visual field defects after frontal eye-field lesions in monkeys[END_REF][START_REF] Libedinsky | Role of prefrontal cortex in conscious visual perception[END_REF][START_REF] Lumer | Neural correlates of perceptual rivalry in the human brain[END_REF]. Some understanding of FEF interactions with other brain locations has been provided by non-human primate studies revealing that the microstimulation of this area yields selective perceptual modulations for stimuli presented within locations corresponding to the receptive fields of the stimulated neurons, but not outside [START_REF] Moore | Selective gating of visual signals by microstimulation of frontal cortex[END_REF][START_REF] Moore | Control of eye movements and spatial attention[END_REF]. Likewise, the non-invasive manipulation of the right FEF activity in the human brain by Transcranial Magnetic Stimulation (TMS) has also shown its ability to modulate neural activity in early visual areas [START_REF] Capotosto | Frontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms[END_REF][START_REF] Ruff | Concurrent TMS-fMRI and psychophysics reveal frontal influences on human retinotopic visual cortex[END_REF] and visual performance on the detection of high-contrast and masked targets [START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field: effects on visual perception and attention[END_REF][START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness[END_REF]. All together those studies suggest that frontal activity has the potential to modulate the processing of visual stimuli, particularly under challenging perceptual conditions. Nonetheless, the processes underlying the ability of this specific cortical frontal site to influence and eventually ameliorate visual perception, particularly when manipulated during the time period preceding the onset of a visual target remain debated.

In the current study, we used single TMS pulses to modulate FEF pre-target activity and studied its impact on the conscious perception of low-contrast near-threshold targets (Experiment 1). Given that neurostimulatory effects have been shown to depend on the pre-existing patterns of activity within the targeted region [START_REF] Silvanto | State-dependency in brain stimulation studies of perception and cognition[END_REF][START_REF] Silvanto | Neural adaptation reveals state-dependent effects of transcranial magnetic stimulation[END_REF], we then made use of visuo-spatial cues, likely to modulate neural activity along the dorsal attentional orienting network, to study whether the effects of pre-target FEF TMS interacted or not with the state of activity within that network (Experiment 2). The topic holds the potential to provide novel insights on the role of right FEF activity on conscious visual perception and could also help settle the bases in an upcoming near future, for new strategies to manipulate such region with the goal of enhancing human perceptual capabilities.

Materials and Methods

A group of thirteen participants (8 women and 5 men) aged between 18 and 28 years (average: 24 years old) took part in the study. All participants reported no history of neurological or psychiatric disorders and normal or corrected-to-normal visual acuity. They were all naı ¨ve as to TMS and the purpose of the experiments and participated voluntarily. The research protocol and inform consent was reviewed and sponsored by the Inserm (Institut National de la Sante ´et la Recherche Scientifique) ethical committee and approved by an Institutional Review Board (CPP Ile de France 1, Ho ˆpital de la Pitie

´-Salpe ˆtrie `re). Written informed consent was received from all participants in the study prior to participation. Participants took part in two experiments (Experiment 1 and 2), the order of which was counterbalanced across subjects.

Apparatus, Visual Stimuli, and Tasks

Visual stimuli were displayed on an eye tracker screen (Tobii T50, Technology AB, Danderyd, Sweden, 1799 wide, 10246768, 16.67 ms refresh rate) using a laptop computer (Dell Latitude E6400, Round Rock, Texas, USA) and standard stimulus presentation software (E-prime, Sharpsburg PA, USA). All stimuli were presented against a grey background (RGB : 194, 194, 194) (Figure 1) and eye movements were controlled throughout each trial. The fixation point (a black ''+'' sign of 0.560.5u) was displayed in the center of the screen, along with three black squared boxes (6.0u width65.5u height), one central and two lateral ones (centered 8.5u to the left and right of the fixation point). The target consisted of a Gabor stimulus (2 cycles/deg. spatial frequency, 3.0u diameter, 0.3u of SD, minimum and maximum Michelson contrast of 0.062 and 0.551, respectively), which could appear at the center of one of two lateral boxes for a brief period of time (33 ms). The lines of the Gabor were tilted 1u to 10u to the left or to the right (corresponding 0u to their vertical orientation). Participants were requested to keep fixation on the central cross throughout the trial and to execute two consecutive tasks after the presentation of the target. They were first asked to determine line orientation (categorization task), as fast and as accurately as possible, by pressing the corresponding button on a computer keyboard with the index and middle finger of their right hand (''1'' for left and ''2'' for right). In this task, we encouraged them to respond to every trial within a window of 2000 ms, and to guess a response even when the target was not presented or they did not consciously perceive it. Performance was assessed through accuracy and reaction time measures. Secondly, participants were required to report whether they had consciously seen the target or not (detection task). To do so, two arrow-like stimuli, one below and one above the fixation cross (... and ,,,), pointing to the left and to the right side of the screen were presented. Participants were provided with three keys, which they had to operate with their left hand: an upper key (''d''), a lower key (''c'') and the space bar. The upper key signaled the side of the screen pointed by the arrow presented in the upper part of the fixation point, while the lower key was associated to the side of the screen pointed by the lower arrow. Participants had to respond by pressing the space bar if they did not see the stimulus, or, if they did see it, with the corresponding key (''d'' or ''c'') to indicate the location where the target had been consciously perceived (left or right). The position of the arrows pointing left or right was randomized across trials. Perceptual sensitivity (d') and response bias (beta) used in Signal Detection Theory [START_REF] Green | Signal detection theory and psychophysics[END_REF][START_REF] Macmillan | Detection theory: a user's guide[END_REF] served to assess performance in this task. The former (d') is a bias-free statistic that provides a measure of observers' ability to detect weak signals, while the latter (beta) describes their relative preference for one response over the other. To compute those two parameters, trials in which the location of a target presented in the screen was correctly determined by participants, were considered as correct detections or ''hits''; trials in which the presence of a present target was not acknowledged by participants were considered as ''misses''; trials in which participants reported the location for targets that were not presented on the screen were treated as ''false alarms''; trials in which the target was absent and participants correctly reported not to have seen it were considered ''correct rejections''; and finally, trials in which the location of a present target was incorrectly reported by participants (4% of the 'seen' targets in both experiments) were excluded from the analyses as errors.

A titration procedure performed prior to the experimental trials allowed to determine, in each experiment and for each participant, the stimulus contrast at which ,62% of the displayed targets were consciously reported in the detection task and the degree of line tilting for which performance in the categorization task remained between 65 and 85% correct. Participants started the titration trials with a high contrast stimulus and, every 20 trials, target contrast and line tilting were adjusted in order to converge to the above-mentioned criteria. Experimental trials started once such performance levels were attained and during the experiment, this whole set of stimulus parameters was also automatically adjusted every 20 trials to avoid behavioral fluctuations caused by task practice or fatigue.

In Experiment 1, every trial started with a fixation screen lasting randomly from 1000 to 1500 ms in order to achieve an inter-trial interval of at least two seconds. The fixation cross became then slightly bigger (0.760.7u) for 66 ms to signal the upcoming event.

After an Inter Stimulus Interval (ISI) of 233 ms, the target could appear at the center of one of the two lateral boxes. The experiment consisted of 600 trials, including 120 trials in which the target was absent. In half of the trials, chosen randomly, a single TMS pulse was delivered on the right FEF either at 80, 100 or 140 ms prior to the target onset (active TMS trials). In the other half (sham TMS trials), a single pulse was delivered, at those same timings, by a second TMS coil placed next to the stimulation site, with the coil surface perpendicular to the head surface, preventing the magnetic field from reaching the skull and stimulating the brain.

In Experiment 2, everything was kept the same as in Experiment 1 except for the following. The fixation sign did not increase its size but, instead, a visuo-spatial cue, consisting of a black circle (1.5u diameter), was presented in the upper external corner of one of the two lateral boxes and displayed for 66 ms. After the same ISI (233 ms), the target could appear at the center of the cued (valid trial) or uncued (invalid trial) lateral box. The cue was predictive about the location of the upcoming target (75% valid and 25% invalid trials). A cue was considered valid when it correctly signaled the location of the upcoming target (left or right), and invalid when it incorrectly signals target location. A valid trial was the one including a valid cue whereas the opposite applied to invalid trials. Similarly, validly cued targets were those preceded by a valid cue, whereas invalidly cued targets were preceded by an invalid cue. The experiment consisted of 800 trials, including 160 target-absent trials. Active or sham TMS pulses were only delivered 80 ms pre-target onset, given the inability to test all three timings and keep the session within a reasonable duration. Prior experiments suggested that short pre-target timings had the highest potential to induce behavioral effects [START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness[END_REF].

Transcranial Magnetic Stimulation (TMS)

TMS pulses were delivered using a biphasic repetitive stimulator (Superapid 2 , Magstim, Withland, UK) with a 70 mm diameter figure-eight air-cooled coil (Figure 2). Pulses were triggered through E-prime software (E-prime, Sharpsburg PA, USA) running on a laptop computer (Dell, Latitude 6410). Prior to the experimental tasks, a structural T1-weighted MRI scan was acquired for every participant at the CENIR MRI center (Ho ˆpital de la Pitie ´-Salpe ˆtrie `re, Paris). A 3T Siemens MPRAGE sequence, flipangle = 9, Repetition Time = 2300 ms, Echo Time = 4.18 ms, slice thickness = 1 mm, was used. For the TMS experiments, the right FEF region was localized using previously identified Talairach coordinates x = 31, y = 22, z = 47 [START_REF] Paus | Location and function of the human frontal eye-field: a selective review[END_REF] and labeled with a 0.5 cm radius spherical Region of Interest (ROI) in the MNI space with the Marsbars toolbox (Sourceforge.net). Using SPM5 software (UCL, London, UK), each participant's structural MRI image was segmented into white and gray matter and the inverse segmentation matrix was used to individually de-normalize the ROI (spatial smooth isotropic Gaussian Kernel of 1-mm fullwidth half-maximum). The same software was used to co-register the de-normalized ROI with each participant structural MRI volume, obtaining a precise individual localization of the area. The final MRI was uploaded into a frameless stereotaxic system (eXimia NBS System, Nexstim, Helsinki, Finland) and reconstructed in 3D for online neuronavigation of the TMS coil. Given the small size of the region and the high inter-individual variability in FEF location, a TMS-guided individual functional confirmation of the location of the right FEF was conducted by following a wellestablished protocol based on evidence that a single TMS pulse delivered on the FEF during the preparation time of a saccade has the ability to delay its onset [START_REF] Thickbroom | Transcranial magnetic stimulation of the human frontal eye field[END_REF].

At all times, the active TMS coil was held tangential to the skull, with its handle oriented ,45u in a rostral-to-caudal and lateral-tomedial orientation, i.e., parallel to the central sulcus. The TMS coil was kept steady within an area of ,2 mm radius from the targeted region by using online neuronavigation feedback on each participant's structural MRI. For all interventions, stimulation intensity was initially set up for every subject at 67% of the TMS machine maximum output. Nonetheless, in some participants, intensity had to be slightly decreased to abolish temporal involuntary muscle activation, involuntary blinks or other types of facial sensations. The average intensity at which participants were stimulated was 6661% for both experiments (113612% and 111615% of the mean resting motor threshold in Experiments 1 and 2, respectively).

Data Analysis

Trials in which participants showed response anticipations, i.e. pressed the button before stimulus presentation (0.02% and 0.01% of all trials respectively), or broke fixation and performed eye movements to one of the lateral boxes (3% and 6% of all trials for Experiment 1 and 2, respectively) were eliminated from the analyses. The first three participants taking part in Experiment 1 In both experiments, participants were requested to fixate at a central cross for a randomly variable period of time between 1000 to 1500 ms. In Experiment 1, the fixation cross became slightly bigger for 66 ms and was followed by an active or a sham single TMS pulse delivered on the right FEF, 80,100 or 140 ms prior to target onset. In Experiment 2, a peripheral visuo-spatial cue, consisting in a black circle was displayed for 66 ms to the right or the left of the fixation cross. The cue was predictive about the location of the subsequent target (75% valid and 25% invalid trials), and was followed by a TMS pulse delivered 80 ms pre-target onset. In both experiments active or sham TMS pulses were interleaved in a randomized order. Then, after an interstimulus interval (ISI) of 233 ms, a Gabor with the lines tilted to the left or the right appeared for 33 ms at the center of one of the two lateral boxes. could not be included in the analyses due to a software programming error.

As accuracy in the categorization task was high when participants correctly reported to have seen the target (74% in both experiments) and remained at chance levels when they reported not to have seen it (51% and 49% in Experiment 1 and 2, respectively), only correctly seen target trials were considered for reaction time and accuracy analyses. For each timing (80, 100 and 140 ms), TMS condition (active or sham TMS) and validity (valid and invalid), trials with reaction time faster than 150 ms and outside 4 standard deviations of the mean (0.1% and 0.4% in Experiment 1 and 2, respectively) were eliminated from the analyses as outliers.

All behavioral outcomes (accuracy and reaction time for the categorization task and perceptual sensitivity and response bias for the detection task) were subjected to a repeated measures ANOVA with timing (80, 100 and 140 ms), target location (left and right) and TMS condition (active and sham TMS) as within-participant factors in Experiment 1 and with validity (valid and invalid), target location and TMS condition as within-participant factors in Experiment 2. Such analysis was also performed for detection errors (i.e. target-present trials in which participants incorrectly indicated target location) to rule out any potential TMS effects on these specific types of events. In Experiment 1, no significant main effects or interactions were observed in such trials. In Experiment 2, only a main effect of validity was observed, indicating that participants made fewer errors in valid than invalid trials (F(1, 12) = 13.64, p = 0.003).

Results

In Experiment 1, we used single TMS pulses on the right FEF to test the ability of pre-target activity on this region to modulate conscious visual perception of low-contrast near-threshold targets. Participants correctly reported to have seen the target in 56% of the present-target trials and the mean rate of false alarms was 2%. All measures (accuracy and reaction time for the categorization task and perceptual sensitivity and response bias for the detection task) were subjected to a repeated measures ANOVA with timing (80, 100 and 140 ms), target location (left and right) and TMS condition (active and sham TMS) as within-participant factors. In the categorization task, no significant effects of TMS condition were observed. Only a main effect of target location in reaction time reached significance (F(1,9 = 7.88, p = 0.020), participants being faster for targets displayed on the right than on the left visual hemifield. Responses also proved to be more accurate when responding to right than left targets (F(1,9) = 6.68, p = 0.030). In contrast, in the detection task, a main effect of TMS condition reached significance, with overall higher perceptual sensitivity (d') under active than sham TMS pulses (F(1,9) = 8.31, p = 0.018). On the basis of the a priori hypothesis that stimulation should depend on pulse delivery time, we performed three separate repeated measures ANOVA for the three TMS timings, with side and TMS condition as within-participant factors. The TMS effect only reached significance when pulses were delivered 80 ms pre-target onset (F(1, 9) = 9.77, p = 0.012), but not when applied 100 ms (F = 5.09, p = 0.051) or 140 ms (F = 3.95, p = 0.078) pre-target onset (Figure 3 and Table 1). No main effects or interactions reached statistical significance for the response bias (beta).

In Experiment 2, FEF TMS was delivered after the engagement of the dorsal attentional orienting network by a peripheral visuospatial cue, which was predictive about the location of the subsequent target. Given our purpose of studying the combined effects of a single TMS pulse and a cue-driven engagement of attentional orienting, only participants effectively orienting their attention according to the cue, and thus exhibiting cueing effects under sham TMS trials, were considered for further analyses. An assessment of the perceptual effects induced by visuo-spatial attentional orienting using the exact same paradigm (see Experiment 4 in [START_REF] Chica | Spatial attention and conscious perception: the role of endogenous and exogenous orienting[END_REF] for details) demonstrated that, for this very same categorization task, effective visuo-spatial attentional orienting entailed significant reaction time reductions in valid as compared to invalidly cued targets. Accordingly, the presence of a significant cueing effect was statistically assessed in our participants by comparing the mean reaction time of valid vs. invalid sham TMS trials. Seven out of the thirteen participants showed statistically significant reductions of reaction time for valid vs. invalid sham TMS trials (unpaired 1 tailed t-test, p,0.05) and thus were considered as exhibiting cueing effects.

Those participants reported to have seen the target in 58% of the present-target trials and the mean rate of false alarms was 6%. All measures (accuracy and reaction time for the categorization task and perceptual sensitivity (d') and response bias (beta) for the detection task) were subjected to a repeated measures ANOVA with validity (valid and invalid), target location (left and right) and TMS condition (active and sham TMS) as within-participant factors. In the categorization task, only a main effect of validity in reaction time reached significance (F(1, 6) = 60.22, p,0.001), with faster responses for valid than invalid trials. In the detection task, a significant interaction between validity and TMS condition was observed on perceptual sensitivity (F(1, 6) = 6.54, p = 0.043), indicating the dependency of TMS effects on the validity of the cue. More specifically, active stimulation improved perceptual sensitivity (d') only when the cue correctly predicted the location of the target (valid trials), as compared to sham TMS (F = 19.26, p = 0.005). Interestingly, no differences between active and sham TMS were observed for invalid trials, in which the cue incorrectly predicted the location of the target (F,1) (Figure 4, Table 2). No significant main effects or interactions were observed for the response bias (beta).

