M Groussard

G Rauchs

B Landeau

F Viader

B Desgranges

F Eustache

& H Platel

Hervé Platel
email: herve.platel@unicaen.fr

The neural substrates of musical memory revealed by fMRI and two semantic tasks

Keywords: inferior frontal cortex, fMRI, music, semantic memory, temporal cortex

Recognizing a musical excerpt without necessarily retrieving its title typically reflects the existence of a memory system dedicated to the retrieval of musical knowledge. The functional distinction between musical and verbal semantic memory has seldom been investigated. In this fMRI study, we directly compared the musical and verbal memory of 20 nonmusicians, using a congruence task involving automatic semantic retrieval and a familiarity task requiring more thorough semantic retrieval. In the former, participants had to access their semantic store to retrieve musical or verbal representations of melodies or expressions they heard, in order to decide whether these were then given the right ending or not. In the latter, they had to judge the level of familiarity of musical excerpts and expressions. Both tasks revealed activation of the left inferior frontal and posterior middle temporal cortices, suggesting that executive and selection processes are common to both verbal and musical retrieval. Distinct patterns of activation were observed within the left temporal cortex, with musical material mainly activating the superior temporal gyrus and verbal material the middle and inferior gyri. This cortical organization of musical and verbal semantic representations could explain clinical dissociations featuring selective disturbances for musical or verbal material.

Semantic memory refers to memory for general knowledge, unrelated to specific experiences or the type of material used (e.g. words, faces or music). Clinical studies have revealed that patients sometimes retain musical abilities despite severe cognitive impairments such as aphasia or amnesia [START_REF] Signoret | Aphasia without amusia in a blind organist. Verbal alexia-agraphia without musical alexia-agraphia in Braille[END_REF][START_REF] Cuddy | Music, memory, and Alzheimer's disease: is music recognition spared in dementia, and how can it be assessed?[END_REF][START_REF] Samson | Emotional power of music in patients with memory disorders: clinical implications of cognitive neuroscience[END_REF]. Based on neuropsychological dissociations reported in clinical studies, Peretz and colleagues [START_REF] Peretz | Modularity of music processing[END_REF][START_REF] Peretz | Music lexical networks: the cortical organization of music recognition[END_REF] have developed a cognitive model of the cortical organization of music recognition. This views the musical semantic memory system as a purely musical lexicon, which interacts with the verbal lexicon. In this study, musical semantic memory is defined as the long-term storage of familiar melodies or musical excerpts. It is musical semantic memory that allows us to experience a strong feeling of knowing when listening to music (reflecting familiarity processes) and gives us the ability to hum or whistle the subsequent notes of a melody, or in some cases retrieve the title, composer or performer of a particular excerpt (corresponding to identification) (Platel and Eustache 2000). Whereas numerous clinical studies have supported the idea that musical knowledge and verbal knowledge are cognitively autonomous (for review, see [START_REF] Peretz | Music, language and modularity in action[END_REF], few authors have investigated this issue using neuroimaging methods. The neural substrates of semantic memory have been unraveled using a variety of experimental paradigms in neuroimaging studies [START_REF] Cabeza | Imaging cognition II: an empirical review of 275 PET and fMRI studies[END_REF][START_REF] Binder | Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies[END_REF]. Semantic memory retrieval requires the activation of a large neural network, mainly located in the temporal and frontal cortices of the left hemisphere. When verbal material is used, semantic memory relies mainly upon the middle and inferior temporal and inferior frontal gyri in this hemisphere (for review, see [START_REF] Binder | Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies[END_REF]. The situation appears to be less clear-cut for musical material, however.

Neuroimaging studies featuring this type of material have reported the involvement of the anterior part of the temporal lobes, either in the left hemisphere [START_REF] Platel | Semantic and episodic memory of music are subserved by distinct neural networks[END_REF] or in both [START_REF] Satoh | Positron-emission tomography of brain regions activated by recognition of familiar music[END_REF], with activation of the middle part of the left superior temporal gyrus and the medial frontal cortices for recognition tasks [START_REF] Satoh | Positron-emission tomography of brain regions activated by recognition of familiar music[END_REF] and mainly of the left inferior frontal gyrus for familiarity tasks [START_REF] Plailly | The feeling of familiarity of music and odors: the same neural signature?[END_REF]. However, these studies did not allow direct comparisons to be made between music and language, and some of them used stimuli, such as nursery songs, which may also have elicited verbal processes.

In a previous H 2 O 15 PET study, we found that the verbal and musical sets of material used in a semantic congruence task drew on two close but partially distinct networks, located mainly in the left temporal cortex [START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF]. In this study, participants had to decide whether the second part of a familiar melody (musical congruence condition) or a French proverb (verbal congruence condition) was the right or wrong ending. They therefore had to access their semantic store in order to retrieve musical or verbal representations of the melodies or expressions they heard in order to decide whether they were then given the right ending or not. The post-experiment debriefing suggested, however, that the congruence task involved syntactic processes as well (i.e. corresponding to the detection of irregularities within the harmonic, melodic, rhythmic or metric structure), particularly for incongruent items.

In order to clarify this issue and to highlight the brain regions mainly involved in the as pure semantic retrieval process as possible, we chose to administer the same semantic tasks in the present study, using fMRI. In fact, using an event-related analysis, this makes it possible to exclude incongruent items which could involve syntactic processes.

Regarding the literature, it is now well established that both the difficulty and nature of the semantic task have an impact on the pattern of activation [START_REF] Mummery | Generating 'tiger' as an animal name or a word beginning with T: differences in brain activation[END_REF][START_REF] Muller | Receptive and expressive language activations for sentences: a PET study[END_REF][START_REF] Cabeza | Imaging cognition II: an empirical review of 275 PET and fMRI studies[END_REF]. Thus, in order to take our direct comparison of musical and verbal semantic tasks a stage further, we decided to administer a "semantic familiarity task" as well, requiring a more thorough semantic memory search to rate the level of familiarity of musical excerpts (musical familiarity condition) and French expressions (verbal familiarity condition) on a 4-point scale. In all the musical conditions, familiar musical stimuli were strictly selected (no excerpts with lyrics, and no excerpts which might spontaneously evoke autobiographical memories) in order to limit labeling and verbalization. These two tasks were highly complementary, in that the congruence task allowed us to explore musical semantic processing with as few verbal associations as possible but was also limited to an automatic search, whereas the familiarity task gave rise to more thorough semantic retrieval, but opened up the possibility of verbal labeling when the participants knew the melody extremely well, corresponding to identification (retrieving the title, composer or performer of the musical excerpt). The complementary aspects of these two tasks (i.e. congruence and familiarity tasks) investigating automatic and more thorough semantic retrieval, performed by the same participants, would allow us to increase the understanding of the functional organization of musical semantic memory and highlight the neural networks activated by verbal material, musical material or both. Previous studies have shown that semantic memory tasks with verbal or musical material activated the prefrontal and temporal areas mainly on the left side for verbal [START_REF] Binder | Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies[END_REF] and in both hemispheres for musical material [START_REF] Platel | The structural components of music perception. A functional anatomical study[END_REF], 2003[START_REF] Satoh | Positron-emission tomography of brain regions activated by recognition of familiar music[END_REF][START_REF] Plailly | The feeling of familiarity of music and odors: the same neural signature?[END_REF]. In addition, we recently proposed an anteroposterior organization within the left middle and superior temporal gyri [START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF], such that there was predominantly anterior activation during the musical semantic task and predominantly posterior activation during the verbal one. Thus, comparing the performance of the same group of nonmusician participants on congruence and familiarity semantic tasks featuring verbal and musical material, allows to examin more deeply these networks and the cognitive contribution of each cortical area.

MATERIAL & METHODS

Participants

Twenty healthy right-handed volunteers (mean ± SD: 24.55 ± 3.80 years) were selected from a population of university students (mean education level ± SD: 16.35 ± 2.03 years) to take part in this study. All were nonmusicians (10 women and 10 men) with normal hearing and no history of neurological disease. Participants were selected according to stringent criteria: (1) none had taken music lessons or participated in musical performances (except for compulsory music classes at secondary school (1hr per week)), (2) they were "common listeners" (i.e. not music lovers, who tend to listen to one specific type of music only), and (3) they scored normally on a test of pitch perception. All gave their written informed consent prior to taking part and the research protocol was approved by the regional ethics committee.

Stimuli and experimental procedure

All participants were tested on two memory tasks: a congruence task and a familiarity task.

The order of these tasks was counterbalanced across participants.

Congruence task

In the congruence task, two similar categories of semantic memory tasks were performed: one using musical material (hereafter called "MusSem") and the other using verbal items ("VerbSem"). In the former, subjects heard the beginning of a well-known tune, followed by a short silence and a beep tone (mean interval 800ms), then either the next part of the melody or a different familiar melody. They had to determine whether the second part matched (i.e. was the right ending to) the first one or not. This musical semantic memory task was contrasted with a perceptual reference condition ("MusRef") in which the subjects listened to two unfamiliar sequences of notes that were either identical or differed by one note, and then had to say whether or not they were the same. This task was designed to call on the same decisional, perceptual and motor processes as the experimental task, but not on musical semantic memory, since the musical sequences were unknown to the participants.

In the verbal semantic memory task, subjects listened to the beginning of a French proverb or popular saying, followed by a short silence and a beep tone (mean interval 800ms), and then by either the right or wrong ending (one belonging to another proverb). They had to decide whether or not the second part matched the first one. This verbal semantic memory test was contrasted with a perceptual reference condition ("VerbRef") in order to subtract the brain activation produced by decisional, perceptual and motor processes. In this task, subjects had to indicate whether two meaningless sequences of syllables (nonwords respecting French phonological rules) were the same or if they differed by one syllable. The difficulty of the verbal and musical semantic tasks was tested and adjusted in a pilot study. The musical congruence task was designed to probe semantic processing with as few verbal associations as possible and thus, to make the contrast between melody and word processing as sharp as possible when comparing the activation results (no lyrics, and an instruction that did not involve trying to remember the title or composer of familiar melodies; see supplementary material). For each task, subjects were instructed to respond by pressing the button with their right index finger if the response was "correct" (musical and verbal semantic tasks) or "similar" (musical and verbal reference tasks) and with their right middle finger if it was "incorrect" (musical and verbal semantic tasks) or "different" (musical and verbal reference tasks).