Discussion

The potential of non-invasive brain neurostimulation to boost cognitive performance beyond the limits set up by individual skills and capabilities in healthy [START_REF] Hilgetag | Enhanced visual spatial attention ipsilateral to rTMS-induced 'virtual lesions' of human parietal cortex[END_REF][START_REF] Waterston | Improved discrimination of visual stimuli following repetitive transcranial magnetic stimulation[END_REF][START_REF] Galea | Disruption of the dorsolateral prefrontal cortex facilitates the consolidation of procedural skills[END_REF][START_REF] Reis | Consensus: Can transcranial direct current stimulation and transcranial magnetic stimulation enhance motor learning and memory formation?[END_REF][START_REF] Gallate | Reducing false memories by magnetic pulse stimulation[END_REF][START_REF] Bolognini | Brain polarization of parietal cortex augments training-induced improvement of visual exploratory and attentional skills[END_REF][START_REF] Tadin | Improved motion perception and impaired spatial suppression following disruption of cortical area MT/V5[END_REF] and patholog-ical states [START_REF] Oliveri | rTMS of the unaffected hemisphere transiently reduces contralesional visuospatial hemineglect[END_REF][START_REF] Khedr | Therapeutic trial of repetitive transcranial magnetic stimulation after acute ischemic stroke[END_REF][START_REF] Fregni | Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory[END_REF][START_REF] Silvanto | Making the blindsighted see[END_REF][START_REF] Ferrucci | Transcranial direct current stimulation improves recognition memory in Alzheimer disease[END_REF] has been postulated for more than a decade. Thanks to its ability to activate discrete cortical regions and associated networks [START_REF] Valero-Cabre | Impact of repetitive transcranial magnetic stimulation of the parietal cortex on metabolic brain activity: a 14C-2DG tracing study in the cat[END_REF], TMS, a focal magnetically-based non-invasive brain stimulation technique, has been shown to induce punctual or lasting changes in the firing patterns of restricted key cortical regions and, in virtue of such capabilities, influence normal or pathological human behavior [START_REF] Wagner | Noninvasive human brain stimulation[END_REF][START_REF] Miniussi | Efficacy of repetitive transcranial magnetic stimulation/transcranial direct current stimulation in cognitive neurorehabilitation[END_REF]. We hereby assayed in healthy humans whether conscious visual Table 1. Data from TMS-induced modulations of right FEF pre-target activity on visual performance (Experiment 1). perception of low-contrast near-threshold targets could be enhanced with non-invasive neurostimulation, by modulating the activity of the right FEF prior to the onset of a visual target. Such brain region has been shown to be involved in visuo-spatial attentional orienting [START_REF] Corbetta | The reorienting system of the human brain: from environment to theory of mind[END_REF] and also to have bearing on conscious access [START_REF] Crick | Are we aware of neural activity in primary visual cortex?[END_REF][START_REF] Dehaene | Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework[END_REF][START_REF] Latto | Visual field defects after frontal eye-field lesions in monkeys[END_REF][START_REF] Libedinsky | Role of prefrontal cortex in conscious visual perception[END_REF][START_REF] Lumer | Neural correlates of perceptual rivalry in the human brain[END_REF] for visual stimuli. In agreement with prior work [START_REF] Moore | Selective gating of visual signals by microstimulation of frontal cortex[END_REF][START_REF] Moore | Control of eye movements and spatial attention[END_REF][START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field: effects on visual perception and attention[END_REF][START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness[END_REF][START_REF] Thompson | The detection of visual signals by macaque frontal eye field during masking[END_REF], our data from Experiment 1 indicate that right FEF pre-target activity is indeed relevant for conscious perception and that its non-invasive manipulation with TMS can induce relevant visual perceptual sensitivity improvements. Interestingly, when the dorsal attentional orienting network was previously activated by means of peripheral predictive visuospatial cues (Experiment 2), the modulation of right FEF pre-target activity with TMS pulses brought visual perceptual sensitivity modulations, which were shaped according to cue validity. More specifically, only when the prior visuo-spatial cue correctly predicted the site (left or right) of the subsequent target (valid trials) but not when it incorrectly predicted it (invalid trials), TMS induced facilitatory effects on conscious detection. These results suggest that cue-driven neural activations related to attentional orienting interact with conscious perception and have the potential to sculpt the effects of time locked pre-target FEF stimulation and render such perceptual facilitatory outcomes more specific. In spite of the lack of an active control condition mimicking not only the TMS clicking noise but also the scalp tapping sensations, the lack of significant effects when TMS pulses were combined with invalid spatial cues became an internal control that rules out a hypothetical contribution of such phenomena to our results.

Prior studies have demonstrated that the impact of non-invasive neurostimulation can be highly influenced by the pre-existing patterns of activity within the stimulated region and its associated networks [START_REF] Silvanto | State-dependency in brain stimulation studies of perception and cognition[END_REF][START_REF] Silvanto | Neural adaptation reveals state-dependent effects of transcranial magnetic stimulation[END_REF]. In our experiments, visuo-spatial cues could have differentially modulated the firing patterns of distinct neuronal subpopulations within the right FEF region, prior to the onset of neurostimulation, and hence have primed the effects of FEF TMS only for those under certain states of activation. In support of this hypothesis, non-human primate research has shown that peripheral predictive visuo-spatial cues increase (and maintain increased along the cue-to-target period) the firing patterns of the FEF neurons that specifically code for the signaled location, but not for those whose receptive fields lay outside the cued site [START_REF] Armstrong | Selection and maintenance of spatial information by frontal eye field neurons[END_REF]. On the basis of this observation, different activity levels or 'states' of activation across FEF neuronal subpopulations as driven by visuo-spatial cues could easily explain how, on a trial-by-trial basis, highly selective visual facilitation patterns could emerge from the stimulation of roughly the same cortical resources as a function of cue validity [START_REF] O'shea | Visual awareness: the eye fields have it?[END_REF].

Our data indicate that the FEF TMS visual facilitatory effects interacted with the orienting of spatial attention engaged by means of predictive spatial cues. Nonetheless, given the frequently hypothesized role of the right FEF not only as a crucial node of the dorsal attentional network but also as relevant in providing access to consciousness, which of these two systems might have been ultimately responsible for the observed visual facilitatory effects remains unclear. Contributing to the discussion of this issue, our data reveal that FEF TMS neither when used in isolation (Experiment 1) nor when combined with visuo-spatial cues (Experiment 2) did modulate the reaction times or accuracy levels for the visual categorization task. A behavioral study performed and published separately by our group assessed the behavioral effects of visuospatial attentional orienting in the same exact paradigm, and showed significant shorter reaction times in response to stimuli presented at attended than unattended locations (see [START_REF] Chica | Spatial attention and conscious perception: the role of endogenous and exogenous orienting[END_REF] Experiment 4 for details). The latter effects, which were accompanied by a modulation in perceptual sensitivity in the detection task only when the cue was predictive about target location, strongly suggest that cuevalidity effects in such paradigm should be considered a solid signature of attentional orienting. On such basis, it is tempting to interpret the current lack of reaction time modulations for the categorization task, accompanying improvements in visual detection by FEF pre-target activity modulations, not as ultimately mediated by the manipulation of visuo-spatial orienting processes but reflecting a genuine effect of right FEF TMS on visual consciousness. In spite of obvious differences between intact and damaged systems, this interpretation could be in agreement with patient work showing a relevant role of the prefrontal cortex in access to consciousness of masked stimuli, not accountable either by attentional orienting processes [START_REF] Cul | Brain dynamics underlying the nonlinear threshold for access to consciousness[END_REF]. Nonetheless, given that attention can alter appearance [START_REF] Carrasco | Attention alters appearance[END_REF] and that in our paradigm composed of two serial tasks, subjects could have eventually sacrificed reaction time for accuracy, or categorization performance for detection performance, whether attention can modulate conscious visibility without affecting reaction time remains an open question. Our data contribute further evidence in support of the notion that the right FEF and its associated systems may constitute according to monkey [START_REF] Moore | Selective gating of visual signals by microstimulation of frontal cortex[END_REF][START_REF] Moore | Control of eye movements and spatial attention[END_REF][START_REF] Thompson | The detection of visual signals by macaque frontal eye field during masking[END_REF] and human [START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness[END_REF][START_REF] O'shea | Visual awareness: the eye fields have it?[END_REF] data, a key area facilitating access to consciousness for visual stimuli. Moreover, our combined modulation strategy based on an 'at will' stimulation of the FEF and the presentation of visuo-spatial cues, showed its ability to selectively enhance human visual awareness for low-contrast near-threshold stimuli and to shape the specificity of such effects, thus setting up the stage for the use of TMS on the direct manipulation of visual conscious perception in healthy and pathological states. Unfortunately, in absence of brain neuroimaging data, we cannot yet rule out if such facilitatory phenomena were driven locally at the stimulated right FEF and directly manipulated by the alleged ability of this area to contribute to visual awareness; emerged from connectivityconveyed trans-synaptic effects on primary visual regions through fronto-parietal-occipital top-down projections [START_REF] Ruff | Concurrent TMS-fMRI and psychophysics reveal frontal influences on human retinotopic visual cortex[END_REF][START_REF] Ekstrom | Bottom-up dependent gating of frontal signals in early visual cortex[END_REF]; or resulted from the modulation of other intermediate cortical or subcortical structures interconnected with the FEF. This remains a highly relevant question to be addressed in an immediate future through specific experiments which, as elegantly performed elsewhere [START_REF] Ruff | Concurrent TMS-fMRI and psychophysics reveal frontal influences on human retinotopic visual cortex[END_REF][START_REF] Ekstrom | Bottom-up dependent gating of frontal signals in early visual cortex[END_REF] might require the combination of stimulation and neuroimaging. Moreover, in the current study, we manipulated activity patterns within the right FEF since this area is a key component of the dorsal network devoted to visuo-spatial attentional orienting; its anatomical location can be individually confirmed through a well-established mapping procedure; there is precedence on its ability to induce connectivity mediated functional modulation on visual regions, and in consideration of its hypothesized role in visual awareness. In agreement with findings suggesting the dominant role of the right hemisphere sites in attentional orienting and consciousness [START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field: effects on visual perception and attention[END_REF][START_REF] Grosbras | Transcranial magnetic stimulation of the human frontal eye field facilitates visual awareness[END_REF][START_REF] Hilgetag | Enhanced visual spatial attention ipsilateral to rTMS-induced 'virtual lesions' of human parietal cortex[END_REF], our intervention in the right FEF proved similarly efficacious for right and left targets. Prior studies have also reported bilateral effects for right FEF activity modulations, whereas the manipulation of the left FEF stimulation would be restricted to an influence on targets presented in the right visual hemifield. Future venues will have to explore the role of left FEF pre-target activity in conscious visual perception and the extent of such effects throughout the visual field. Furthermore, functional MRI and TMS brain-function studies suggest that the modulation of non necessarily frontal regions, such as the right intraparietal sulcus or the angular gyrus [START_REF] Corbetta | The reorienting system of the human brain: from environment to theory of mind[END_REF] could potentially also interact with cue validity and result in visual facilitatory effects, and thus they would also deserve to be explored in similar paradigms in the future.

In sum, our findings show that FEF pre-target activity can be effectively manipulated to influence conscious visual perception using non-invasive neurostimulation methods, and that a com-bined strategy based on right hemisphere frontal stimulation and visual cues can be implemented not only to episodically enhance visual performance, but to shape the selectivity of those effects. The fact that a combination of TMS and attentional cues can indeed improve visual sensitivity should be considered a proof of concept that visual capabilities can be manipulated and improved through those approaches. On that basis, strategies operating on cerebral sites involved in attentional orienting and conscious access could become a reality to punctually increase visual capabilities in healthy participants. Similar principles could be also applied to clinical rehabilitation, aiming at containing visual acuity losses in patients with retinal defects, and allowing the emergence of episodic or lasting periods of conscious vision in cortically damaged patients. Nonetheless, it should also be strongly emphasized that the ameliorations demonstrated in our study operate trial-by-trial and remain extremely short lasting. Furthermore they have been demonstrated for lateralized right or left peripheral detections and thus might not equally occur for targets presented in other locations of the visual hemifield. Both aspects weaken the current applicability of the results for meaningful behavioral ameliorations in healthy individuals or therapeutic applications in patients. In order to overcome such limitations, however, longer rTMS patterns and multi-day rTMS regimes combined with spatial cuing paradigms remain to be studied and evaluated for their ability to generate lasting increases in visual sensitivity. Similarly, the differential ability of TMS based approaches to generate ameliorations for targets presented at different visual field locations than those tested in the current paper would need to be studied before our findings could be considered potentially interesting for clinical applications.

Introduction

Evidence in support of the fundamental role played by cerebral oscillations in cognitive processing has strongly emerged in the last decades. In the particular domain of visual perception, prior work has indicated that for different brain regions and systems, from sensory areas to large-scale frontoparietal networks exerting top-down influences on visual processing and behavioral performance, oscillatory activity at specific frequency bands might contribute to distinct aspects of behavior [START_REF] Donner | Population activity in the human dorsal pathway predicts the accuracy of visual motion detection[END_REF][START_REF] Fries | Neuronal gamma-band synchronization as a fundamental process in cortical computation[END_REF]Siegel et al., 2011). However, the specific role of neural oscillatory frequency remains to be causally isolated.

For nearly two decades, the ability of non-invasive neurostimulation to depolarize local neuronal clusters and interfere with neural processing has been used to establish, in several cognitive domains, causal associations between brain regions and behaviors (Wagner et al., 2007). Recently, by comparing the behavioral modulations obtained at different frequencies, transcranial alternate current stimulation and short-burst and repetitive transcranial magnetic stimulation (TMS) have been used in the investigation of the oscillatory basis of human cognition [START_REF] Klimesch | Enhancing cognitive performance with repetitive transcranial magnetic stimulation at human individual alpha frequency[END_REF]Kanai et al., 2008;Romei et al., 2010Romei et al., , 2011;;[START_REF] Feurra | Frequency specific modulation of human somatosensory cortex[END_REF]. More importantly, seminal work performed in this field has demonstrated that uniform TMS bursts can entrain rhythmic brain oscillation patterns tuned to the applied input frequency (Thut et al., 2011).

In the present study, we compared the behavioral impact of frequency-specific (or uniform) and non-frequency-specific (or non-uniform) TMS patterns delivered over a right frontal region to provide causal evidence in humans on the modulatory role of high-beta and gamma activity to distinct aspects of human visual performance. For both frequencies, these two types of TMS patterns had equal duration and number of pulses. Nonetheless, in frequency-specific patterns pulses were uniformly distributed across the duration of the burst, whereas in non-frequency-specific patterns, those were delivered at fixed unequal interpulse intervals. This approach warrants the delivery of an identical amount of activity during the same time interval in both types of bursts, isolating the specific impact of stimulation frequency to behavior.

We targeted the right frontal eye field (FEF), a site involved in visuospatial attentional orienting (Corbetta and Shulman, 2002;Corbetta et al., 2008) and conscious perception (Grosbras and Paus, 2003;Moore and Armstrong, 2003;Libedinsky and Livingstone, 2011;Chanes et al., 2012), holding rich interactions with parietal and occipital brain regions, that can engage in high-beta (30 Hz) and gamma (50 Hz) oscillatory activity [START_REF] Fries | Modulation of oscillatory neuronal synchronization by selective visual attention[END_REF]Buschman and Miller, 2007;Gregoriou et al., 2009). More specifically, Buschman and Miller (2007) reported a differential involvement of these two oscillation frequencies in a pop-out and a visual search task engaging, respectively, exogenous and endogenous attentional orienting processes, which are known to induce distinct behavioral influences on human visual performance (Chica et al., 2011). We hypothesized that uniform TMS bursts delivered over the right FEF at high-beta versus gamma frequency would yield frequency-specific effects on visual performance, compared with their equivalent non-uniform TMS patterns not tuned to any particular frequency.

Materials and Methods

Two groups of 14 participants (6 women and 8 men and 10 women and 4 men), aged between 19 and 39 years (average of 25 Ϯ 3 and 25 Ϯ 6 years old) reporting no history of neurological or psychiatric disorders and normal or corrected-to-normal vision, took part in the experiments. Twenty-three of them were naive as to TMS and to the purpose of the experiments, and they all participated voluntarily. The protocol was reviewed by the Inserm ethical committee and approved by an Institutional Review Board (CPP Ile de France 1).

Apparatus, stimuli, and procedure. Visual stimuli were displayed on an eye-tracker screen (Tobii Technology AB; 17 inches wide, 1024 ϫ 768) using a laptop computer (Dell Latitude E6400) and standard stimulus presentation software (E-Prime Software). Each trial started with a gray resting screen (luminance: 75 cd/m 2 , 2500 ms), followed by a fixation screen (randomly lasting between 1000 and 1500 ms) (Fig. 1a). The fixation cross (0.5 ϫ 0.5°) was displayed in the center, along with three rectangular boxes (6.0 ϫ 5.5°): one central and two lateral ones (centered 8.5°to the left and right of the fixation point). Then, the fixation cross became slightly larger (0.7 ϫ 0.7°, 66 ms) to alert participants of an upcoming event. After an interstimulus interval (233 ms), a target appeared at the center of one of the two lateral boxes for a brief period of time (33 ms). The target consisted of a low-contrast Gabor stimulus (2 cycles/degree spatial frequency, 3.0°diameter, 0.3°of SD, minimum and maximum Michelson contrast of 0.031 and 0.283, respectively) with its lines tilted 1°to 10°clockwise or counterclockwise. The intertrial interval lasted at least 4 s.

Participants were asked to execute two tasks. The first task was to determine the orientation of the Gabor lines (discrimination task) by pressing the corresponding button on a computer keyboard ("1" for left and "2" for right) with the index and middle fingers of their right hand. Participants were forced to guess a response even when the target was not present or they did not consciously perceive it and accuracy was collected as outcome measure. The second task was to report whether they had consciously perceived the Gabor or not (conscious detection task). To do so, two arrow-like stimuli (ϽϽϽ and ϾϾϾ) pointing to the left and to the right were simultaneously presented below and above the fixation cross. Participants were provided with 3 keys, which they had to operate with their left hand: an upper key "d," a lower key "c," and the space bar. The upper and lower keys were associated to the arrows presented on the top and the bottom, respectively. Participants had to respond by pressing the space bar if they did not see the stimulus, or the key ("d" or "c") to select the arrow pointing to the visual hemifield (right/left) in which they perceived the target. The location of each arrow, above or below the fixation point, was randomized across trials. This task was assessed through perceptual sensitivity (dЈ) and response criterion (beta), two measures used in Signal Detection Theory (Green and Swets, 1966;Macmillan and Creelman, 2005). Perceptual sensitivity is a bias-free measure that informs on participants' ability to detect weak signals in situations that might be strongly influenced by belief. Response bias (beta) describes the relative preference of participants for one response over the alternative one, independently on signal strength. When participants favor neither a "yes, I saw it" response nor a "no, I did not see it" response, beta is equal to 1. Values lower than 1 indicate a bias toward the affirmative response, whereas values Ͼ1 indicate a bias toward the negative response. To compute these measures, trials in which the location of a target was correctly determined by participants were considered as correct detections or "hits"; trials in which the presence of the target was not acknowledged were counted as "misses"; trials in which participants reported the location for targets that were not presented were considered "false alarms"; trials in which the target was absent and participants correctly reported not to have seen it were considered "correct rejections"; and, finally, trials in which the location of a present target was incorrectly reported were counted as "errors" and excluded from further analyses. Eye movements were monitored during each trial for fixation control purposes. Fixation was considered broken when participants' eyes position was recorded outside the central box (i.e., 3°away from the fixation cross horizontally and 2.75°vertically).