The musical and verbal stimuli were presented in two functional runs of sixteen 31-34 s blocks. Each run consisted of four blocks in each condition (alternating between musical reference, musical semantic, verbal semantic and verbal reference). The response interval between two stimuli was set at 3 seconds in order to minimize automatic subvocal labeling or episodic memory processes during this time.

Familiarity task

Like the congruence task, the familiarity task featured both musical and verbal material. In this task, participants had to rate the level of familiarity of 60 excerpts of melodies and 60

French expressions on a 4-point scale (i.e. familiarity referring to the participants" life experience). Participants were instructed to press the button under the middle finger of their left hand if they were sure that they never had heard the melody or the expression before (Fam1), the button under the index finger of their left hand if they were not sure whether they had heard it before or not (Fam2), the button under the index finger of their right hand if they remembered hearing it on several occasions (Fam3) and the button under the middle finger of their right hand if they knew it extremely well (Fam4). Participants were instructed to close their eyes in order to focus more on the task. Melodies were purely instrumental tonal excerpts, taken from both the classical and modern repertoires. Popular songs and melodies associated with lyrics were avoided in order to minimize verbal associations, as were those which might spontaneously evoke autobiographical memories, such as the "Wedding March" or melodies used in popular TV commercials. All melodies were selected from a previous pilot study whose participants were matched with the present task subject sample. The musical and verbal stimuli used in this familiarity task were different from those used in the congruence task (see supplementary material). The familiarity processes involved in this task required recognition not just of the item"s perceptual features, but also of its conceptual or semantic features. After a short training session performed inside the scanner, participants underwent two runs, each lasting 4 minutes and including 60 stimuli (30 verbal and 30 musical). The order of presentation of the conditions (Verbal, Music) was determined according to an efficient stochastic design [START_REF] Friston | Stochastic designs in event-related fMRI[END_REF] and the optimum order for detecting differences between the verbal and music items was computed using a genetic algorithm [START_REF] Wager | Optimization of experimental design in fMRI: a general framework using a genetic algorithm[END_REF].

All the melodies were played on a digital keyboard with a flute voice without orchestration and lasted 5-6 s. Before each fMRI scanning session (i.e. congruence task plus familiarity task), the subjects were trained with stimuli that were not used during the scanning session.

To allow direct comparisons to be made, the verbal and musical excerpts belonging to the same experiment were delivered to each participant in the same scanning session.

The musical stimuli were delivered through electrodynamic headphones ensuring attenuation of scanner noise by as much as 45dB (MR Confon headphones, Magdeburg, Germany). The volume of the musical stimuli was adjusted to ensure that each subject could hear the stimuli clearly above the noise of the MRI scanner. Melodies were produced using E-Prime software (Psychology Software Tools, Pittsburgh, PA) implemented within IFIS (Invivo, Orlando, FL).

Following the scanning session, we performed a debriefing in order to determine whether the melodies and expressions evoked any personal memories or mental imagery for the participants.

Behavioral analyses

Congruence task

Using Statistica software, we first carried out a repeated-measures analysis of variance (ANOVA) on performances. A two-way ANOVA was then conducted, with material as a between-subjects factor with two modalities (verbal and musical), and type of task as a within-subjects factor with two modalities (reference and semantic). If a significant interaction effect was observed, these analyses were followed up with Tukey"s HSD post hoc tests.

Familiarity task

Once again, a two-way ANOVA was performed, with material as a between-subjects factor with two modalities (verbal and musical), and familiarity as a within-subjects factor with four modalities (Fam1, Fam2, Fam3 and Fam4). Similar analyses were conducted with response time as a within-subjects factor with the same four modalities as those described above. If a significant interaction effect was observed, these analyses were followed up with Tukey"s HSD post hoc tests.

MRI scans

All images were acquired using the Philips (Eindhoven, The Netherlands) Achieva 3.0 T scanner. For each participant, a high-resolution, T1-weighted anatomical image was first were warped to roughly match the non-EPI T2* volume using the methodology developed and validated by [START_REF] Villain | A simple way to improve anatomical mapping of functional brain imaging[END_REF] to reduce geometric distortion. The warping parameters were then applied to all the EPI volumes for the session. The T1 image was then segmented/normalized using the SPM5 "Segment" procedure [START_REF] Ashburner | Unified segmentation[END_REF], with the ICBM/MNI priors and the resulting normalization parameters were applied to the T1 to the T2 Star normalized EPI images and to the non-EPI T2 Star volume. The normalized EPI images were finally smoothed at 8 mm FWHM. A high-pass filter was implemented using a cut-off period of 128 s to remove low-frequency drift from the time series.

Data were then analyzed using the general linear model approach on a voxel-by-voxel basis with a random effects model implemented with a two-level procedure, for both the congruence and the familiarity experiments. The aim of our study was understand the functional organization of semantic memory more fully, particularly the distinction between the semantic retrieval of musical and verbal material. For this purpose we chose to restrict our investigations to contrasts that revealed semantic retrieval processes and allowed the direct comparison of musical and verbal material.

fMRI data analysis

For each participant, changes in condition-related BOLD responses were estimated first, in the intra-individual analysis. For each experimental condition, the hemodynamic response was estimated by convolving the stimuli onset with the canonical hemodynamic response function (HRF). Motion parameters obtained from the realignment procedure were included as covariates of no interest in the design matrix.

Congruence task

Several contrasts (e.g. MusSem-MusRef; VerbSem-VerbRef; MusFam-VerFam; VerbFam-MusFam) were computed for each subject. In these contrasts, only the congruent stimuli were included, in order to reduce possible contamination by incongruity detection processes. All resulting contrast images were then entered into second-level random effect analyses.

For the congruence experiment, a full factorial design was used to reveal brain activation specifically associated with musical and verbal semantic memory processes. We To identify the brain areas involved in both the verbal semantic task versus verbal reference task [VerbSem-VerbRef] and the musical semantic task versus musical reference task [MusSem-MusRef] comparisons, we performed a conjunction analysis. This analysis served to pinpoint the cerebral network common to both musical and verbal semantic processes. We used a conjunction analysis based on the "valid conjunction inference with the minimum statistic" procedure [START_REF] Nichols | Valid conjunction inference with the minimum statistic[END_REF]. Each comparison in the conjunction was individually significant, corresponding to the valid test for a "logical AND".

Familiarity task

Two contrasts (e.g. Musical-Verbal and Verbal-Musical) were computed for each subject, and all resulting contrast images were then entered into second-level random effect analyses using a one-sample t-test to highlight differences between the verbal and musical semantic networks.

To investigate the effects of familiarity on the parametric modulation of the canonical hemodynamic response function, a parametric regressor modeling familiarity was constructed on the basis of the familiarity judgments for each song excerpt and each French expression in the intra-individual level analysis. Familiarity was rated on a 4-point scale (from unfamiliar (1) to very familiar (4)). This analysis, performed for each subject, allowed us to identify brain areas where activation increased with familiarity for each type of material (MusicFam and VerbalFam). An explicit mask was used to exclude activation due to a motor response.

Then, in a second-level, group analysis, a random effects analysis was conducted using a one sample t-test.

The resulting set of voxel values was threshold at p < 0.001 (uncorrected) and we only report activation involving clusters of voxels above 50. This statistical threshold was chosen in the light of empirical studies showing that this threshold protects against false positives [START_REF] Bailey | A method for measuring the absolute sensitivity of positron emission tomographic scanners[END_REF].

RESULTS

Behavioral data

Congruence task

Mean accuracy of performance was 83.71% (SD = 8.72) for the musical semantic task, 98.71% (SD = 2.85) for the verbal semantic task, and 94.06% (SD = 5.26) and 93.42% (SD = 3.94) for the musical and verbal reference tasks. Accuracy was significantly lower for the musical semantic task than for either the verbal semantic task or either of the reference tasks (p < 0.001). These performances were not significantly different from those of the subjects in our pre-experimental population.

Familiarity task

The statistical analysis (ANOVA) performed on familiarity ratings revealed (1) no main effect of material, F(1, 38) = 1.24, p = 0.27, (2) a main effect of familiarity, F(3, 114) = 38.23, p < 0.001, and (3) a significant effect of the interaction between material and familiarity, F(3, 114) = 34.95, p < 0.001. On the basis of this finding, post hoc comparisons (Tukey"s HSD) were performed in order to identify specific effects. We observed a significant difference (p < 0.001) in the number of verbal and musical stimuli rated as Fam2, Fam3 and Fam4. As might have been expected with nonmusicians, participants found more verbal than musical samples to be "very familiar" (Fam 4).

A similar analysis of response times revealed (1) a main effect of material, F(1, 38) = 165.59, p < 0.001,with longer response times for music than for words, (2) a main effect of familiarity, F(3, 114) = 94.50, p < 0.001, with longer response times when subjects were not sure whether they had heard the item before or not (i.e. Fam 2) and (3) an effect of the interaction between the material and familiarity factors, F(3, 114) = 10.69, p < 0.001. To sum up, the more familiar the stimuli, the shorter the response times for both musical and verbal material.

fMRI data

Congruence task

The musical semantic contrast ([SemMus-RefMus]-[SemVerb-RefVerb], Figure 1, red, Table1) bilaterally activated the superior temporal gyrus (BA 22), extending to the superior part of the middle temporal gyrus, the inferior frontal gyrus (BA 44) (i.e. pars triangularis and orbital part), the inferior part of the parietal lobule (BA 40/39), and the supplementary motor area (upper part of BA 6). On the left side, we also obtained activation of the insula (BA 47) and cerebellum.