A titration procedure performed before the onset of the experiment allowed us to determine for each participant the stimulus contrast at which ϳ50% of the displayed targets were consciously reported. The degree of line tilting was also adjusted to maintain discrimination accuracy between 65 and 85% of correctly reported targets. Such titration levels ensured that in both tasks, performance was halfway between the worst (0% in the detection task and 50% in the forced-choice discrimination task) and the best possible performance (100% for both tasks).

Participants started the titration trials with a high contrast stimulus and, every 20 trials, target contrast and the degree of line tilting were adjusted (in steps of 0.07 Michelson contrast and 1°of tilting, respectively) to converge to the preestablished criteria. The experiment started once performance levels reached those criteria. Throughout the experiment, stimulus parameters were automatically adjusted every 20 trials to maintain these titration levels.

Each block consisted of 200 trials, including 40 trials in which the target was absent. In half of the trials, a short burst of 4 TMS pulses was applied to the right FEF (active TMS trials) so that the last pulse of each burst was always delivered 16 ms before target onset. In the other half (sham TMS trials) the same short burst was delivered by a second TMS coil placed next to the stimulation site, with the coil surface perpendicular to the head surface, preventing the magnetic field from reaching the skull and stimulating the brain. The order of active and sham TMS trials was randomized across trials. Participants were allowed to take a short break every 40 trials and at the end of each sub-block received feedback on the screen about their performance and eye movement rates.

In two groups of participants, we explored the effects of two different stimulation frequencies, high-beta (30 Hz) and gamma (50 Hz), on visual performance. Each group performed two blocks of trials: in the frequency-specific block, 4 TMS pulses were distributed uniformly, whereas in the non-frequency-specific block, pulses were unequally distributed over the same period of time (the first and last pulses occurred at timings identical to those in the frequency-specific block, whereas the second and third pulses were slightly anticipated and delayed respectively; Fig. 1b). The order in which participants performed the two blocks was counterbalanced across participants.

Transcranial magnetic stimulation (TMS). TMS pulses were delivered using a biphasic repetitive stimulator (Superapid 2, Magstim) with a 70-mm-diameter figure-of-eight coil (Fig. 1c). A structural T1-weighted MRI scan (3T Siemens MPRAGE, flip angle ϭ 9, TR ϭ 2300 ms, TE ϭ 4.18 ms, slice thickness ϭ 1 mm) was acquired for every participant at the CENIR (Centre de Neuro-Imagerie de Recherche) MRI center (Salpe ˆtrie `re Hospital, Paris). The right FEF region was localized on each individual MRI using averaged Talairach coordinates x ϭ 31, y ϭ Ϫ2, z ϭ 47 (Paus, 1996) and a 0.5 cm radius spherical region of interest (for details see Chanes et al., 2012). The final MRI was uploaded into a frameless stereotaxic system and reconstructed in 3D for its use in an online stereotaxic TMS neuronavigation system (eXimia NBS System, Nexstim).

At all times, the TMS coil was held tangentially to the skull, with its handle oriented ϳ45°in a rostral-to-caudal and lateral-to-medial orientation, i.e., approximately parallel to the central sulcus. Coil position was tracked online throughout the experiments and kept steady within an area of ϳ2 mm radius from the targeted site. The representation of the right primary motor cortex (M1) of the abductor pollicis brevis muscle was located and the left and right motor thresholds were determined as the TMS intensity yielding thumb twitching responses in ϳ50% of the attempts.

For all interventions, stimulation intensity was set up at 45% of the TMS machine maximal output. Nonetheless, in some participants such a level had to be slightly decreased to abolish temporal and facial muscle involuntary activations, blinks, or other types of facial sensations induced by magnetic field spread. Before the experiment, we verified on each participant that none of the TMS FEF patterns used induced contralateral motor activations on forearm or hand muscles. The average intensities at which participants were stimulated were 44.3% (SD 1.5%) and 44.9% (SD 0.5%) of the maximum machine output for the 30 and 50 Hz bursts groups, respectively (i.e., 72 Ϯ 13% and 74 Ϯ 14% of their individual motor thresholds).

Statistical analyses. Outcome measures (perceptual sensitivity and response criterion for the conscious detection task and accuracy for the discrimination task) of each group (30 and 50 Hz) were subjected to a 2 ϫ 2 ϫ 2 repeated-measures ANOVA with block (frequency-and nonfrequency-specific), target location (left and right visual field), and TMS condition (active and sham) as within-participant factors. The same ANOVA was performed for trials in which participants reported to have seen the target but incorrectly determined its location (error trials), which were eliminated from the analyses, to exclude any potential effect Figure 1. Experimental design. a, Following a period of central fixation, a low-contrast near-threshold Gabor stimulus was briefly presented within a left or right peripheral box. Participants were requested to perform a discrimination task, indicating the orientation of the Gabor lines (left/right), followed by a conscious detection task in which they reported if they had seen or not a Gabor and where ("no" or if "yes," left/right). b, Schematic drawing representing the temporal distribution of the 4 pulses of each TMS burst in frequency-specific (uniform) and non-frequency-specific (nonuniform) blocks. For each stimulation frequency (30 Hz and 50 Hz), bursts used in either block were equal in duration and number of pulses. c, TMS coil positioning on the right FEF, displayed in a representative 3D reconstructed native MRI brain and its associated sagittal, axial and coronal brain sections.

of TMS in such trials. Finally, the factor group (30 and 50 Hz) was implemented as between-participant factor in a general ANOVA with the same within-participant factors described above.

Results

One participant was excluded from the analyses in the 30 Hz group because broke fixation in Ͼ50% of the trials. Trials in which participants broke fixation were eliminated from the analyses (8% and 5% for the 30 and 50 Hz groups, respectively). Moreover, error trials were also eliminated (3% and 7% of seen targets for the 30 and 50 Hz groups, respectively). The repeatedmeasures ANOVA for errors did not yield any significant main effects or interactions, indicating that those were similar across conditions. The average number of trials per participant used in the analyses of each experimental condition ranged between 35 and 38 (mean Ϯ SD: 36 Ϯ 4). In the discrimination task, participants' general accuracy was 63% and 64% for the 30 Hz and 50 Hz groups, respectively. As expected, it was high when they reported to have seen the target (75% and 76%, respectively), and it remained at chance levels when they reported not to have seen it (50% for both groups).

Our data revealed frequency-specific contributions of FEF activity to visual performance (Fig. 2; Table 1). For the conscious detection task, active TMS bursts delivered at 30 Hz, but not at the matched non-frequency-specific patterns, improved participants' perceptual sensitivity (significant interaction between block and TMS condition, F (1,12) ϭ 6.07, p ϭ 0.030). Scores were higher for Notice that active 30 Hz TMS bursts (but not their non-frequency-specific associated bursts) enhanced perceptual sensitivity (dЈ) compared with sham TMS patterns. Neither uniform 50 Hz bursts nor their associated non-frequency-specific patterns modulated this outcome measure. In contrast, active 50 Hz TMS bursts (but not its associated non-frequency-specific burst) relaxed response criterion for active TMS bursts compared with sham. No significant criterion differences were observed either for 30 Hz TMS bursts or for their associated non-frequency specific patterns. Asterisks indicate statistically significant differences for active versus sham TMS conditions.

active than for sham TMS only when pulses were uniformly delivered at 30 Hz (planned comparison active vs sham for the frequencyspecific block: F ϭ 5.55, p ϭ 0.036), but not when non-uniform patterns were used (planned comparison active vs sham for the nonfrequency-specific block: F Ͻ 1) (Fig. 2a, left). Moreover, no significant main effects or interactions were observed for the response criterion (Fig. 2b,left). In the discrimination task, no significant modulations of accuracy were observed in any of the blocks.

On the other hand, TMS bursts delivered at 50 Hz and their corresponding non-frequency-specific patterns proved unable to modulate perceptual sensitivity (dЈ) in the conscious detection task (Fig. 2a, right). However, the uniform pattern shifted response criterion (significant interaction between block and TMS condition, F (1,13) ϭ 6.42, p ϭ 0.025). Active 50 Hz TMS bursts decreased the strictness of participants' response criterion compared with sham TMS (planned comparison active vs sham for the frequency-specific block: F ϭ 13.37, p ϭ 0.003), whereas no significant differences in response criterion were observed when TMS bursts were delivered at their associated non-frequencyspecific pattern (planned comparison active vs sham for the nonfrequency-specific block: F ϭ 1.05, p ϭ 0.325) (Fig. 2b,right). In the discrimination task, no main effects or interactions were observed.

Finally, the significant interaction between group ϫ block ϫ TMS condition for both perceptual sensitivity (F ϭ 6.84, p ϭ 0.015) and response criterion (F ϭ 10.14, p Ͻ 0.01) when group (30 and 50 Hz) was integrated as between-participant factor in a general ANOVA emphasizes the frequency specificity of the TMS impact.

Discussion

Our findings shed novel light on the oscillatory basis underlying visual detection behavior for near-threshold stimuli and suggest distinct modulatory roles for high-beta and gamma frontal activity in visual performance.

Frontal 30 Hz TMS bursts impacted perceptual sensitivity (dЈ) in a conscious visual detection task, whereas 50 Hz TMS patterns yielded changes in response criterion. Beta oscillations from the FEF have been previously correlated with modulations of visual performance [START_REF] Gross | Modulation of long-range neural synchrony reflects temporal limitations of visual attention in humans[END_REF][START_REF] Donner | Population activity in the human dorsal pathway predicts the accuracy of visual motion detection[END_REF], and we thereby show here that such activity could be causally linked to these behavioral effects. These oscillations may reflect reverberant activity within and among visual, frontoparietal and frontal motor cortices [START_REF] Engel | Beta-band oscillations-signalling the status quo?[END_REF], which might facilitate the accumulation and maintenance of sensory evidence for decisionmaking [START_REF] Donner | Population activity in the human dorsal pathway predicts the accuracy of visual motion detection[END_REF]Siegel et al., 2011). Importantly, our data provide direct proof that the processing of visual sensory evidence could be episodically enhanced by an extrinsic source of neural synchronization, such as TMS, tuned to a specific oscillation frequency.

Patterns at 50 Hz over the right FEF specifically decreased response criterion when detecting faint near-threshold stimuli. This finding is consistent with prior work showing a correlation between prestimulus gamma-band oscillations in occipital regions and decision biases (Wyart and Tallon-Baudry, 2009). We now extend this result to frontal regions, and, most importantly, we hypothesize a causal contribution of gamma-band oscillations to such processes. The modulation of these phenomena by stimulus features reported previously suggests that oscillatory activity at this frequency band is likely to reflect sensory evidence [START_REF] Frien | Fast oscillations display sharper orientation tuning than slower components of the same recordings in striate cortex of the awake monkey[END_REF][START_REF] Siegel | A functional gamma-band defined by stimulusdependent synchronization in area 18 of awake behaving cats[END_REF][START_REF] Kayser | Stimulus locking and feature selectivity prevail in complementary frequency ranges of V1 local field potentials[END_REF][START_REF] Hall | The missing link: analogous human and primate cortical gamma oscillations[END_REF][START_REF] Henrie | LFP power spectra in V1 cortex: the graded effect of stimulus contrast[END_REF][START_REF] Hoogenboom | Localizing human visual gamma-band activity in frequency, time and space[END_REF]Liu and Newsome, 2006;[START_REF] Vidal | Visual grouping and the focusing of attention induce gamma-band oscillations at different frequencies in human magnetoencephalogram signals[END_REF]Siegel et al., 2007;Berens et al., 2008;Wyart andTallon-Baudry, 2008, 2009). Accordingly, we hypothesize that the induction of a brief lowgamma pretarget pattern on a higher cortical region, such as the right FEF, holding connections with the visual cortex, may have been encoded as sensory evidence, and thus favored a conscious affirmative detection response ("Yes I saw it") over a negative one ("No I did not see it") for near-threshold targets, independent of stimulus presence, hence decreasing the strictness of the response criterion.

The double dissociation observed between stimulation frequency (30 Hz vs 50 Hz bursts) and behavioral outcome measure (perceptual sensitivity vs response criterion), together with the lack of behavioral modulations when the same activity (equal number of TMS pulses delivered across the same interval at identical stimulation intensity) was induced by a slightly different TMS pattern indicate that the observed effects are a specific consequence of stimulation frequency, rather than depend on the total amount of activity induced within a critical time window. Furthermore, none of our interventions proved able to modulate the visual discrimination task, suggesting that the induced activity might not impact visual performance at a purely perceptual level but could rather operate on conscious access. Alternatively, the discrimination task could not have been modulated simply because it might require more refined processing and accumulation of more evidence.

In sum, our results provide causal evidence that characteristic spatiotemporal activity patterns induced by neurostimulation to the same cerebral region can yield exquisitely distinct behavioral outcomes such as increases of visual sensitivity and decreases of response criterion. Such evidence may reflect a general brain mechanism to multiplex functions within the same neural substrate [START_REF] Thut | The functional importance of rhythmic activity in the brain[END_REF]. Findings coherent with this notion have been reported for parietal TMS bursts delivered at lower stimulation frequencies (Romei et al., 2010(Romei et al., , 2011)). Similarly, recordings in nonhuman primates have provided correlational evidence of enhanced gamma frequency synchrony between frontal and posterior parietal regions during exogenous attention in a pop-out visual detection task, and synchrony increases at the high-beta range between these Allstatisticallysignificantmaineffectsandinteractions(repeated-measuresANOVA)areindicatedinthetablebyanasterisk (*p Ͻ 0.05). In addition to the significant interaction between block and TMS condition reported, for 30 Hz TMS patterns and its associated non-frequency-specific bursts: perceptual sensitivity was higher for targets displayed to the right than to the left visual field (F [START_REF] Posner | Attention and the detection of signals[END_REF][START_REF] Libedinsky | Role of prefrontal cortex in conscious visual perception[END_REF] ϭ 10.98, p ϭ 0.006) and for frequency-specific (or uniform) blocks than for non-frequencyspecific (or non-uniform) blocks (F (1,12) ϭ 6.33, p ϭ 0.027). For the 50 Hz TMS pattern (and their corresponding nonfrequency-specific or non-uniform bursts), in addition to the significant interaction between block and TMS condition reported, response criterion was more relaxed for targets displayed to the left than to the right visual field (F (1,13) ϭ 8.07, p ϭ 0.014) and with active than sham TMS bursts (F (1,13) ϭ 7.95, p ϭ 0.014).

same areas during endogenous attentional orienting, as tested in a visual search paradigm (Buschman andMiller, 2007, 2009). Similar to the current data, such evidence in the field of attentional orienting supports the hypothesis that characteristic synchronization patterns emerging from the FEF may underlie different cognitive processes, leading to different behavioral outcomes. Finally, our data show that perceptual sensitivity in healthy participants can be episodically enhanced by an extrinsic source of neural synchronization tuned to a specific frequency and support future uses of non-invasive neurostimulation to probe and manipulate oscillatory phenomena in the human brain from circumscribed cortical regions. Furthermore, pathological conditions involving specific alterations of cerebral oscillations [START_REF] Thut | The functional importance of rhythmic activity in the brain[END_REF] associated with impaired cognitive performance could potentially benefit from the use of frequency-tailored neurostimulation to locally manipulate activity and restore the characteristic oscillation frequencies of the healthy system.

Introduction

Research developed in the last decades is unveiling an intriguing link between brain activity, temporal dynamics and human cognition. Specific frequency bands on particular brain regions and their associated networks have been correlated to distinct cognitive processes. Interestingly however, differences or similarities in the modulation of behavioral correlates by characteristic spatiotemporal patterns in homotopic brain areas of opposite hemispheres have rarely been addressed.

In the current study, we focused on the frontal eye field (FEF), a cerebral region contributing in humans to the planning of saccadic activity, visuo-spatial attention, conscious access and decision-making (Chanes et al. 2012;Corbetta et al. 2008;Libedinsky and Livingstone 2011;Moore and Armstrong 2003;Paus 1996).

The right and left FEF are part of a bilaterally distributed dorsal attentional orienting network (Shulman et al. 2010;Thiebaut de Schotten et al. 2011). Prior research has provided strong evidence of right hemisphere dominance for visuo-spatial attentional processing (Bartolomeo et al. 2008;Grosbras and Paus 2002). Similarly, interhemispheric differences have been reported with regards to prefrontal contributions to visual consciousness (Del Cul et al. 2009;Rastelli et al.) or decisionmaking [START_REF] Heekeren | Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality[END_REF], suggesting a relevant role for the left hemisphere in these processes.

Here, we explored the impact of high-beta frequency-specific as compared to rhythmic and random arrhythmic non-frequency-specific activity, delivered to the left FEF in short bursts of transcranial magnetic stimulation (TMS). The effects observed are compared to those formerly recorded in its right homotopic region, which consisted in increases of perceptual sensitivity on a conscious visual detection task with high-beta frequency-specific (but not rhythmic non-frequency-specific) activity patterns (Chanes et al. 2013), likely to reflect attentional orienting-related processes (Buschman and Miller 2007;[START_REF] Phillips | Greater frontal-parietal synchrony at low gamma-band frequencies for inefficient than efficient visual search in human EEG[END_REF].

Similar results on the left FEF would indicate similar coding strategies of the left and right frontal cortex to modulate perceptual performance. In contrast, the observation of facilitatory effects by non-frequency-specific activity would provide causal evidence in support of a different coding strategy for perceptual modulation. In particular, performance enhancements by random arrhythmic patterns would reflect stochastic facilitation and support a behaviorally relevant role for noise activity in neural coding [START_REF] Mcdonnell | The benefits of noise in neural systems: bridging theory and experiment[END_REF][START_REF] Medina | Stochastic facilitation of artificial tactile sensation in primates[END_REF].