The verbal semantic contrast ([SemVerb-RefVerb]-[SemMus-RefMus], Figure 1, blue, Table 1) revealed only left-sided activation, in the angular gyrus (BA 39) (extending to the middle portion of the temporal gyrus), the anterior half of the middle and inferior temporal gyri (BA 21), and the anterior part of the medial frontal cortex, including parts of the orbitofrontal (BA 9/10) and cingulate gyri.

The conjunction analysis (Figure 1, yellow, Table 1) revealed left-sided activation, common to both the verbal and the musical tasks, in the posterior middle temporal area (BA21), the posterior inferior frontal gyrus (BA45/47, including Broca"s area) and, to a lesser extent, the middle frontal gyrus (BA6), and the right cerebellum.

Familiarity task

The musical contrast ([Mus-Verb], Figure 2, red, Table 2) revealed bilateral activation in the superior temporal gyri (BA 22) (extending into Heschl"s gyri), left middle cingulate and right insula (BA 47).

The verbal contrast ([Verb-Mus], Figure 2, blue, Table 2) highlighted mostly left-sided activation, in the inferior and middle temporal gyri (BA 21) (extending to the inferior part of the inferior orbitofrontal gyrus), the medial superior (BA 10) and inferior pars triangularis frontal gyri (BA 45), the posterior middle temporal gyrus and, on the right side, the inferior orbitofrontal cortex (BA 47), the middle temporal pole (BA 38), the anterior part of the hippocampus and the cerebellum.

Effect of familiarity on the parametric modulation of the Hemodynamic Response Function (HRF)

To highlight the effect of familiarity, a parametric regressor modeling familiarity was constructed, in order to highlight those areas where activity increased with familiarity. This analysis revealed greater activity for music (Figure 3, Table 3) in an extended network that included the bilateral inferior frontal gyrus (BA45) (extending to the middle frontal gyrus), the posterior part of the inferior and middle temporal gyri (BA20/37), the medial superior frontal gyrus (BA10) and the right superior temporal pole (BA38). The verbal familiarity analysis revealed greater activity in the left medial superior frontal gyrus (BA10), the middle cingulate cortex bilaterally, the left putamen and thalamus, and the inferior and middle parts of the frontal (BA47/46) and temporal gyri (BA20/21) bilaterally. We also found activation of the posterior part of the right hippocampus and the left parahippocampal cortex.

DISCUSSION

The possibility of there being a neural distinction between verbal and musical material has been raised by several clinical studies (e.g. [START_REF] Signoret | Aphasia without amusia in a blind organist. Verbal alexia-agraphia without musical alexia-agraphia in Braille[END_REF][START_REF] Eustache | Identification and discrimination disorders in auditory perception: a report on two cases[END_REF][START_REF] Peretz | Brain specialization for music[END_REF] but has seldom been investigated in neuroimaging studies. Direct comparisons between language and music have rarely been performed so far and focused mainly on perceptual, production and syntactic processing [START_REF] Patel | Language, music, syntax and the brain[END_REF][START_REF] Koelsch | Interaction between syntax processing in language and in music: an ERP Study[END_REF][START_REF] Brown | Music and language side by side in the brain: a PET study of the generation of melodies and sentences[END_REF][START_REF] Ozdemir | Shared and distinct neural correlates of singing and speaking[END_REF][START_REF] Steinbeis | Comparing the processing of music and language meaning using EEG and fMRI provides evidence for similar and distinct neural representations[END_REF]. According to these studies, a number of brain areas, among which the left prefrontal cortex (BA44/45, [START_REF] Koelsch | Music, language and meaning: brain signatures of semantic processing[END_REF][START_REF] Maess | Musical syntax is processed in Broca's area: an MEG study[END_REF], appear to be involved in both language and music particularly in the syntactic processing. So far, until now, few neuroimaging studies have supported the existence of an independent musical network, corresponding to the musical lexicon postulated by Peretz and collaborators on the basis of neuropsychological observations [START_REF] Peretz | Modularity of music processing[END_REF].

The results of our recent PET activation study suggest that such a distinction does indeed exist for verbal and musical semantic memory processes [START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF]. Using two semantic tasks (congruence and familiarity), we now confirm our previous results and increase the knowledge of the functional organization of the processes involved in musical semantic retrieval. In fact, using fMRI, we revealed partially segregated neural networks for musical and verbal semantic memory retrieval within the frontal and temporal gyri in the left hemisphere, whichever semantic task was used. We first address the neural substrates underlying musical semantic memory and their specific functions. Then, we discuss the cerebral areas shared by the musical and verbal processes and the specific ones.

Musical semantic memory network

Consistent with previous neuroimaging studies using musical semantic memory tasks [START_REF] Satoh | Positron-emission tomography of brain regions activated by recognition of familiar music[END_REF][START_REF] Plailly | The feeling of familiarity of music and odors: the same neural signature?[END_REF][START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF], we observed bilateral activation for musical material, mainly in temporal and frontal areas. The complementary nature of the tasks used in this study, allowed to further highlight the various processes involved in musical semantic retrieval and their neural substrates.

The congruence task, which required participants to decide whether the second part matched (i.e. was the right ending for) the first part or not, revealed ([SemMus-RefMus]-[SemVerb-RefVerb], Figure 1, Table 1) a network encompassing the bilateral inferior frontal (BA 44/45) and superior temporal gyri (BA22), with additional activation in the inferior part of the parietal gyrus bilaterally (BA40) and the left insula (BA47). The familiarity task, which required participants to compare the melodies they heard with those already stored in their semantic memory, revealed adjacent activation in the superior temporal gyri (BA48) bilaterally (Mus-Verb; Figure 2, Table 2) and in the inferior frontal (BA45/47) and medial superior frontal gyri (BA10) on both sides (musical familiarity analysis; Figure 3, Table 3).

The activation of the left frontal cortex has already been highlighted using different types of musical material and experimental paradigms, including a musical recognition task [START_REF] Satoh | Positron-emission tomography of brain regions activated by recognition of familiar music[END_REF][START_REF] Watanabe | Memory of music: roles of right hippocampus and left inferior frontal gyrus[END_REF], a musical familiarity task [START_REF] Platel | The structural components of music perception. A functional anatomical study[END_REF][START_REF] Platel | Semantic and episodic memory of music are subserved by distinct neural networks[END_REF][START_REF] Plailly | The feeling of familiarity of music and odors: the same neural signature?[END_REF] and a musical syntactic task [START_REF] Tillmann | Activation of the inferior frontal cortex in musical priming[END_REF][START_REF] Koelsch | Interaction between syntax processing in language and in music: an ERP Study[END_REF]. In the light of all these findings, the left inferior frontal areas (BA44/45/47) would appear to play a crucial role in musical retrieval processes, possibly reflecting the selection and recapitulation processes recruited to solve the task. While retrieving memory for musical material, the participants had to retain the melody they had heard in order to compare it with those already stored in their semantic memory, and inhibit all irrelevant representations [START_REF] Badre | Left ventrolateral prefrontal cortex and the cognitive control of memory[END_REF]. These are the processes that were predominantly involved in our congruence and familiarity experiments, consistent with the extended activation observed in the left inferior frontal area (Figure 1 and3). In addition, the left inferior frontal activation observed when familiarity increased (Figure 3) could reflect successful recognition in semantic memory, particularly for the anterior part (BA 45) of this area [START_REF] Watanabe | Memory of music: roles of right hippocampus and left inferior frontal gyrus[END_REF][START_REF] Hayama | Right dorsolateral prefrontal cortex is engaged during postretrieval processing of both episodic and semantic information[END_REF]. The right inferior frontal activation observed in both experiments may also have reflected semantic processes, but were probably associated with imagery and internal decision-making processes. The right frontal cortex is known to reflect the musical imagery evoked on hearing a familiar melody [START_REF] Halpern | When that tune runs through your head: a PET investigation of auditory imagery for familiar melodies[END_REF]) and the internal decisions that precede response selection [START_REF] Hayama | Right dorsolateral prefrontal cortex is engaged during postretrieval processing of both episodic and semantic information[END_REF]. Although we selected stimuli with as few emotional associations as possible, listening to musical excerpts inevitably induced various emotional reactions. Our data also revealed activation in areas classically involved in emotion, particularly the insula and the cingulate cortex [START_REF] Blood | Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion[END_REF][START_REF] Brown | Passive music listening spontaneously engages limbic and paralimbic systems[END_REF][START_REF] Stewart | Music and the brain: disorders of musical listening[END_REF].

The activation of the superior temporal gyri (BA 22) obtained in the musical congruence and musical familiarity tasks (Figure 1 and 2) extends our previous results [START_REF] Platel | The structural components of music perception. A functional anatomical study[END_REF][START_REF] Platel | Semantic and episodic memory of music are subserved by distinct neural networks[END_REF][START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF] and is consistent with previous musical memory studies [START_REF] Halpern | When that tune runs through your head: a PET investigation of auditory imagery for familiar melodies[END_REF][START_REF] Satoh | Positron-emission tomography of brain regions activated by recognition of familiar music[END_REF][START_REF] Watanabe | Memory of music: roles of right hippocampus and left inferior frontal gyrus[END_REF]. These areas play a crucial role in musical semantic memory, whatever semantic task is used. The right superior temporal gyrus was mainly highlighted by the familiarity analysis (Music vs. Verbal without subtracting perceptual processes) and may reflect the musical perceptual processes that precede semantic retrieval (see [START_REF] Zatorre | Music and the brain[END_REF]. [START_REF] Chen | Listening to musical rhythms recruits motor regions of the brain[END_REF] reported posterior temporal gyrus activation in the right hemisphere during the perception of and synchronization with musical rhythms, and interpreted it as meaning that this area is a "computational hub". Given that information about rhythm, as well as pitch, serves as the basis for deciding whether or not a melody is familiar [START_REF] Kostic | Song recognition without identification: when people cannot "name that tune" but can recognize it as familiar[END_REF], and given the results of our previous study [START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF], we hypothesize that the right temporal cortex, especially its superior part, is mainly involved in the retrieval of perceptual memory traces.