Materials and methods

Participants and consent

Twelve participants (7 women and 5 men) [START_REF] Posner | Attention and the detection of signals[END_REF]. The apparatus, visual stimuli and TMS stimulation procedure employed here on the left FEF were identical to those reported for the right FEF in a recent publication (Chanes et al. 2013). fixation point). Then, the fixation cross became slightly larger (0.7x0.7 o , 67 ms) to alert participants of an upcoming event. After an inter-stimulus interval (233 ms), a target could appear at the center of one of the two lateral placeholders for a brief period of time (33 ms). The target consisted of a low-contrast Gabor stimulus ( 2cycles/degree spatial frequency, 3.0 o diameter, minimum and maximum Michelson contrast of 0.037 and 0.283, respectively) with its lines tilted 1° to 10° clockwise or counterclockwise from the vertical orientation. The inter-trial interval lasted at least 4 seconds.

Participants were asked to execute two tasks. First, they had to determine the orientation of the Gabor lines (discrimination task) by pressing the corresponding button on a computer keyboard ("1" for counterclockwise and "2" for clockwise) with the index and middle fingers of their right hand. Participants were forced to guess a response even when the target was not presented or they did not consciously perceive it and accuracy was collected as the outcome measure. Secondly, they had to report whether they had consciously perceived the Gabor or not (conscious detection task). To do so, two arrow-like stimuli (">>>" and "<<<") pointing to the left and to the right were simultaneously presented below and above the fixation cross.

Participants were provided with 3 keys, which they had to operate with their left hand: an upper key "d", a lower key "c" and the space bar. The upper and lower keys were associated to the arrow presented on the top and the bottom, respectively.

Participants had to respond by pressing the space bar if they did not see the stimulus, or the given key ("d" or "c") to select the upper or lower arrow pointing to the placeholder (right/left) in which they perceived the target. The location of each arrow, above or below the fixation point, was randomized across trials. This task was assessed through perceptual sensitivity (d') and response criterion (beta), two measures used in Signal Detection Theory. Perceptual sensitivity is a bias-free measure that informs on the participants' ability to detect weak signals in situations that might be strongly influenced by belief whereas response criterion describes the relative preference of participants for one response over the alternative one, independently on signal strength. To compute these measures, trials in which the location of a target was correctly determined by participants were considered as correct detections or "hits" and trials in which participants reported a location for targets that were not presented were considered "false alarms". Trials in which the location of a present target was incorrectly reported were counted as "errors". Eye movements were monitored for fixation control purposes. Fixation was considered broken when participants' eyes position was recorded outside the central placeholder (i.e. 3 o away from the fixation cross horizontally and 2.75 o vertically).

A titration procedure performed prior to the onset of the experiment, allowed us to determine for each participant the stimulus contrast at which ~50% of the displayed targets were consciously reported. The degree of line tilting was also adjusted individually to maintain discrimination performance at ~75% of correctly reported targets. Such titration levels ensured that, in both tasks, performance was halfway between the worse (0% in the detection task and 50% in the forced-choice discrimination task) and the best possible performance (100% in both tasks).

Participants started the titration trials with a high contrast stimulus and, every 20 trials, target contrast and line tilting were adjusted in order to converge to the preestablished criteria. The experiment started once performance levels reached those criteria. Throughout the experiment, stimulus parameters were automatically adjusted every 20 trials (in steps of 0.07 Michelson contrast and 1° of tilting) to maintain these titration levels.

Each participant performed two experimental blocks, each of them consisting of 200 trials, including 40 trials in which the target was absent. In half of the trials, a short burst of 4 TMS pulses was delivered on the left FEF (active TMS trials). In the other half (sham TMS trials), the same short burst was applied by a second TMS coil placed next to the stimulation site, with the coil's surface perpendicular to the scalp, preventing the magnetic field from reaching the skull and stimulating the brain. The order of active and sham TMS bursts was randomized across trials in sub-blocks of 20 trials. Participants were allowed to take a short break and received feedback on the screen about performance and eye movement rates every 40 trials.

In the first experiment, each participant performed two blocks of trials the order of which was counterbalanced across subjects. In the frequency-specific 30 Hz block, the 4 TMS pulses were uniformly delivered across a 102 ms time interval, whereas, in the rhythmic non-frequency-specific block, these were unequally distributed over the same time window (the first and last pulses occurred at identical timing as in the frequency-specific block, whereas the second and third pulses were slightly anticipated and delayed respectively, Chapter IV -Figure 1b). After the titration and before the two experimental blocks, participants performed a few short blocks of familiarization in which they performed the task with the stimulation.

The second experiment consisted in a single block, in which random arrhythmic patterns of TMS were employed. In those patterns, the temporal distribution of the TMS pulses changed across trials, so that the first and last of the 4 pulses were delivered at the same timing as in the first experiment, whereas the second and third pulses were delivered randomly during the interval left by the former two, but subjected to the following constraints: (i) the 2 middle pulses were delivered at least 19 ms apart, in order to ensure consistent TMS capacitor recharge time; and (ii) the 4 pulses of the burst could not be equally distributed across the burst interval, in order to avoid occurrence of the frequency-specific pattern employed in Experiment 1.

Transcranial Magnetic Stimulation (TMS)

TMS pulses were delivered using a biphasic repetitive stimulator (SuperRapid) with a 70 mm diameter figure-of-eight coil. A structural T1-weighted MRI scan (3T Siemens MPRAGE, flip angle=9, TR=2300 ms, TE=4.18 ms, slice thickness=1mm) was acquired for every participant at the CENIR MRI center (Salpêtrière Hospital, Paris).

The left FEF region was localized on each individual MRI using averaged Talairach coordinates x=31, y=-2, z=47 (Paus 1996) and a 0.5 cm radius spherical Region of Interest (ROI). The final MRI was uploaded into a frameless stereotaxic system and reconstructed in 3D for its use in an online stereotaxic TMS neuronavigation system (Eximia, Nextim) (Chapter IV -Figure 1c). At all times, the TMS coil was held tangentially to the skull, with its handle oriented ~45º in a rostral-to-caudal and lateral-to-medial orientation, i.e. ~parallel to the central sulcus. Coil position was tracked online throughout the experiments and kept steady within an area of ~1-2 mm radius from the targeted site. The representation of the right and left primary motor cortices (M1) of the abductor pollicis brevis (APB) muscle were localized and the motor thresholds at these sites were determined as the TMS intensity yielding thumb twitching responses in ~50% of the attempts. In the first experiment, stimulation intensity was set up at 45% of the TMS machine maximal output. Nonetheless, in one participant such level had to be slightly decreased to abolish temporal involuntary facial muscle activation, blinks and other types of facial sensations induced by field spread. The average intensity at which participants were stimulated was 44.8% (SD: 0.6%), which corresponded to 70% of the mean individual motor threshold. In the second experiment, stimulation intensity was also set up at 45% of the TMS machine maximal output, a value that corresponded to 71% of the mean individual motor threshold.

Data analyses

In the first experiment, trials in which participants broke fixation (710%) were eliminated from the analyses. Moreover, trials in which participants reported to have seen the target but incorrectly determined its location (errors, 33% of reported targets) were also excluded. In the detection task, the mean perceptual sensitivity was 1.8 (SD: 0.2) and the mean response criterion was 5.0 (SD: 0.7). In the discrimination task, one participant was excluded from the analyses because he did not perform the task correctly (his mean accuracy along the experiment was under chance levels). Participants' general accuracy (meanSD) was 636%. It was high when they reported to have seen the target (748%), and it remained at chance levels when they reported not to have seen it (516%). Only correctly detected target trials were considered for accuracy analyses. Each outcome measure (perceptual sensitivity and response criterion for the conscious detection task and accuracy for the discrimination task) was subjected to a 2x2x2 repeated measures ANOVA with block (frequency-and rhythmic non-frequency-specific), target location (left and right visual fields) and TMS condition (active and sham) as within-participant factors. The same ANOVA was performed on the excluded error trials. This analysis did not yield any significant main effects or interactions indicating that the percentage of errors was similarly distributed across conditions.

Similarly, trials of the second experiment in which participants broke fixation (710%) or incorrectly determined the location of seen targets (errors, 55%) were eliminated. In the detection task, mean perceptual sensitivity was 1.8 (SD: 0.3) and the mean response criterion was 4.8 (SD: 0.8). Participants' general accuracy in the discrimination task (meanSD) was 634%. Again, it was high when subjects reported to have seen the target (767%), and it remained at chance levels when they reported not to have seen it (505%). Each outcome measure was subjected to a 2x2 repeated measures ANOVA with target location (left and right visual fields) and TMS condition (active and sham) as within-participant factors. The same ANOVA was performed on the eliminated error trials. A main effect of TMS (F [START_REF] Posner | Attention and the detection of signals[END_REF][START_REF] Latto | Visual field defects after frontal eye-field lesions in monkeys[END_REF]=6.667, p=0.026) was observed, indicating that participants made more errors under the impact of active than sham TMS bursts.

Results

Our first experiment revealed pattern-specific modulatory effects of frontal activity on visual performance (Chapter IV -Figure 2). In the conscious detection task, an interaction between block and TMS condition reached statistical significance for perceptual sensitivity (repeated measures ANOVA with block, target location and TMS condition as within-participant factors, F [START_REF] Posner | Attention and the detection of signals[END_REF][START_REF] Latto | Visual field defects after frontal eye-field lesions in monkeys[END_REF]=5.34, p=0.041), indicating different effects of TMS in each of the stimulation blocks. Whereas no differences were observed between active and sham stimulation for frequency-specific 30-Hz bursts (planned comparison active vs. sham TMS: F<1), perceptual sensitivity proved higher for active than sham TMS in the rhythmic non-frequency-specific block (planned comparison active vs. sham TMS: F=5.95, p=0.033). Given that, as previously reported for this region, perceptual effects could differ across the visual field and be rather contralateral with respect to the stimulation site (Grosbras and Paus 2002), we performed additional separate planned comparisons for left and right targets of the rhythmic non-frequency-specific block. Significant effects were observed for contralateral (planned comparison active vs. sham TMS for the right visual field: F=11.78, p=0.006) but not ipsilateral (planned comparison active vs. sham TMS for the left visual field: F<1) targets. In addition to the significant interaction block x TMS condition, a main effect of target location reached significance (F [START_REF] Posner | Attention and the detection of signals[END_REF][START_REF] Latto | Visual field defects after frontal eye-field lesions in monkeys[END_REF]=23.16, p=0.001), indicating that participants performed better for right than left targets. No significant main effects or interactions were observed for response criterion in the detection task or for accuracy in the discrimination task.

In order to confirm that the effect of rhythmic non-frequency-specific bursts on conscious visual detection resulted from the lack of frequency-specificity of the TMS patterns employed and not from the specific temporal features embedded in it, we performed a second experiment in which random arrhythmic non-frequency-specific bursts (i.e. noise activity) were employed. In the conscious detection task, the interaction between target location and TMS condition reached statistical significance for perceptual sensitivity (repeated measures ANOVA with target location and TMS condition as within-participant factors, F [START_REF] Posner | Attention and the detection of signals[END_REF][START_REF] Latto | Visual field defects after frontal eye-field lesions in monkeys[END_REF]=16.96, p=0.002), indicating that active random arrhythmic patterns improved performance as compared to sham for targets contralateral (planned comparison active vs. sham TMS for the right visual field: F=8.91, p=0.012) but not ipsilateral (planned comparison active vs. sham TMS for the left visual field: F=2.35, p=0.154) to the TMS coil location. In addition, a main effect of target location reached significance (F(1,11)=8.39, p=0.015), indicating better performance for right than left targets. As in the first experiment, no significant main effects or interactions were observed for response criterion (beta) in the detection task or for accuracy in the discrimination task. The equivalence of the results for the rhythmic non-frequency-specific block employed in the first experiment and the random arrhythmic non-frequency-specific block tested in the second experiment was further tested by performing a 2x2 repeated measures ANOVA with TMS condition (active or sham) and target location (right or left fields) as within-participant factors and group (rhythmic and random arrhythmic non-frequency-specific) as between-participant factor. As expected, an interaction between target location and TMS condition was observed, indicating an effect of TMS only for contralateral (right) but not ipsilateral (left) visual targets. No interactions with group were observed, indicating that both rhythmic and random arrhythmic non-frequency-specific patterns yielded similar results.

along the right fronto-parietal dorsal attentional orienting network (Buschman and Miller 2007;[START_REF] Phillips | Greater frontal-parietal synchrony at low gamma-band frequencies for inefficient than efficient visual search in human EEG[END_REF]. As for the right FEF (Chanes et al. 2013), left FEF effects on conscious visual detection may have emerged from the modulation of visuo-spatial processes. For example, attentional synchronization along the right dorsal fronto-patietal network could have benefited through transcallosal interactions from the induction of noise to the left hemisphere. Another possibility is that the perceptual effects observed did not resulted from attentional processes but rather from the modulation of other (non-attentional) cognitive processes also relevant for visual performance, such as or decision-making or access to visual consciousness to which the left FEF is known to contribute to (Del Cul et al. 2009;[START_REF] Heekeren | Involvement of human left dorsolateral prefrontal cortex in perceptual decision making is independent of response modality[END_REF][START_REF] Rastelli | Neural dynamics of neglected targets in patients with right hemisphere damage[END_REF]. For example, target omission in visuo-spatial neglect patients has been associated with left prefrontal beta-band synchronization [START_REF] Rastelli | Neural dynamics of neglected targets in patients with right hemisphere damage[END_REF], and, in line with this observation, the induction of noise activity to the left frontal cortex could have prevented the build-up of such rhythms and facilitated detection. Whether this injection of noise is enhancing or suppressing ongoing neural activity in one or more frequency bands remains elusive in the absence of accompanying electrophysiological recordings. A challenging combination of frequency-specific and non-frequency-specific non-invasive stimulation patterns and online EEG recordings will be necessary to understand the specific mechanisms by which noise induction results into the observed facilitation of visual performance.

In sum, our study provides support for stochastic visual facilitation through the induction of non-frequency-specific activity to the left frontal cortex, and shows that right and left homotopic human frontal regions induce similar behavioral effects on conscious visual perception through different coding strategies (beta synchronization vs. stochastic facilitation). Future research will explore the functional links between right and left frontal contributions to perceptual performance and will further characterize the specific cognitive processes that these underlie, among those involving the FEF and their associated networks.

Introduction

Recent studies on the impact of frontal, parietal and occipital activity on perceptual performance have provided causal evidence that spatiotemporal activity patterns induced by transcranial magnetic stimulation (TMS) have the ability to modulate visual perception in a frequency-specific manner (Chanes et al. 2013;Romei et al. 2011;Romei et al. 2010).

Although prior research efforts have concentrated in the role of oscillation frequency, the phase is also known to be a key factor in neural activity function. In the domain of perception, studies have reported that the phase of ongoing alpha oscillations predicts visual detection [START_REF] Busch | The phase of ongoing EEG oscillations predicts visual perception[END_REF][START_REF] Jaegle | Direct Control of Visual Perception with Phase-specific Modulation of Posterior Parietal Cortex[END_REF]) and this same parameter has been proven to be causally linked to cortical excitability in occipital sites [START_REF] Dugue | The phase of ongoing oscillations mediates the causal relation between brain excitation and visual perception[END_REF]. Moreover, the phase of lower frequency oscillations, particularly in the theta-band, has been found to modulate the power of higher frequency oscillations, such as those in the gamma-band (Canolty et al. 2006;Jensen and Colgin 2007;Lakatos et al. 2008;Mormann et al. 2005). Recent work has also revealed that increases of alpha oscillatory activity on occipito-parietal locations by 5-pulse TMS bursts is enhanced mainly beyond the third pulse of each pattern and that such entrainment effects tend to dissipate extremely quickly, in a matter of a cycle and a half at those frequencies (Thut et al. 2011).

We here focused on the frontal eye field (FEF), a cerebral region contributing in humans to the planning of saccadic activity, visuo-spatial attention, conscious access and decision-making (Chanes et al. 2012;Corbetta et al. 2008;Libedinsky and Livingstone 2011;Moore and Armstrong 2003;Paus 1996). The right and left FEF are part of a bilaterally distributed dorsal attentional orienting network (Shulman et al. 2010;Thiebaut de Schotten et al. 2011) although there is strong evidence of right hemisphere dominance for visuo-spatial processing (Bartolomeo et al. 2008;Grosbras and Paus 2002). In a prior study, we showed that right FEF high-beta and gamma activity had a different impact on visual performance. In particular, high-beta but not gamma activity proved able to selectively enhance perceptual sensitivity in a conscious detection task leaving response criterion unchanged, whereas gamma activity shifted response criterion without changes in perceptual sensitivity (Chanes et al. 2013).

Using an identical behavioral paradigm and high-beta 4-pulse TMS bursts, we explored whether the phase of the induced right frontal pattern with regards to target onset was able to modulate the effect of such in the conscious detection of a lowcontrast near-threshold stimulus. In accordance with prior literature in other frequency bands [START_REF] Busch | The phase of ongoing EEG oscillations predicts visual perception[END_REF][START_REF] Dugue | The phase of ongoing oscillations mediates the causal relation between brain excitation and visual perception[END_REF][START_REF] Jaegle | Direct Control of Visual Perception with Phase-specific Modulation of Posterior Parietal Cortex[END_REF], we hypothesized that the phase of TMS-induced high-beta activity pattern would influence its ability to impact perceptual outcomes, and such phase-dependent visual performance facilitatory effects would become weaker as the last pulse of the stimulation patterns is moved temporally further from target onset.

Materials and methods

Fourteen right-handed participants (10 women and 4 men) aged between 18 and 25 years old (212) took part in the study. All of them were naïve as to TMS and to the purpose of the experiment. They all participated voluntarily and reported no history of neurological or psychiatric disorders and normal or corrected-to-normal vision. The protocol was reviewed by the Inserm (Institut National de la Santé et la Recherche Scientifique) ethical committee and approved by an Institutional Review Board (CPP Ile de France 1).

Apparatus, stimuli and procedure

Visual stimuli were displayed on a screen (22") using MATLAB and the Psychtoolbox running in a personal computer (HP Z800). Each trial started with a grey resting screen (luminance: 31 cd/m2, 2500 ms), followed by a fixation screen (randomly lasting between 1000 and 1500 ms) (Chapter V -Figure 1a). The fixation cross (0.5x0.5 o ) was displayed in the center, along with two laterally located rectangular placeholders (6.0 o x5.5 o ) centered 8.5 o to the left and to the right of the fixation point.