The left superior temporal gyrus was flagged up in all our previous musical memory studies [START_REF] Platel | The structural components of music perception. A functional anatomical study[END_REF][START_REF] Platel | Semantic and episodic memory of music are subserved by distinct neural networks[END_REF][START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF], as well as in the present congruence and familiarity experiments. This area seems to be linked to access to nonverbal semantic attributes and knowledge of familiar tunes, processes involved in distinguishing between familiar and unfamiliar melodies. Dalla [START_REF] Dalla Bella | Time course of melody recognition: a gating paradigm study[END_REF] suggested breaking the music recognition process down into two stages: access and selection. In the light of our results, we suggest that the left superior temporal gyrus subserves the access stage and the inferior frontal area the selection one.

Musical and verbal memory: two neural networks?

The possibility of there being a neural distinction between music and language was initially suggested by clinical dissociations (for review, see [START_REF] Peretz | Music, language and modularity in action[END_REF]), but has rarely been investigated in neuroimaging studies using comparable semantic memory tasks featuring musical and verbal material. Our previous study [START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF] revealed a neural dissociation between language and music for semantic memory processes. In the present study, we used two complementary semantic tasks to confirm this finding and to understand more fully the functional organization of the various processes involved in semantic retrieval, particularly for musical material.

In the congruence task, the conjunction analysis revealed regions involved in both musical and verbal processing, such as the left frontal inferior area, which is activated by syntactic and semantic tasks featuring both verbal and musical material [START_REF] Platel | Semantic and episodic memory of music are subserved by distinct neural networks[END_REF][START_REF] Koelsch | Significance of Broca's area and ventral premotor cortex for musicsyntactic processing[END_REF][START_REF] Steinbeis | Comparing the processing of music and language meaning using EEG and fMRI provides evidence for similar and distinct neural representations[END_REF][START_REF] Binder | Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies[END_REF][START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF]. Consistent with Brown et al."s proposition (2006), we suggest that the left inferior frontal area (BA 44/45) subserves amodal executive and recapitulation processes involved in performing semantic memory tasks whichever type of material is used. [START_REF] Badre | Left ventrolateral prefrontal cortex and the cognitive control of memory[END_REF] interpreted the role of the inferior frontal cortex as subserving the top-down control of semantic information to perform a memory task. Regarding the existence of interactions between the inferior frontal cortex and the posterior part of the middle temporal lobe during conceptual recognition [START_REF] Kuhl | Strategic control of memory[END_REF], it is not surprising that activation of the left posterior middle temporal cortex also appeared to be common to music and language in our semantic tasks. Our results confirm those reported by [START_REF] Brown | Music and language side by side in the brain: a PET study of the generation of melodies and sentences[END_REF] when they compared music and language in a melody and sentence generation task. These authors found that the posterior part of the left middle temporal gyrus was activated for both language and music, and concluded that this area serves as a key phonological/semantic interface, in verbal semantic processing.

Using two semantic memory tasks in an fMRI investigation, we found that musical semantic memory and verbal semantic memory are subserved by two distinct networks. As explained below, our present study suggested that the musical retrieval network mainly encompasses the bilateral superior temporal areas, while the verbal retrieval network ([SemVerb-RefVerb]-[SemMus-RefMus] and [Verb-Mus], Figure 1, Table 1) comprises the left inferior and middle temporal gyri. These findings are consistent with activation patterns revealed in verbal semantic memory studies (for review, see [START_REF] Cabeza | Imaging cognition II: an empirical review of 275 PET and fMRI studies[END_REF][START_REF] Binder | Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies[END_REF]. Our results support Brown et al."s proposal (2006) that the left superior temporal gyrus is a plausible candidate for representing the semantics of music. Moreover, the verbal familiarity task revealed specific, bilateral activation of the hippocampal areas. These results are consistent with the involvement of the hippocampal structures in familiarity processes postulated by a number of authors [START_REF] Squire | The medial temporal lobe[END_REF][START_REF] Daselaar | Triple dissociation in the medial temporal lobes: recollection, familiarity, and novelty[END_REF][START_REF] Diana | Imaging recollection and familiarity in the medial temporal lobe: a three-component model[END_REF][START_REF] Svoboda | The effects of rehearsal on the functional neuroanatomy of episodic autobiographical and semantic remembering: a functional magnetic resonance imaging study[END_REF][START_REF] Burianova | A common functional brain network for autobiographical, episodic, and semantic memory retrieval[END_REF].

In our previous study [START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF], we suggested that verbal and musical types of material rely on two different networks and proposed an anteroposterior organization within the temporal cortex for semantic concepts, with musical semantic retrieval involving the anterior temporal lobe more than its verbal equivalent. Using a more sensitive neuroimaging technique and two semantic experiments performed by the same population, we were able to confirm the existence of a neural distinction between language and music material during semantic memory retrieval. Overall, our present data suggest that the temporal cortex is organized along an inferior/superior axis depending of the nature of the material being retrieved. The musical material mainly activated the superior temporal cortex, whereas the middle and inferior temporal cortex was activated by the verbal material. Moreover, the anteroposterior organization suggested by our previous study [START_REF] Groussard | Musical and verbal semantic memory: Two distinct neural networks?[END_REF] could be added to the inferior/superior organization. This anteroposterior distribution appears to be dependent on the nature (unique or general) of the semantic representation to retrieve. Thus, the representations of musical material could be considered as unique semantically unique (such as faces or famous buildings) because of their personal specificity, whereas verbal material refers to more general semantic representations shared by everybody and associated with several concepts.