Then, the fixation cross became slightly larger (0.7x0.7 o , 67 ms) to alert participants of an upcoming event. After an inter-stimulus interval (233 ms), a target appeared or not at the center of one of the placeholders for a brief period of time (33 ms). The target consisted of a low-contrast Gabor stimulus (0.5 degree/cycle sinusoidal spatial frequency, 0.6 degree exponential standard deviation, minimum and maximum Michelson contrast of 0.005 and 1, respectively) with its lines tilted 1° to 10° clockwise or counterclockwise from the vertical orientation. The inter-trial interval lasted at least 4 seconds.

Participants were asked to execute two tasks. First, they had to determine the orientation of the Gabor lines (discrimination task) by pressing the corresponding button on a computer keyboard ("1" for counterclockwise and "2" for clockwise) with the index and middle fingers of their right hand. They were forced to guess a response even when the target was not presented or they did not consciously perceive it and accuracy was collected as the outcome measure. Secondly, they had to report whether they consciously perceived the target or not and, if they did, where (conscious detection task). To do so, two arrow-like signs (">>>" and "<<<") pointing to the left and to the right were simultaneously presented below and above the fixation cross. Participants were provided with 3 keys, which they had to operate with (active TMS trials). In the other half (sham TMS trials), the same short burst was delivered by a second TMS coil placed next to the real stimulation site, with the coil's surface perpendicular to the scalp, preventing the magnetic field from reaching the skull and stimulating the brain. The order of active and sham TMS bursts was randomized across trials. At the end of each familiarization block, if performance in sham TMS trials fell outside titration levels, stimulus parameters were adjusted in steps of 0.05 Michelson contrast and 1° of tilting. Experimental trials started only once steady levels of performance in both visual tasks, discrimination and conscious detection, were reached. Nonetheless, throughout the experiment, stimulus parameters were also automatically adjusted in steps of 0.005 Michelson contrast and 1° of tilting every 20 trials according to performance levels in sham TMS trials to maintain titration levels (45-55% for the detection task and 65-85% for the discrimination task). As in the familiarization blocks, within each of these 20 trials sub-blocks, half of the trials used active TMS and the other half sham stimulation.

Participants were allowed to take a short break and received feedback on the screen about their percentage of "false alarms" and "errors".

We explored the effects of frequency-specific high-beta (30 Hz) activity patterns on visual performance. All participants performed 5 experimental blocks, corresponding to the 5 phases/conditions tested, the order of which was randomized across subjects (Chapter V -Figure 1b). Each one consisted of 140 trials (including 28 trials in which the target was absent) and corresponded to a specific phase determined by the time elapsed between the last pulse of the TMS burst and the target onset. The shortest phase corresponded to π, i.e. half a cycle (time lag of ~17 ms between the last pulse and target onset), whereas the longest one was 3π, i.e. one and a half cycle (time lag of 50 ms). In between those values, we tested phases hemisphere's motor resting threshold. This was measured on the cortical hotspot of the abductor pollicis brevis (APB) muscle in the right primary motor cortex (M1) and defined as the TMS intensity yielding thumb responses in ~50% of the attempts.

Stimulation intensity was set up at a fixed value of 45% of the TMS machine maximal output as used previously (Chanes et al. 2013) and we verified that no observable temporal involuntary facial muscle activation, blinks and other types of facial sensations were induced by field spread. The intensity used corresponded to 72% of the mean individual motor thresholds.

Data analyses

Each outcome measure (perceptual sensitivity and response criterion for the conscious detection task and accuracy for the discrimination task) was subjected to a 5x2x2 repeated measures ANOVA with block (each of the 5 phases, i.e. π, 3π/2, 2π, 5π/2, 3π), target location (left and right visual field) and TMS condition (active and sham) as within-participant factors. The same analysis was performed for errors (3% of reported targets) and did not yield any significant main effects or interactions, indicating that percentage of errors was similar across conditions.

Results

In the detection task, the mean perceptual sensitivity (d') was 1.8 (SD: 0.3).

The repeated measures ANOVA revealed a main effect of TMS (F(1,13)=13.56, p=0.003), with higher perceptual sensitivity levels for active than sham stimulation.

With the a priori hypothesis that some burst-to-target phases should work better than others, we tested the comparison between active and sham TMS for each specific block/phase. The TMS effect reached significance for all phases except for the one in which the last pulse of the burst was most distanced from target onset (i.e., 50 ms, 3π) (π: F=8.47, p=0.012; 3π/2: F=7.66, p=0.01; 2π: F=5.72, p=0.033; 5π/2: F=12.81, p=0.003; 3π: F=1.60, p=0.228), indicating that enhancement of perceptual sensitivity by high-beta frontal activity is phase independent and rather limited to a time window close to target onset (Chapter V -Figure 2). Asterisks (*) indicate statistically significant differences (p<0.05) between active and sham TMS and for 0.05<p<0.10 the exact value is indicated. High-beta (30-Hz TMS) stimulation improved perceptual sensitivity in a phase-independent manner, particularly when the burst was delivered closer to the target.

The mean response criterion (beta) accounted by the group was 4.1 (SD: 0.9).

A repeated measures ANOVA revealed a main effect of TMS (F(1,13)=8. 35, p=0.013) on this parameter, with less conservative response criterion for active than sham TMS bursts. Again, with the a priori hypothesis that the effects could differ across phases, we tested the comparison between active and sham TMS for each phase/block. Interestingly, the TMS effect was either not far from significance or significant for alternate phases: π (F=3.49, p=0.084), 2π (F=5.78, p=0.032) and 3π (F=7.07, p=0.020) as opposed to phase 3π/2 (F=1.07, p=0.321) and 5π/2 (F<1), for which criterion was not modulated (Chapter V -Figure 3). In the discrimination task, participants' general accuracy was 647% (meanSD). As expected, it was high when they reported to have seen the target (7510%), and it remained at chance levels when they reported not to have seen it (506%). When only responses to correctly-detected target were considered for accuracy analyses, no main effects or interactions were observed, indicating that conscious discrimination was not significantly affected by high-beta right FEF patterns at any of the phases tested in the study. However, when discrimination responses were independently considered from conscious access (both correctly detected and undetected trials were taken into account), a main effect of TMS was observed (F(1,13)=32.42, p<0.001), indicating that participants discriminated more accurately with active than sham TMS patterns. When the same output measure was tested separately for the different phases, the comparison between active and sham TMS reached significance only for phase 5π/2 (F=18.67, p<0.001), whereas phase 3π/2 and 2π were marginally significant (F=3.99, p=0.067 and F=4.66, p=0.050, respectively) and the earliest and latest phases (π and 3π) did not yield significant effects (F=3. 30, p=0.092 and F=2.37, p=0.148, respectively) (Chapter V -Figure 4).

Discussion

We here tested the effect of the phase, defined by the time elapsed between the last pulse of a high-beta pattern and target onset, on previously proven facilitatory effects on conscious visual detection. Our results are in agreement with prior findings (Chanes et al. 2013) showing a causal relation between high-beta activity induced in the right FEF and improvements of perceptual sensitivity (d') during the conscious detection of near-threshold targets. Most importantly, they also demonstrate that such high-beta (30 Hz) frontal modulation, which had been previously probed for TMS bursts delivered 1/2 of an oscillation cycle (i.e. ~17 ms, π phase) prior to target onset, appears to be phase-independent, and thus that similar effects can be induced when the last pulse of the rhythmic TMS pattern is delivered at up to 5/4 of a cycle (i.e., ~42 ms, 5π/2) from target onset, but not at the longest interval used here (i.e. 50 ms, 3π). As shown elsewhere for occipito-parietal alpha induced oscillations, these data prove that the behavioral effects associated to right frontal high-beta spatiotemporal patterns operate on a very restricted time scale, and exert an influence during the stimulation pattern and only shortly beyond their duration (Thut et al. 2011).

Our data also show that response criterion (beta), a signal detection measure characterizing the relative preference for one response over the other one independently on signal strength, appears to be modulated by the phase of high-beta frontal activity with regards to target onset. In particular, only oscillation patterns that were in phase or in opposition of phase (i.e. 2π, 3π), but not at intermediate phases (i.e. 3π/2 and 5π/2), and rather further away from the target (the results for π did not reach statistical significance) proved able to induce significant shifts of response criterion. In line with these observations, in a previous study (Chanes et al. 2013), no significant effect on participants' response criterion was observed for high-beta patterns, identical to the ones used here with a π phase. On the contrary, in that same study, 50 Hz bursts significantly shifted response criterion (they made it less strict), demonstrating a causal role of gamma activity in the modulation of criterion.

Taken together, these results suggest that a possible explanation for the high-beta effects on response criterion could be through cross-frequency interactions (Jensen and Colgin 2007). Indeed, the phase of lower frequency oscillations, particularly in the theta-band, has been found to modulate the power of higher frequency oscillations, such as those in the gamma-band (Canolty et al. 2006;Jensen and Colgin 2007;Lakatos et al. 2008;Mormann et al. 2005). We hypothesize that, through a similar mechanism, the phase of our high-beta oscillations could have modulated higher-frequency neural activity and yielded effects associated with gamma band. If this hypothesis happened to be true, the influence of high-beta over gamma frequency patterns could take a bit of time to set up and so the effect would difficultly reach significance if high-beta activity is induced too close to target onset (e.g. in the case of phase π in this study). A demonstration of this hypothesis would require concurrent electrophysiological recordings, for example a TMS-EEG design, so that the time-frequency signature of high-beta patterns on the stimulated region during and immediately following the end of the pulses can be assessed. Successful EEG recordings showing oscillation entrainment have been achieved for alpha-band stimulation patterns (Thut et al. 2011) but those remain technically complex and extremely challenging at higher frequencies of stimulation such as those in the beta or the gamma band, with very limited periods of inter-pulse artifact-free data to analyze.

Our study also revealed unexpected improvements of visual discrimination accuracy, which TMS either in single pulses or identical high-beta patterns with a π phase failed to impact in prior studies when applied to this very same right cortical area (Chanes et al. 2012;Chanes et al. 2013). Such effect only arose when all trials, and not only those in which the target was correctly consciously detected, were considered in the analyses. This indicates that high-beta frontal activity patterns can affect performance at a perceptual level further than specifically impacting conscious access. Very interestingly, this effect did not reach significance for the earliest nor the latest tested phase value (π and 3π, i.e. ~17 and 50 ms), separated by one oscillation cycle. This observation could reflect either that such effect on visual discrimination is phase-independent but only arises if right-FEF activity is engaged within a short critical time window, or that it is phase-dependent and fails to reach significance when high-beta activity is induced in phase opposition to target onset (π and 3π). Further studies will be needed to understand in detail the mechanisms underlying such phenomenon.

In sum, our study provides causal evidence for a timing-dependent impact of high-beta right frontal oscillatory activity on visual discrimination, which could be assayed in novel TMS-based visual rehabilitation applications to boost their efficiency. These same patterns also proved able to induce phase-independent improvements of conscious visual detection reflected by perceptual sensitivity enhancements, and response criterion shifts that appeared to depend on the pattern phase with regards to target onset. Future research with concurrent TMS-EEG recordings will contribute to elucidate if the phase-dependent effects on criterion reported here are genuine or yielded through cross-frequency interactions between high-beta and higher (gamma) activity patterns at right frontal brain locations.

Introduction

Neuronal synchronization within and between regions of the same network is considered an essential mechanism underlying cognition [START_REF] Fries | A mechanism for cognitive dynamics: neuronal communication through neuronal coherence[END_REF]. In particular, local and long-range cerebral oscillations within and between frontal and parietal regions have been shown to underlie different processes relevant for perception such as attention [START_REF] Womelsdorf | The role of neuronal synchronization in selective attention[END_REF], decision-making (Siegel et al. 2011) and conscious access (Dehaene and Changeux 2011).

The intraparietal sulcus (IPS), particularly in the right hemisphere known to be dominant for visuo-spatial processes, is a key node involved in these processes. It is connected to the frontal eye field (FEF) through the first branch of the superior longitudinal fasciculus (SLF I) (Thiebaut de Schotten et al. 2011). Neuronal synchronization in the monkey's FEF and the lateral intraparietal (LIP) area has been shown to be increased in the high-beta band (around 30 Hz) during top-down attentional orienting and in the gamma band (around 50 Hz) during bottom-up attentional orienting, providing evidence on how top-down and bottom-up processes could be coded along the same fronto-parietal network based on oscillation frequency (Buschman and Miller 2007). In a previous study, we showed that activity noninvasively induced in the human right FEF at these two frequencies modulated different behavioral outcomes (Chanes et al. 2013). More specifically, high-beta ( 30Hz) activity selectively enhanced perceptual sensitivity in a conscious detection task without any changes in response criterion, while gamma (50 Hz) activity selectively shifted response criterion in the same task without any modulation of perceptual sensitivity. Based on these studies, we hypothesized that frequency-specific patterns in the right IPS would yield similar perceptual modulations.

Using short bursts of transcranial magnetic stimulation (TMS), we tested the impact of right parietal high-beta and gamma activity on visual performance.

Selective effects on perceptual sensitivity and response criterion by these two frequency bands respectively would provide causal evidence that perceptual modulations of conscious perception can be yielded from different nodes of the same network, while different results would emphasize the importance of the level of the network at which activity is induced. Furthermore, we hypothesized that the close direct reciprocal interaction of the posterior parietal cortex with occipital regions could result in further effects on perception, including non-frequency-coded modulations of conscious detection or other perceptual processes such as forced-choice orientation discrimination. The comparison between frequency-and non-frequency-specific patterns (equally long and with equal intensity and number of pulses) allowed us to isolate the effects of frequency from those linked to the amount of activity induced in a given time window.

Materials and methods

Two groups of 14 right-handed participants (7 women and 7 men in both groups) aged between 21 and 31 years old (243) took part in the experiments.

Thirteen of them were naïve as to TMS and to the purpose of the experiment. They all participated voluntarily and reported no history of neurological or psychiatric disorders and normal or corrected-to-normal vision. The protocol was reviewed by the Inserm (Institut National de la Santé et la Recherche Scientifique) ethical committee and approved by an Institutional Review Board (CPP Ile de France 1).

Apparatus, stimuli and procedure associated to the arrow presented on the top and the bottom, respectively.

Participants had to respond by pressing the space bar if they did not see the stimulus, or the given key ("d" or "c") to select the upper or lower arrow pointing to the placeholder (right/left) in which they perceived the target. The location of each arrow, above or below the fixation point, was randomized across trials. This task was assessed through perceptual sensitivity (d') and response criterion (beta), two measures used in Signal Detection Theory. Perceptual sensitivity is a bias-free measure that informs on the participants' ability to detect weak signals in situations that might be strongly influenced by belief. Response criterion describes the relative preference of participants for one response over the alternative one, independently on signal strength. To compute these measures, trials in which the location of the target was correctly determined by participants, were considered as correct detections or "hits" and trials in which participants reported a location for targets that were not presented were considered "false alarms". Trials in which the location of a present target was incorrectly reported were counted as "errors". Eye movements were monitored for fixation control purposes (Tobii TX300). Fixation was considered broken when participants' eyes position was recorded outside the central placeholder (i.e. 3 o away from the fixation cross horizontally and 2.75 o vertically).

A titration procedure performed prior to experimental trials allowed us to determine, for each participant, the stimulus contrast at which ~50% of the displayed targets were consciously reported. The degree of line tilting was also adjusted individually to maintain discrimination performance at ~75% of correctly reported targets. Such titration levels ensured that in both tasks, performance was halfway between the worse (0% in the detection task and 50% in the forced-choice discrimination task) and the best possible performance (100% for both tasks).

Participants started the titration trials with a high-contrast stimulus and, every 20 trials, target contrast and degree of line tilting were adjusted in order to converge to the pre-established criteria. The experiment started once performance levels reached titration criteria and throughout experimental trials stimulus parameters were automatically adjusted every 20 trials (in steps of 0.07 Michelson contrast and 1° of tilting, respectively) to maintain those levels.

Each experimental block consisted of 200 trials, including 40 trials in which the target was absent. In half of the trials, a short pattern of 4 TMS pulses was delivered on the right FEF (active TMS trials). In the other half (sham TMS trials), the same short pattern was applied by a second TMS coil placed next to the stimulation site, with the coil's surface perpendicular to the scalp, preventing the magnetic field from reaching the skull and stimulating the brain. The order of active and sham TMS patterns was randomized across trials in sub-blocks of 20 trials. Participants were allowed to take a short break and received feedback on the screen about performance and eye movement rates every 40 trials.

We explored the effects of two different stimulation frequencies, high-beta ( 30Hz) and gamma (50 Hz), on visual performance in two groups of participants. Each participant of each group performed two experimental blocks of trials the order of which was counterbalanced across subjects. In the frequency-specific block, the 4 TMS pulses were delivered uniformly in time, whereas, in the corresponding nonfrequency-specific block, the same 4 pulses were unequally distributed over the same time window (the first and last pulses occurred at identical timing as in the frequency-specific block, whereas the second and third pulses were slightly anticipated and delayed respectively, Chapter VI -Figure 1b). After the titration and before the two experimental blocks, participants performed a few short blocks of familiarization in which they performed the task with the stimulation.

Transcranial Magnetic Stimulation (TMS)

TMS pulses were delivered using a biphasic repetitive stimulator (SuperRapid) with a 70 mm diameter figure-of-eight coil. A structural T1-weighted MRI scan (3T Siemens MPRAGE, flip angle=9, TR=2300 ms, TE=4.18 ms, slice thickness=1mm) was acquired for every participant at the CENIR MRI center (Salpêtrière Hospital, Paris).

The right IPS region was localized on each individual MRI using averaged Talairach coordinates x=16, y=-63, z=47 (Kincade et al. 2005) and a 0.5 cm radius spherical Region of Interest (ROI). The marked MRI was uploaded into a frameless stereotaxic system and reconstructed in 3D for its use in an online stereotaxic TMS neuronavigation system (Brainsight, Rogue Instruments) (Chapter VI -Figure 1c). At all times, the TMS coil was held tangentially to the skull, with its handle oriented ~45º in a caudal-to-rostral and lateral-to-medial orientation, i.e. ~perpendicular to the central sulcus. Coil position was tracked online throughout the experiments and kept steady within an area of ~1-2 mm radius from the targeted site by means of the neuronavigation system. The representation of the right and left primary motor cortex (M1) of the abductor pollicis brevis (APB) muscle was localized and the motor thresholds at these sites were determined as the TMS intensity yielding thumb twitching responses in ~50% of the attempts.

Stimulation intensity was set up at 45% of the TMS machine maximal output, for which no involuntary facial muscle activation, blinks and other types of facial sensations induced by field spread were observed. This intensity corresponded to 74% of the mean individual motor threshold.

Data analyses

Trials in which participants broke fixation (25%) were eliminated from the analyses.

Eye movements could not be recorded in one participant because the eye-tracker failed to detect her gaze.

Each outcome measure (perceptual sensitivity and response criterion for the conscious detection task and accuracy for the discrimination task) for each frequency (30 and 50 Hz) was subjected to a 2x2x2 repeated measures ANOVA with block (frequency-and non-frequency-specific), target location (left and right visual field) and TMS condition (active and sham) as within-participant factors. The same ANOVA was performed to errors (1.4% and 1.6% of reported targets in the 30 and 50 Hz experiment respectively), which were excluded from the main analyses. An interaction between block and target location was observed for the 30-Hz experiment (F(1,13)=4.85, p=0.046), indicating that in the frequency-specific block participants made more errors for left than right targets, while there were no differences between left and right in the percentage of errors in the non-frequency-specific block. In the 50-Hz experiment, a main effect of TMS was observed indicating that participants made fewer errors with active than sham TMS (F(1,13)=5.94, p=0.03).

Results

For the detection task, participants' mean perceptual sensitivity (d') was 1.9 (SD: 0.2) and the mean response criterion was 6 (SD: 1). Participants' general accuracy (meanSD) in the discrimination task was 6311%. It was high when they reported to have seen the target (769%), whereas they remained at chance levels when they reported to have not seen it (501%).

In the conscious detection task, perceptual sensitivity was modulated by active 30 Hz but not its corresponding matched non-frequency-specific pattern (marginally significant interaction between block and TMS condition, F(1,13)=4.48, p=0.054, Chapter VI -Figure 2 top left). Perceptual sensitivity was higher for active than for sham 30-Hz TMS (planned comparison active vs. sham TMS for the frequencyspecific block: F=5.20, p=0.040), whereas no differences between active and sham were observed for the non-frequency specific pattern (planned comparison active vs.

sham TMS for the non-frequency-specific block: F<1). This result is analogous to previously reported effects in the right FEF (Chanes et al. 2013). The interaction between block, target location and TMS condition reached significance for response criterion (F(1,13)=5.59, p=0.034, Chapter VI -Figure 2 bottom left). However, planned comparisons between active and sham TMS for left and right targets in either block did not reach significance (frequency-specific block left targets: F=1.21, p=0.292; frequency-specific block right targets: F<1; non-frequency-specific block left targets: F<1; non-frequency-specific block right targets: F=2.38, p=0.147).

In the discrimination task, when all trials were included in the analysis (i.e. correctly-consciously-detected-target and undetected-target trials, thus perceptual forced-choice discrimination independently of conscious access), no main effects or interactions were observed (Chapter VI -Figure 3 top left). However, when only correctly-consciously-detected-target trials were considered in the analysis, the interaction between target location and TMS condition was marginally significant (F(1,13)=4.68, p=0.050, Chapter VI -Figure 3 bottom left), suggesting that TMS improved accuracy ipsilaterally (i.e. for right but not left targets), although planned comparisons between active and sham TMS did not yield significance (F=3.73, p=0.075 and F=2.54, p=0.135, for right and left respectively). and TMS condition was not significant (F(1,13)=2.74, p=0.122, Chapter VI -Figure 2 bottom right), we performed a planned comparison between active and sham TMS separately for each block on the basis that our right-FEF prior study had shown frequency-specific modulations of response criterion by 50-Hz activity (Chanes et al. 2013). These comparisons revealed that frequency-specific (F=14.79, p=0.002) but not non-frequency-specific (F<1) patterns reduced response criterion, which is analogous to the results reported for stimulation in the right FEF. In the discrimination task, when all trials were included in the analysis (i.e. correctly-consciously-detected-target and undetected-target trials, thus perceptual forced-choice discrimination independently on conscious access), a main effect of TMS was observed (F(1,13)=5.04, p=0.043, Chapter VI -Figure 3

Discussion

Our data demonstrate frequency-specific effects of right posterior parietal activity on visual performance analogous to those previously reported for the right frontal cortex (Chanes et al. 2013). Frequency-specific high-beta (30 Hz) patterns but not its matched non-frequency-specific pattern delivered to the right IPS enhanced participants' perceptual sensitivity. In contrast, 50 Hz activity in the same location but not its matched non-frequency-specific activity pattern relaxed participants' response criterion (i.e. participants became less conservative). These results show that the temporal dynamics characterizing patterns of right parietal neural activity are a key factor to explain and understand its contributions.

The right FEF and IPS are key sites of the dorsal attentional network (Corbetta et al. 2002;Corbetta et al. 2008). Prior evidence has shown that neuronal synchronization at high-beta and gamma frequencies in these two areas correlate respectively with the engagement of top-down and bottom-up attention (Buschman and Miller 2007). Furthermore, recent unpublished data from our laboratory show that the effects of frequency-specific activity in the right FEF correlate with the volume of the first branch of the superior longitudinal fasciculus (SLF I) linking these two areas, which together with the fact that the effects described here are in accordance with those yielded by top-down and bottom-up attention in prior work using similar tasks (Chica et al. 2011), suggest that TMS patterns at these frequencies in these two areas might reflect top-down vs. bottom-up attentional processes that impact differently our conscious detection task.

Interestingly, additional effects were observed in this study. Both 50 Hz and the matched non-frequency-specific pattern improved perceptual sensitivity in the conscious detection task and accuracy in the discrimination task. Given that the activation itself (i.e. either in a frequency-or non-frequency-specific pattern) was not sufficient to yield these effects when the pulses were distributed in a larger time window (30 Hz and matched non-frequency-specific block), this results suggest that a sufficiently powerful activation of the IPS, defined as the amount of activation (i.e. number of pulses) divided by the time period in which it is delivered (i.e. time between the first and last pulses), can yield perceptual improvements independently on frequency or at least in a broader band (including at least 50Hz and the frequencies embedded in the control pattern). The fact that the effect on accuracy was observed when all targets (correctly detected and undetected) were taken into account, suggests that a sufficiently powerful activation improves perception rather than selectively affecting conscious access. These perceptual improvements may result from the rich reciprocal connections of the IPS with visual areas, which may allow an important interaction between TMS and bottom-up activity. Indeed, differences in the influence of frontal vs. parietal TMS bursts to the visual cortex have been described. While parietal TMS effects in visual areas appear to depend on the current visual input, frontal TMS effects do not depend on the presence of visual stimuli but rather differ between central and peripheral locations [START_REF] Ruff | Distinct causal influences of parietal versus frontal areas on human visual cortex: evidence from concurrent TMS-fMRI[END_REF].

These differential effects, as the ones reported here, might reflect distinct functional signatures of these two regions.

In sum, our data extend to the right posterior parietal cortex the frequencyspecific effects on conscious visual performance reported in prior work for a right frontal area and provide evidence of perceptual enhancements by a sufficient amount of activation in a short time window, possibly thanks to the rich connection of the stimulated region to visual areas. Future studies using concurrent TMS-EEG will address the exact way in which the induced patterns interact with ongoing neural activity.

Chapter VII

Discussion

VII.I. Overview and summary of the main results

The general goal of this thesis was to study the role and temporal dynamics of fronto-parietal activity patterns on perceptual performance and explore the possibility to use such knowledge to design future interventions that could serve to induce visual performance enhancements in healthy participants or neurological patients. For this purpose, we used visual perception as a testing bench to investigate the influence of frontal and parietal areas in perceptual processes. Noninvasive neurostimulation allowed us to establish causal relationships between specific spatiotemporal neural activity patterns and behavior. This general goal was achieved through an investigation articulated in three main aims in which the general hypothesis was that fronto-parietal systems and their activity subtend and hence influence the modulation of perceptual performance and that the spatiotemporal dynamics of this activity plays a key role in such impact.

Our first aim was investigated in the experiments described in Chapter II. We explored the impact of pre-target activity in the right FEF (i.e. FEF activity before visual stimulus onset) on the discrimination and conscious detection of low-contrast near-threshold stimuli by delivering a single pulse of TMS prior to the presentation of the target, alone (either 80, 100 or 140 ms pre-target onset) or after the display of a peripheral predictive visuo-spatial cue likely engaging the fronto-parietal attentional network (80 ms pre-target onset) (Chanes et al. 2012). Our data show that pre-target FEF activity is relevant for conscious perception as its modulation by TMS impacted conscious detection performance. More specifically, perceptual sensitivity was improved by pre-target single-pulse TMS, particularly when the pulse was delivered closer to target onset. The results also show that neuronal activity within the right FEF is shaped by visuo-spatial attention, as the effect of single-pulse TMS on perception was modulated by the validity of the trial. Indeed, when delivered before the target onset but after the presentation of a cue, the TMS pulse improved perceptual sensitivity only when the cue correctly predicted the location of the target (valid trials). FEF neuronal subpopulations coding for attended locations (i.e., those relevant for valid trials) have been shown in the monkey to increase their activity and keep it increased during the cue-to-target period (Armstrong et al. 2009). In the case of our study, this implies that TMS would have preferentially affected the most active FEF populations and acted weakly on remaining units. It is through mechanisms of this sort that the neurostimulation of a large area can be shaped at the subpopulation level (Silvanto et al. 2008).

It is well established that brain cognitive functions are not coded simply on the basis of regional and network activations and deactivations but that the fine temporal dynamics of such processes and particularly local and interregional cerebral oscillations play an important role. Hence in our second aim (Chapters III, IV and VI) we addressed the impact of specific spatiotemporal activity patterns delivered to the right FEF, the left FEF and the right IPS on visual performance, and aimed to provide causal evidence of pattern-coded modulations of perceptual performance.

In a first study (Chapter III), we explored the effects of FEF short-burst TMS over the right FEF at specific frequencies (30 Hz and 50 Hz) in the discrimination and conscious detection of low-contrast near-threshold targets (Chanes et al. 2013). A double dissociation between stimulation frequency and modulated behavioral outcome was observed, with 30 Hz activity selectively improving perceptual sensitivity (d') with no changes in response criterion and 50 Hz selectively relaxing criterion (beta), without inducing modulations of perceptual sensitivity. We demonstrated that such effects were specifically linked to the frequency content of the stimulation patterns rather than to the total amount of activity yielded by the bursts, as no effects were observed when the temporal distribution of the patterns was slightly different, yet equivalent in number of pulses, duration, timing with regards to target onset and TMS intensity. This study provided causal evidence that different behavioral outcomes can be impacted from a single cortical area in a frequency-coded manner, what could reflect a general brain mechanism to multiplex functions within the same brain region and network. In a second study (Chapter IV), 30 Hz bursts and equivalent non-frequency-specific bursts were delivered this time to the left FEF and participants were requested to perform the same behavioral tasks. In this case, no effects linked to the 30 Hz frequency were observed, and instead perceptual sensitivity (d') was improved by non-frequency-specific patterns, matched in burst duration, TMS intensity and number of pulses. Together with the results in the right homotopic area, these data suggest hemisphere-specific coding strategies for the modulation of visual perception, with the right hemisphere modulating perceptual sensitivity through high-beta activity patterns, probably reflecting attentional orienting processes, and the left one modulating the same behavioral correlate through stochastic facilitation, subtending the same or a different cognitive process. Finally, the same design of Chapter III was performed on the right IPS (Chapter VI), which, together with the FEF, is an important component of the dorsal attention network and known to increase its synchrony at a beta and gamma frequencies during top-down and bottom-up attentional orienting processes, respectively, in non-human primates (Buschman and Miller 2007). Such investigation revealed effects of active 30 and 50 Hz TMS bursts on conscious perception tasks analogous to those induced by the same frequency-specific patterns delivered over the right FEF (i.e. enhancement of perceptual sensitivity and shift of response criterion, respectively). In addition, the more powerful activation (i.e. more pulses per unit of time) of the 50 Hz burst and its matched non-frequency-specific pattern proved able to enhance perceptual sensitivity, as well as accuracy in the discrimination task when all targets (consciously detected and undetected) were considered.

In addition to the frequency, the phase of cerebral oscillations has also been shown to be relevant for behavior and cognition. Our third aim, addressed in the study reported in Chapter V, assessed the impact of the phase of frequency-specific frontal oscillations with regards to target onset on their effect on visual performance.

A 30 Hz burst was delivered to the right FEF and participants were again requested to perform a discrimination and a conscious detection task involving low-contrast near-threshold targets. Across 5 separate blocks, 4-pulse TMS bursts were delivered at 5 different phases (i.e. different time lags between the last pulse of the pattern and target onset) within one oscillation period (last pulse delivered from ~17 to 50 ms pretarget onset), every quarter of a cycle (i.e. ~8 ms). The effects of 30 Hz activity on perceptual sensitivity observed in a prior study (Chapter III) appeared to be phaseindependent and not to reach significance if the last pulse was delivered too far from the onset of the target. Interestingly, an effect on response criterion appeared for alternate oscillation phases, particularly for longer time intervals between the last pulse and target onset. This effect on criterion, together with prior evidence that this parameter is selectively modulated by gamma oscillatory activity, could indicate cross-frequency interactions between 30 Hz and 50 Hz activity.

VII.II. Frontal and parietal contributions to perceptual performance through characteristic spatiotemporal activity patterns: Implications of our results for the current ideas on neural dynamics as a key factor for brain function and its noninvasive manipulation in humans

Taken together, the results presented here show that frontal and parietal areas, in particular the FEF and the IPS, modulate perceptual performance through specific neural activity patterns that underlie the cognitive processes in which they are involved (e.g. attention). Our results provide an extensive yet not exhaustive mapping of perceptual performance modulations to specific activity patterns in the human brain.

They contribute to the existing literature providing causal evidence in humans that the frequency of neural activity, isolated from the average amount of activation, plays a key role in its function and point out to similar observations for the phase. It has to be noted that some of our results also show that perceptual modulations can also be sensitive to the average amount of induced activity rather than its frequency, or at least arise from activity in a wide frequency band (see Chapter VI, perceptual sensitivity and accuracy effects under both 50 Hz and non-frequency specific patterns). In addition, our experiments served at assessing the malleability of the system and the possibility to manipulate neural coding through noninvasive stimulation methods with the ultimate goal to enhance perceptual performance in the near future, particularly in clinical brain damage conditions.

Evidence that visual performance can be modulated from different areas and through different temporal activity patterns makes our data compatible with models of large-scale highly-distributed networks of cognitive processes relevant for perception, such as attention and conscious access (e.g. (Corbetta et al. 2008;Corbetta and Shulman 2002;Dehaene et al. 1998)). Our experiments were not designed to address which cognitive process underlies a specific perceptual performance modulation but rather to explore the mechanisms (i.e. neural code) through which neural activity underlying those processes modulate visual perception.

More generally, brain oscillations have been shown in multiple domains of brain function to underlie many brain processes, including attention and access to consciousness but also memory (e.g. [START_REF] Duzel | Brain oscillations and memory[END_REF][START_REF] Klimesch | EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis[END_REF])) and language (e.g. [START_REF] Bastiaansen | Oscillatory neuronal dynamics during language comprehension[END_REF][START_REF] Mainy | Cortical dynamics of word recognition[END_REF])), among other cognitive operations. In this context, the results of this dissertation support evidence of these characteristic spatiotemporal activity patterns as a general mechanism for brain function. Our results emphasize the importance of not only spatial distribution but also the temporal dynamics of brain activity for brain function and cognition. Moreover, they support the need to further enrich a traditional view of brain function based on specific circuits and networks that would be associated to specific processes with a more dynamical model based on largely-distributed transient spatiotemporal patterns of neural activity that could code for several operations in a limited number of neurons. In a context in which studies assessing the neural basis of different cognitive processes often report networks that are highly co-localized (see (Corbetta et al. 2002;Corbetta et al. 2008;Dehaene and Changeux 2011;Siegel et al. 2011) for reviews in attention, consciousness and perceptual decision-making), in line with influential literature published in the last decade and departing from the three influential reviews of Engel, Salinas, Varela and colleagues [START_REF] Engel | Dynamic predictions: oscillations and synchrony in top-down processing[END_REF]Salinas and Sejnowski 2001;Varela et al. 2001), our data show that cerebral oscillations, characterized by site, frequency and phase, provide an excellent electrophysiological code to multiplex functions within a given neural substrate. Our studies gathered extensive causal evidence that such refined language might cover the modulation of a broad spectrum of visual behaviors and might ultimately underlie different cognitive operations and processes.

At the same time, our studies aimed to explore the ability of noninvasive stimulation methods, and very particularly TMS, to modulate specific neural activity patterns with the ultimate goal to apply these techniques and procedures in the near future to treat pathological conditions involving specific alterations of neural activity and brain oscillations. TMS proved largely able to modulate brain rhythms with an expected temporal resolution that allows isolating the effects of frequency through precise small temporal variations of the stimulation patterns used. We provide evidence in line with previous work that the effects of TMS depend on the state of activity within the stimulated region and associated networks and that such could be employed through simple visuo-spatial paradigms to shape neurostimulation effects and make them specific to some of the interleaved subpopulations of neurons within the stimulated region (Chanes et al. 2012;Silvanto et al. 2008;Silvanto and Muggleton 2008a;Silvanto and Muggleton 2008b;Silvanto et al. 2007a;Silvanto et al. 2007b;Silvanto and Pascual-Leone 2008).

VII.III. Pending questions and future directions

Our studies were designed to maximize the number of conditions tested respecting at all times safety regulations. Notwithstanding, the high technological demands and long duration of our experimental sessions have certainly limited the stimulation parameters that could be probed and the number of conditions that could be tested. Our will to have adequate sham TMS conditions and active TMS control patterns to isolate the effects of frequency from the impact of frequency-unspecific regional activation, significantly increased the duration of the experiments and limited the number of regions and frequencies tested. Indeed, the delivery of sham and active pulses randomly interleaved required the use of two repetitive TMS machines, which was challenging to have available at the same place and time but provided an outstanding design in which active and sham TMS patterns were embedded in the same block.

Our studies have thoroughly addressed the role of the right FEF with single pulses and the oscillatory basis and phase dependence of its causal contributions to visual discrimination and conscious detection. We have also studied similar oscillation-related effects on the right IPS region and addressed inter-hemispheric differences between the right and left FEF. However, interesting aspects remain unexplored at the end of this thesis and will have to be addressed in the near future in order to provide further detail and complete the general picture of how right and left fronto-parietal systems operate to modulate visual perception. In particular, the role and dynamics of the left IPS in perceptual performance remains unexplored, as well as the role of lower frequencies, like theta (known to modulate gamma through crossfrequency interactions) (Canolty et al. 2006;Jensen and Colgin 2007;Lakatos et al. 2008;Mormann et al. 2005) or alpha (the desynchronization of which at occipital and posterior parietal locations has been associated with visuo-spatial attention) (Gould et al. 2011;Rihs et al. 2007;Sauseng et al. 2005;Thut et al. 2006;Worden et al. 2000).

The requirements of some of the experiments also limited the stimulation parameters that could be used. Although the optimal stimulation intensity is not known, frontal and parietal regions are often stimulated at intensities of 50-65% of the maximum stimulator output (e.g. (Grosbras and Paus 2002;Grosbras and Paus 2003;O'Shea et al. 2004;Romei et al. 2011;Silvanto et al. 2006)). However, in our experiments using short TMS bursts, we used a fixed intensity of 45%, at which the capacitors of the machine showed capable of recharging on time to deliver each of the 4 pulses of the burst (particularly for the higher frequency, i.e. 50 Hz, for which the pulses were closer). Although our studies show that such "technically-imposed" slightly low levels of intensity induce significant perceptual effects, one could think that higher TMS intensities, as those more widely used, could have been more effective. Notwithstanding, higher intensities are also associated with higher 'click' sound and stronger tapping sensation accompanying the pulses, which might interfere more severely with the experiment and be more difficult to control for.

Moreover, in the same experiments, we chose to deliver trains of four pulses, as a compromise between an enough trial-by-trial number of induced cycles and, together with long inter-trial intervals, a short-enough stimulation to avoid carry over effects across trials and experimental blocks.

Our choice of a double sequential visual task (conscious detection and forcedchoice discrimination) was made with the aim to capture potential modulations of frontal and parietal neural activity to well-known behavioral measures, which represent different aspects of visual function and performance. However, our interventions proved mostly unable to modulate forced-choice discriminations. Only when a sufficient amount of activity was delivered on the right IPS in a short critical time window (corresponding to 50 Hz and its matched non-frequency-specific pattern), TMS stimulation improved discrimination accuracy. We also found improvements of visual discrimination during the stimulation of the right FEF with high-beta TMS patterns when the 4-pulse bursts were delivered in a critical short pretarget time window. In both cases, these effects were found to be independent on conscious access (i.e. observed when all targets, undetected and correctly detected, we taken into account). Future research will have to address why awarenessindependent perceptual modulations were so rarely observed and why conscious processes could not be modulated further than detection (i.e. why conscious discrimination could not be enhanced).

In our first study (Chapter II), inspired by well-characterized versions of this same type of paradigms (Chica et al. 2011), we asked participants to perform as fast and as accurately as possible a forced-choice discrimination task. This was done because both endogenous and exogenous attention are known to consistently decrease reaction times in this task (Chica et al. 2011), what is considered a robust maker of attentional orienting. However, single-pulse right-FEF TMS stimulation, either alone or in combination with spatial cues, proved unable to modulate discrimination reaction times and accuracy, suggesting that such increases in perceptual sensitivity (d') might not have been mediated by attentional processes. In order to avoid a potential speed-accuracy trade-off that could limit the modulation of visual discrimination and in an attempt to approximate response conditions to those of the conscious visual detection task in which there was no time pressure, for the remaining experiments we asked participants to perform discrimination 'as accurately as possible' (as opposed to 'as fast and as accurately as possible'). This, we thought, would possibly make it easier to observe modulations of discrimination but, at the same time, eliminated the well-studied signature of attentional processes from our studies. Future research will have to address whether perceptual modulations induced by different activity patterns correspond to attentional or other cognitive processes.

Finally, the interpretation of some of our results was limited by the lack of concurrent TMS-EEG recordings. Indeed, such data, which we are currently trying to develop, will help to elucidate how the TMS pulses delivered interact with ongoing activity and particularly with ongoing oscillations. They will help to quantify the entrainment of oscillations at the frequencies used (Chapters III, IV and VI) and could provide further basis to understand the visual detection stochastic facilitation driven by random arrhythmic left-FEF activity (Chapter IV). Such recordings could also shed further light in the hypothesized potential cross-frequency interactions between highbeta and gamma activity in the right FEF resulting in the modulation of response criterion (Chapter V), and help to better understand how the synchrony of the right posterior parietal (IPS) and the right FEF regions at beta and gamma frequencies interact.

The use of concurrent TMS-EEG has been seriously developed in the last decade thanks to ad hoc TMS-compatible EEG equipment, particularly pre-amplifiers, able to clamp recordings during the TMS artifact (e.g. [START_REF] Virtanen | Instrumentation for the measurement of electric brain responses to transcranial magnetic stimulation[END_REF])), to not respond to the very high slew rate of the TMS pulse (e.g. [START_REF] Thut | Effects of single-pulse transcranial magnetic stimulation (TMS) on functional brain activity: a combined event-related TMS and evoked potential study[END_REF])) or to not saturate during the TMS pulse thanks to wide dynamic range (e.g. [START_REF] Bonato | Transcranial magnetic stimulation and cortical evoked potentials: a TMS/EEG co-registration study[END_REF]). Nonetheless, it remains very challenging to record artifact-free EEG activity during the short inter-pulse intervals and the key time between the burst end and target onset. Such approaches have however been able to provide at least important evidence of alpha entrainment during 5-pulse 10-Hz bursts with a rapid decay time beyond the duration of the burst (Thut et al. 2011). More challenging is the recording of similar evidence in actively performing participants stimulated at higher frequencies, as the ones used in our studies (i.e. 30 and 50 Hz, with inter-pulse intervals of ~33 and 20 ms, respectively). Additional hurdles that difficult the use of such methods is that combined TMS-EEG behavioral experiments are cumbersome for participants and long, with important preparation time and a high required number of trials per condition. This limits the number of conditions that can be tested per session. Overall, the use of concurrent TMS-EEG is of course well justified when EEG recordings are essential to prove the hypothesis raised in the study. In absence of concurrent EEG recordings, we have mostly tried to limit our interpretations along the present dissertation to the patterns that were 'injected' to the region, rather than base them on the interactions of such patterns with the region's ongoing activity. We have also made a real effort to emphasize the potential but also the limits of our interpretations, differentiating between proven facts and explanatory hypothesis in attendance of further demonstration.

VII.IV. Perspectives

The relevant role of cerebral oscillations in brain function and cognition has been now studied for more than a decade. Nonetheless, this endeavor just started and in the years to come, the development of novel methods and imaging technologies will permit a noninvasive causal investigation of the many aspects that remain to date controversial or simply unexplored (see (Dayan et al. 2013) for a recent review). In particular, recent low-cost, portable non-invasive neuromodulation devices such as transcranial alternate current stimulation (tACS), for example, may provide an easier and safer way to explore, at the cost of a limited spatial resolution, the causal contribution of characteristic spatiotemporal activity patterns to human cognition in healthy participants and brain-damaged patients. This and other emerging techniques, such as ultrasound or optical stimulation, will certainly combine their efforts with frequency-tailored rhythmic TMS, as used in the current dissertation, to pursue common goals.

In addition, the development of more selective stimulation coils for TMS and other non-invasive brain stimulation tools capable of reaching deeper brain structures will extend to subcortical sites explorations that are now limited to rather superficial brain areas. Most importantly, the combination of noninvasive neurostimulation with neuroimaging techniques able to assess the extent of its impact with an acceptable spatial resolution, and with high-density EEG in online TMS applications and magnetoencephalography (MEG) in offline designs, will provide valuable knowledge on brain function and broaden the interpretation of causal evidence.

The challenges ahead are complex and the stakes high, since the therapeutic potential is enormous and could benefit many. Indeed, brain oscillations have been shown for example to be affected in highly prevalent neuropsychiatric disorders including schizophrenia (Uhlhaas and Singer 2010) and depression (Linkenkaer-Hansen et al. 2005). Similarly, visual target omissions have been correlated recently in right-hemisphere stroke patients suffering visuo-spatial neglect to increases in the power of low-beta oscillations in the left FEF [START_REF] Rastelli | Neural dynamics of neglected targets in patients with right hemisphere damage[END_REF], providing clear clues for the use oscillation-tailored TMS in the rehabilitation of focal brain damage.

Overall, a better understanding of impaired brain rhythms and their relation with cognitive function and deficits should encourage the use of noninvasive neurostimulation techniques, first to causally probe their physiological role and later on to better understand its alterations with the ultimate goal to restore the rhythms of the healthy system.

  work at lower intensities. Nonetheless, stimulation of lateral occipital regions has been shown to affect shape discrimination and distance judgments (Ellison and Cowey 2006) as well as facial recognition (Gilaie-Dotan et al. 2010).

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Figure 1 .

 1 Figure 1. Sequence of events during a representative trial of Experiment 1 (left) and Experiment 2 (right).In both experiments, participants were requested to fixate at a central cross for a randomly variable period of time between 1000 to 1500 ms. In Experiment 1, the fixation cross became slightly bigger for 66 ms and was followed by an active or a sham single TMS pulse delivered on the right FEF, 80,100 or 140 ms prior to target onset. In Experiment 2, a peripheral visuo-spatial cue, consisting in a black circle was displayed for 66 ms to the right or the left of the fixation cross. The cue was predictive about the location of the subsequent target (75% valid and 25% invalid trials), and was followed by a TMS pulse delivered 80 ms pre-target onset. In both experiments active or sham TMS pulses were interleaved in a randomized order. Then, after an interstimulus interval (ISI) of 233 ms, a Gabor with the lines tilted to the left or the right appeared for 33 ms at the center of one of the two lateral boxes. Participants were then requested to perform two sequential tasks; first a visual line categorization task to indicate the orientation of the Gabor lines (left/right) and second, a conscious visual detection task in which they had to report if they did see the target, and where they saw it (left/right). A cue is considered valid when it correctly signals the location of the upcoming target (left or right), and invalid when it incorrectly signals target location. A valid trial is the one including a valid cue and the opposite applies to invalid trials. The figure shows for Experiment 2 an example of a valid trial (see Material and Methods for full details on the behavioral paradigms). doi:10.1371/journal.pone.0036232.g001

  Figure 1. Sequence of events during a representative trial of Experiment 1 (left) and Experiment 2 (right).In both experiments, participants were requested to fixate at a central cross for a randomly variable period of time between 1000 to 1500 ms. In Experiment 1, the fixation cross became slightly bigger for 66 ms and was followed by an active or a sham single TMS pulse delivered on the right FEF, 80,100 or 140 ms prior to target onset. In Experiment 2, a peripheral visuo-spatial cue, consisting in a black circle was displayed for 66 ms to the right or the left of the fixation cross. The cue was predictive about the location of the subsequent target (75% valid and 25% invalid trials), and was followed by a TMS pulse delivered 80 ms pre-target onset. In both experiments active or sham TMS pulses were interleaved in a randomized order. Then, after an interstimulus interval (ISI) of 233 ms, a Gabor with the lines tilted to the left or the right appeared for 33 ms at the center of one of the two lateral boxes. Participants were then requested to perform two sequential tasks; first a visual line categorization task to indicate the orientation of the Gabor lines (left/right) and second, a conscious visual detection task in which they had to report if they did see the target, and where they saw it (left/right). A cue is considered valid when it correctly signals the location of the upcoming target (left or right), and invalid when it incorrectly signals target location. A valid trial is the one including a valid cue and the opposite applies to invalid trials. The figure shows for Experiment 2 an example of a valid trial (see Material and Methods for full details on the behavioral paradigms). doi:10.1371/journal.pone.0036232.g001

Figure 2 .

 2 Figure 2. TMS targeted region, neuronavigation and coil placement. The specific location of the right FEF was identified and labeled in a three dimensional reconstruction of each participant's MRI. The area was targeted with a 70 mm figure-of-eight TMS coil guided by a frameless stereotaxic neuronavigation system (a and b). The active TMS coil was placed flat with its center tangential to the targeted site and oriented lateral to medial and rostral to caudal orientation (c), approximately parallel to the medial portion of the central sulcus, i.e., , a 45u angle with respect to the interhemispheric fissure. See axial (d), coronal (e) and sagittal (d) MRI views of the location for the TMS targeted right FEF (see Material and Methods for full details on the targeting strategy). doi:10.1371/journal.pone.0036232.g002

Figure 3 .

 3 Figure 3. TMS-induced modulations of right FEF pre-target activity on conscious detection (Experiment 1). Perceptual sensitivity (mean 6 SE) for the three different timings (80, 100 and 140 ms pre-target onset) used in Experiment 1. Data is presented separately for targets displayed in the visual field contralateral (left visual field, LVF) or ipsilateral (right visual field, RVF) with respect to the targeted right FEF under active (red) or sham (blue) TMS stimulation. A main effect of TMS condition was observed, with higher perceptual sensitivity scores under active than sham TMS pulses (F(1,9) = 8.31, p = 0.018). Based on the a priori hypothesis that such effect depended on timing, we performed three separate repeated measures ANOVA for the three timings. The TMS effect only reached significance when pulses were delivered 80 ms pre-target onset (F(1, 9) = 9.77, p = 0.012), but not when applied 100 ms (F = 5.09, p = 0.051) nor 140 ms (F = 3.95, p = 0.078) pre-target onset. doi:10.1371/journal.pone.0036232.g003

Figure 4 .

 4 Figure 4. TMS-induced modulations of FEF pre-target activity on conscious detection after cue-driven attentional orienting (Experiment 2). Perceptual sensitivity (mean 6 SE) for targets displayed in the visual field contralateral (left visual field, LVF) or ipsilateral (right visual field, RVF) with respect to the stimulated right FEF site under active TMS (red) or sham TMS (blue). An interaction between validity and TMS proved statistically significant (F(1, 6) = 6.54, p = 0.043) indicating that, when delivered after the presentation of a peripheral predictive visuo-spatial cue, TMS pulses yielded significant bilateral enhancements of conscious visual detection only when the cue correctly signaled the location of the subsequent target (valid trials, F = 19.26, p = 0.005, indicated by the asterisk), whereas no effects were observed when the cue incorrectly predicted it (invalid trials, F,1). doi:10.1371/journal.pone.0036232.g004

Figure 2 .

 2 Figure 2. Impact of neurostimulation bursts at 30 and 50 Hz on conscious visual detection measures. a, b, Series of histograms displaying the effects of active (light gray) or sham (dark gray) frequency-specific and non-frequency-specific TMS bursts on perceptual sensitivity (a) and response criterion (b) values for each of the two TMS frequencies tested. Data are presented for targets displayed in the contralateral (left) and ipsilateral (right) visual fields (LVF and RVF, respectively) with regards to the stimulated right FEF region.Notice that active 30 Hz TMS bursts (but not their non-frequency-specific associated bursts) enhanced perceptual sensitivity (dЈ) compared with sham TMS patterns. Neither uniform 50 Hz bursts nor their associated non-frequency-specific patterns modulated this outcome measure. In contrast, active 50 Hz TMS bursts (but not its associated non-frequency-specific burst) relaxed response criterion for active TMS bursts compared with sham. No significant criterion differences were observed either for 30 Hz TMS bursts or for their associated non-frequency specific patterns. Asterisks indicate statistically significant differences for active versus sham TMS conditions.

  aged between 20 and 31 years old (244) took part in the first experiment (left FEF frequency-specific and rhythmic nonfrequency-specific patterns). Eleven of them were naïve as to TMS and to the purpose of the experiment. A new group of 12 participants (6 women and 6 men, 4 of them having participated in the first experiment) aged between 21 and 39 (255) took part in the second experiment (left FEF random arrhythmic non-frequency-specific patterns). All subjects participated voluntarily and reported no history of neurological or psychiatric disorders and normal or corrected-to-normal vision. The protocol was reviewed by the Inserm (Institut National de la Santé et la Recherche Scientifique) ethical committee and approved by an Institutional Review Board (CPP Ile de France

Chapter IV -Figure 2 .

 2 Effects of frequency-specific, rhythmic non-frequency-specific and random arrhythmic non-frequency-specific TMS bursts on perceptual sensitivity. Bars represent perceptual sensitivity (d') for targets displayed in the ipsilateral (left, L) and contralateral (right, R) visual fields (VF) with regards to the stimulated site. Asterisks (*) indicate statistically significant differences (p<0.05) between active and sham TMS. (a) Frequency-specific 30 Hz TMS bursts did not yield significant effects on visual sensitivity as compared to sham TMS. In contrast, both rhythmic and (b) random arrhythmic non-frequency-specific patterns enhanced perceptual sensitivity.

Chapter V -Figure 2 .

 2 Effects of high-beta TMS bursts at different phases on perceptual sensitivity in the conscious visual detection task. Bars represent perceptual sensitivity for targets displayed in the ipsilateral (left, L) and contralateral (right, R) visual fields (VF) with regards to the stimulated site.

Chapter V -Figure 3 .

 3 Effects of high-beta TMS bursts at different phases on response criterion in the conscious visual detection task. Bars represent response criterion for targets displayed in the ipsilateral (left, L) and contralateral (right, R) visual fields (VF) with regards to the stimulated site.Asterisks (*) indicate statistically significant differences (p<0.05) between active and sham TMS and for 0.05<p<0.10 the exact value is indicated. High-beta (30-Hz TMS) stimulation shifted response criterion in a phase-dependent manner, particularly when the burst was delivered further from the target.

Chapter V -Figure 4 .

 4 Effects of high-beta TMS bursts at different phases on the forced-choice visual discrimination task. Bars represent accuracy for targets displayed in the ipsilateral (left, L) and contralateral (right, R) visual fields (VF) with regards to the stimulated site. Asterisks (*) indicate statistically significant differences (p<0.05) between active and sham TMS and for 0.05<p<0.10 the exact value is indicated. 30-Hz TMS bursts improved accuracy when they were delivered in a specific time window.

Chapter VI -Figure 1 .

 1 Experimental design. (a) Following a central fixation screen, a low-contrast near-threshold target (Gabor stimulus) could be briefly presented within the left or right peripheral placeholders. Participants were asked to perform two tasks: first, a forced-choice line orientation discrimination task (clockwise/counterclockwise); second, a conscious detection task (did you see the target? no/yes and if yes, in which of the lateral boxes). (b) Schematic representation of the temporal distribution of the 4 pulses in the different patterns. (c) Coil positioning on the right IPS.

Chapter VI -Figure 3 .

 3 Impact of 30 (left) and 50 (right) Hz activity and their corresponding nonfrequency-specific patterns on general accuracy (top) and accuracy of consciously correctly detected targets (bottom) in the discrimination task for active (red) and sham (blue) TMS separately for the left (L) and the right (R) visual fields (VF). A frequency-independent effect was observed: sufficiently powerful activation (50 Hz and its matched non-frequency specific block) enhanced accuracy independently on conscious access.

  top right), indicating better performance with active than sham TMS patterns. No main effects or interactions were observed when only correctly-consciously-detected-target trials were considered (Chapter VI -Figure 3 bottom right).

  SE) and response criterion (beta measures, mean 6 SE), and reaction time (RT) (mean 6 SE) and accuracy (mean 6 SE), for the three different TMS delivery timings (80, 100 and 140 ms pre-target onset), obtained respectively for the conscious visual detection and visual categorization tasks explored in Experiment 1. Data are presented for targets displayed in the visual field contralateral (left visual field, LVF) and ipsilateral (right visual field, RVF) with respect to the stimulated right FEF under the effects of active or sham TMS pulses. doi:10.1371/journal.pone.0036232.t001

	Task	Mean values±SE	TMS condition	80 ms		100 ms		140 ms
				LVF	RVF	LVF	RVF	LVF	RVF
	Detection	d' score	Sham	1.8260.16	2.0360.13	1.7960.23	1.8160.13	1.8960.20	2.1560.12
			Active	2.0560.15	2.2060.09	2.0360.13	2.3160.12	2.1260.11	2.2360.12
		Beta measure Sham	5.6960.45	5.6660.29	4.6660.56	4.9360.53	5.3860.45	5.4260.35
			Active	5.6060.37	6.8460.34	5.9160.33	5.5060.37	5.9060.31	5.3260.43
	Categorization	RT (ms)	Sham	849655	778651	805638	767649	814649	779658
			Active	840652	792653	833652	776641	834650	789640
		Accuracy	Sham	0.6860.03	0.7960.04	0.7060.04	0.7860.03	0.7660.04	0.7260.03
			Active	0.7760.02	0.7960.02	0.6960.03	0.7660.02	0.6960.04	0.7560.02
	Perceptual sensitivity (d' scores, mean 6

Table 2 .

 2 Data from TMS-induced modulations of FEF pre-target activity on visual performance after cue-driven attentional orienting (Experiment 2). SE) and response criterion (beta measures, mean 6 SE), and reaction time (RT) (mean 6 SE) and accuracy (mean 6 SE), for the conscious visual detection and visual categorization tasks explored in Experiment 2. Data are presented for valid and invalid trials, in which targets were displayed in the visual field contralateral (left visual field, LVF) and ipsilateral (right visual field, RVF) to the stimulation site (right FEF), under the effects of active or sham TMS pulses. doi:10.1371/journal.pone.0036232.t002

	Task	Mean values±SE	TMS condition	Invalid		Valid
				LVF	RVF	LVF	RVF
	Detection	d' score	Sham	2.0560.77	1.8160.41	2.5760.45	2.4260.33
			Active	1.9960.78	1.7760.28	2.8760.40	2.6860.32
		Beta Measure	Sham	15.6766.57	15.7364.87	9.7366.89	13.4166.54
			Active	13.4965.21	14.9464.76	10.4464.79	14.0967.13
	Categorization	RT (ms)	Sham	9106175	825674	7176133	7306105
			Active	8326136	8206120	7196128	719697
		Accuracy	Sham	0.7360.18	0.7960.03	0.7360.05	0.7960.04
			Active	0.7960.14	0.8360.10	0.7460.05	0.7460.06
	Perceptual sensitivity (d' scores, mean 6

Table 1 . Summary of main statistical effects and interactions of the different neurostimulation patterns on conscious visual detection

 1

			Perceptual sensitivity	Response criterion
		Main effects and	(dЈ)		(beta)
	Group	interactions	F	p	F	p
	30 Hz	Block	6.328	0.027*	0.715	0.414
		Side	10.978	0.006*	1.770	0.208
		TMS	0.576	0.463	0.056	0.816
		Block ϫ Side	1.219	0.291	1.584	0.232
		Block ϫ TMS	6.072	0.030*	3.897	0.072
		Side ϫ TMS	2.178	0.166	1.055	0.325
		Block ϫ Side ϫ TMS	0.013	0.911	0.045	0.836
	50 Hz	Block	0.229	0.641	0.002	0.968
		Side	2.180	0.164	8.069	0.014*
		TMS	0.048	0.830	7.948	0.014*
		Block ϫ Side	1.666	0.219	1.005	0.334
		Block ϫ TMS	1.776	0.205	6.418	0.025*
		Side ϫ TMS	0.866	0.369	0.486	0.498
		Block ϫ Side ϫ TMS	0.288	0.601	0.973	0.342

PLoS ONE | www.plosone.org

May 2012 | Volume 7 | Issue 5 | e36232

• J. Neurosci.,March 13, 2013 • 33(11):5000 -5005 Chanes et al. • Frontal Oscillations and Visual Performance

• J. Neurosci.,March 13, 2013 • 33(11):5000 -5005Chanes et al. • Frontal Oscillations and Visual Performance

Acknowledgments

The authors thank Drs. Paolo Bartolomeo, Sabine Meunier Pascale Pradat-Diehl and Katia Andrade for providing medical supervision during TMS sessions, Drs. Jarrett Rushmore, Seth Elkin-Frankston and Traian Popa for suggestions, and Dr. Pierre Pouget for advice on data analyses. We also thank the coordinators of the IFR neurosciences/CRICM platform and the ER6 UPMC, Dr. Rose Katz, Dr. Ve ´ronique Marchand-Pauvert and Dr. Alexandra Lackmy, for technical and logistic support.

Acknowledgements

Study funded by FP6 (EU VIth Frame Program) & ANR (Agence National de la Recherche Scientifique) project eraNET-NEURON BEYONDVIS to Dr. A. Valero-Cabré. L. Chanes was supported by a PhD fellowship of the École des Neurosciences de Paris (ENP). Dr. M. Vernet was supported by the Fondation pour la Recherche Médicale. We are thankful to Dr. B. Dubois for scientific and logistic for some of the experiments, to Drs. P. Bartolomeo, S. Meunier and P. Pradat-Diehl for providing medical supervision during TMS sessions and Drs. R. Katz, and A. Lackmy, for technical and logistic help with TMS equipment. Acknowledgements Study funded by FP6 (EU VIth Frame Program) & ANR (Agence National de la Recherche Scientifique) project eraNET-NEURON BEYONDVIS to Dr. A. Valero-Cabré. L. Chanes was supported by a PhD fellowship of the École des Neurosciences de Paris (Paris School of Neuroscience, ENP) and "la Caixa". Dr. M. Vernet was supported by a Fondation pour la Recherche Médicale fellowship. We also thank the IFRAD foundation for providing equipment funds. We are thankful to Dr. B. Dubois for scientific and logistic support in some of the experiments and to Drs. P. Bartolomeo, P. Pradat-Diehl, Rose Katz and S. Meunier for providing medical supervision during TMS sessions. Acknowledgements Study funded by FP6 (EU VIth Frame Program) & ANR (Agence National de la Recherche Scientifique) project eraNET-NEURON BEYONDVIS to Dr. A. Valero-Cabré. L. Chanes was supported by a PhD fellowship of the École des Neurosciences de Paris (Paris School of Neuroscience, ENP) and "la Caixa". Dr. M. Vernet was supported by a Fondation pour la Recherche Médicale fellowship. We also thank the IFRAD foundation for providing equipment funds. We are thankful to Dr. B. Dubois for scientific and logistic support in some of the experiments and to Drs. P. Bartolomeo, P. Pradat-Diehl, Rose Katz and S. Meunier for providing medical supervision during TMS sessions.

This work was funded by FP6 and ANR project eraNET-NEURON BEYONDVIS, Marie Curie Intra-European Program (FP7), and PhD fellowship E ´cole des Neurosciences de Paris (ENP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was funded by FP6 (EU VIth Frame Program) and Agence National de la Recherche Scientifique project eraNET-NEURON BEYONDVIS to A.V.-C. We thank the IFRAD foundation for providing equipment funds. L.C. was supported by a PhD fellowship of the E ´cole des Neurosciences de Paris. We are thankful to Dr. B. Dubois for scientific and logistical support in some of the experiments, and to Drs. P. Bartolomeo, S. Meunier, and P. Pradat-Diehl for providing medical supervision during TMS sessions, and Drs. R. Katz and A. Lackmy for technical and logistical support with TMS equipment.

Chapter II

Manipulation of Pre-Target Activity on the Right Frontal Eye Field Enhances Conscious Visual Perception in Humans

Author Contributions

Conceived and designed the experiments: LC ABC AVC. Performed the experiments: LC AVC. Analyzed the data: LC ABC RQ AVC. Contributed reagents/materials/analysis tools: LC ABC AVC. Wrote the paper: LC AVC. RQ Contributed to data analysis and manuscript discussions leading to the written interpretations included in this PLoS ONE submission: RQ.

Chapter III

Brief Communications

Causal Frequency-Specific Contributions of Frontal Spatiotemporal Patterns Induced by Non-Invasive Neurostimulation to Human Visual Performance Lorena Chanes, 1 Romain Quentin, 1 Catherine Tallon-Baudry, 1,2 and Antoni Valero-Cabre ´1,3,4 1 Universite ´Pierre et Marie Curie, CNRS UMR 7225-INSERM UMRS S975, Groupe Centre de Recherche de l'Institut du Cerveau et la Moelle (ICM), 75013 Paris, France, 2 Laboratoire de Neurosciences Cognitives, INSERM-ENS UMR960, 75005 Paris, France, 3 Laboratory for Cerebral Dynamics Plasticity and Rehabilitation, Boston University School of Medicine, Boston, Massachusetts 02118, and 4 Cognitive Neuroscience and Information Technology Research Program, Open University of Catalonia (UOC), 08035 Barcelona, Spain Neural oscillatory activity is known to play a crucial role in brain function. In the particular domain of visual perception, specific frequency bands in different brain regions and networks, from sensory areas to large-scale frontoparietal systems, have been associated with distinct aspects of visual behavior. Nonetheless, their contributions to human visual cognition remain to be causally demonstrated. We hereby used non-uniform (and thus non-frequency-specific) and uniform (frequency-specific) high-beta and gamma patterns of noninvasive neurostimulation over the right frontal eye field (FEF) to isolate the behavioral effects of oscillation frequency and provide causal evidence that distinct visual behavioral outcomes could be modulated by frequency-specific activity emerging from a single cortical region. In a visual detection task using near-threshold targets, high-beta frequency enhanced perceptual sensitivity (d) without changing response criterion (beta), whereas gamma frequency shifted response criterion but showed no effects on perceptual sensitivity. The lack of behavioral modulations by non-frequency-specific patterns demonstrates that these behavioral effects were specifically driven by burst frequency. We hypothesize that such frequency-coded behavioral impact of oscillatory activity may reflect a general brain mechanism to multiplex functions within the same neural substrate. Furthermore, pathological conditions involving impaired cerebral oscillations could potentially benefit in the near future from the use of neurostimulation to restore the characteristic oscillatory patterns of healthy systems. avalerocabre@gmail.com; avalero@bu.edu

Chapter IV

Summary

The frontal eye field (FEF) is a brain region involved in several processes relevant for visual performance, including visuo-spatial attention, access to consciousness and decision-making. Prior research has casually demonstrated that frontal high-beta FEF activity in the right hemisphere enhances conscious visual perception, an outcome that is in agreement with evidence of beta synchronization along a right dorsal fronto-parietal network during attentional orienting and with a right-hemisphere's dominance in spatial attention. However, frontal regions in the left hemisphere have also been shown to contribute to the modulation of perceptual performance. To causally explore the neural basis of these contributions, we delivered high-beta frequency-specific activity to the left FEF and report that in this region, these patterns failed to modulate conscious perception. In contrast, both rhythmic and random arrhythmic non-frequency-specific activity patterns yielded visual performance improvements similar to those formerly reported by high-beta activity on its right-hemisphere homotopic area. Our study yields support for stochastic facilitation of conscious vision by non-frequency-specific activity on the left frontal cortex, and suggests a behaviorally relevant role for the induction of neural noise in the modulation of visual perception. Furthermore, taken together with prior causal right FEF evidence, our results indicate that frontal regions in both hemispheres contribute to the modulation of conscious visual perception through different neural coding strategies. Visual stimuli were displayed on an eye-tracker screen (Tobii Technology AB 17'' wide, 1024x768) using a laptop computer (Dell Latitude E6400) and standard stimulus presentation software (E-prime). Each trial started with a grey resting screen (luminance: 75 cd/m2, 2500 ms), followed by a fixation screen (randomly lasting between 1000 and 1500 ms) (Chapter IV -Figure 1a). The fixation cross (0.5x0. preference for 'yes I perceived the target' over 'no, I did not perceive the target' or viceversa), independently on signal strength. To compute these measures, trials in which the location of a target was correctly determined by participants were considered as correct detections or "hits" and trials in which participants reported a location for targets that were not presented were considered "false alarms". Trials in which the location of a present target was incorrectly reported were counted as "errors". Eye movements were monitored for fixation control purposes (Eyelink 1000, SR Research). Fixation was considered broken when participants' eyes position was recorded outside a circle of 2 o around the fixation cross anytime from 300 ms before target onset to the end of target presentation. If this occurred, participants received an alert message the trial was randomized with the rest of the trials left in the block and repeated.

Keywords

A titration procedure performed prior to the onset of the experiment, allowed us to estimate for each participant the stimulus contrast at which ~50% of the displayed targets were consciously reported and the degree of line tilting for which the discrimination performance was ~75% of correctly reported targets. Such titration levels ensured that, in both tasks, performance was halfway between the worse (0% in the detection task and 50% in the forced-choice discrimination task) and the best possible one (100% for both tasks). Participants started the titration trials with a high initial stimulus contrast and a high initial tilting angle. A one-up/one-down titration staircase procedure was used to determine the threshold level (50% correct responses in the conscious detection task), with an initial contrast step equal to the initial contrast, then divided by two at each change of direction of the staircase procedure but kept always larger than 0.005 Michelson contrast. We considered that the threshold had been steadily reached when the last five consecutively tested contrasts were not different by more than 0.01 Michelson contrast. In parallel, the degree of line tilting was titrated using a one-up/three-down titration staircase procedure to determine the threshold level (75% of correct response in the discrimination task), with an initial angle step equal to the initial angle, then divided by two at each change of direction of the staircase procedure but kept always an integer and larger than 1°. We considered that the threshold had been steadily reached when, the contrast being already set, the last five consecutively tested angles were not different by more than 2° of tilting.

Once the two titrations were completed, participants performed between 1 and 4 blocks of familiarization during which right FEF-TMS patterns were employed. Each of these blocks consisted of 20 trials, including 4 trials in which the target was absent.

In half of the trials, a short burst of 4 TMS pulses was delivered on the right FEF

Transcranial Magnetic Stimulation (TMS)

TMS pulses were delivered using a biphasic repetitive stimulator (Magstim SuperRapid) with a 70 mm diameter figure-of-eight coil. A structural T1-weighted MRI scan (3T Siemens MPRAGE, flip angle=9, TR=2300 ms, TE=4.18 ms, slice thickness=1mm) was acquired for every participant at the CENIR MRI center (Pitié-Salpêtrière Hospital, Paris). The right FEF region was localized on each individual MRI using averaged Talairach coordinates x=31, y=-2, z=47 (Paus 1996) and a 0.5 cm radius spherical Region of Interest (ROI). This same coordinates have been successfully employed in preceding experiments in our lab (Chanes et al. 2012;Chanes et al. 2013). The final MRI was uploaded into a frameless stereotaxic system and reconstructed in 3D for its use in an online stereotaxic TMS neuronavigation system (Brainsight, Rogue Research Inc.) (Chapter V -Figure 1c). At all times, the TMS coil was held tangentially to the skull, with its handle oriented ~45º in a rostralto-caudal and lateral-to-medial orientation, i.e. ~parallel to the central sulcus. Visual stimuli were displayed on an eye-tracker screen (Tobii TX300, Tobii Technology AB) using a laptop computer (Dell Latitude E6400) and standard stimulus presentation software (E-prime). Each trial started with a grey resting screen (luminance: 75 cd/m2, 2500 ms), followed by a fixation screen (randomly lasting between 1000 and 1500 ms) (Chapter VI -Figure 1a). The fixation cross (0.5x0. Participants were asked to execute two tasks. First, they had to determine the orientation of the Gabor lines (discrimination task) by pressing the corresponding button on a computer keyboard ("1" for counterclockwise and "2" for clockwise) with the index and middle fingers of their right hand. Participants were forced to guess a response even when the target was not presented or they did not consciously perceive it and accuracy was collected as the outcome measure. Secondly, they had to report whether they consciously perceived the Gabor or not (conscious detection task). To do so, two arrow-like stimuli (">>>" and "<<<") pointing to the left and to the right were simultaneously presented below and above the fixation cross. Participants were provided with 3 keys, which they had to operate with their left hand: an upper key "d", a lower key "c" and the space bar. The upper and lower keys were