In addition, this study allowed us to show that a number of areas are activated by both verbal and musical semantic memory processes. Verbal and musical semantic retrieval share the same executive and selection processes, which are subserved by the left inferior frontal cortex. This inferior/superior organization within the left temporal cortex could help understanding the clinical dissociations that are observed [START_REF] Piccirilli | Modularity of music: evidence from a case of pure amusia[END_REF] and the disproportionate preservation of musical knowledge in semantic dementia contrasting with severely impaired verbal skills [START_REF] Hailstone | Relatively preserved knowledge of music in semantic dementia[END_REF].

  acquired using a 3D fast field echo sequence (3D-T1-FFE sagittal, TR = 20 ms; TE = 4.6 ms; flip angle = 20°; 170 slices; slice thickness = 1 mm; FOV = 256 x 256 mm²; matrix = 256 x 256; acquisition voxel size = 1 x 1 x 1 mm 3 ), followed by a high-resolution, T2-weighted anatomical image (2D-T2-SE sagittal, SENSE factor = 2; TR = 5500 ms; TE = 80 ms; flip angle = 90°; 81 slices; slice thickness = 2 mm; FOV = 256 x 256 mm²; matrix = 256 x 256; acquisition voxel size = 2 x 1 x 1 mm 3 ) and a non-echo-planar (non-EPI) T2* image (2D-T2*-FFE axial, SENSE factor = 2; TR = 3505 ms; TE = 30 ms; flip angle = 90°; 70 slices; slice thickness = 2 mm; FOV = 256 x 256 mm²; matrix = 128 x 128; acquisition voxel size = 2 x 2 x 2 mm 3 ). Functional data were acquired using an interleaved 2D T2* EPI sequence designed to reduce geometric distortion and magnetic susceptibility artefacts (2D-T2*-FFE-EPI axial, SENSE factor = 2; TR = 2382 ms; TE = 30 ms; flip angle = 80°; 44 slices; slice thickness = 2.8 mm; matrix = 80 x 80; FOV = 224 x 224 mm²; acquisition voxel size = 2.8 x 2.8 x 2.8 mm 3 ; 216 volumes per run for the congruence task and 207 for familiarity). The functional volumes were collected during two functional sessions for each experiment (i.e. congruence and familiarity tasks). The six initial scans of each session were discarded to control for magnetic effects. fMRI data processing Data were analyzed using statistical parametric mapping software (SPM5; Wellcome Department of Cognitive Neurology, Institute of Neurology, London, UK). In the preprocessing steps, images were corrected for slice timing and then realigned on the first volume of the first run. Subsequently, coregistration of the EPI volumes onto the T1 image was done in 3 steps: (1) the non-EPI T2* volume was first coregistered to the mean EPI image for the two runs, (2) the T2 image was then coregistered to the coregistered non-EPI T2* volume, and finally (3) the T1 volume was coregistered to the coregistered T2 image. Images

  performed the direct comparison between the two semantic tasks after subtracting the respective reference tasks [MusSem-MusRef] -[VerbSem-VerbRef] (masked exclusively by the [VerbRef-VerbSem] contrast), to highlight brain activation specifically associated with musical semantic memory processes, excluding both the effects of perceptual activity and contamination by verbal semantic components. We also performed the reverse comparison [VerbSem-VerbRef] -[MusSem-MusRef] (masked exclusively by the MusRef>MusSem] contrast) which we expected to highlight the neural substrates of verbal semantic memory.

Figure 1 :

 1 Figure 1: Brain areas activated during the congruence experiment: activation in the musical semantic contrast (in red), verbal semantic contrast (in blue) and conjunction analysis (in yellow) of musical semantic versus musical reference and verbal semantic versus verbal reference. Contrasts are displayed at p < 0.001 (uncorrected) and superimposed onto an MNI template brain using Anatomist software (www.brainvisa.info).

Figure 2 :

 2 Figure 2: Brain areas activated during the familiarity experiment: activation in the musical contrast (in red) and verbal contrast (in blue). Contrasts are displayed at p < 0.001 (uncorrected) and superimposed onto an MNI template brain using Anatomist software (www.brainvisa.info).

Figure 3 :

 3 Figure 3: The familiarity experiment: effect of familiarity of music (in red) and language (in blue). Contrasts are displayed at p < 0.001 (uncorrected) and superimposed onto an MNI template brain using Anatomist software (www.brainvisa.info).

Table 1 :

 1 Brain regions activated during the congruence experiment. x, y, z coordinates (mm) are given in standard stereotactic MNI space. All regions listed are statistically at the p < 0.001 level (uncorrected).

	Contrast and anatomical location		Cluster size (in voxels)	x	y	z	Z score
	[MusSem-MusRef]-[VerbSem-VerbRef]
	Left superior and middle temporal (BA22)	201		-58	-28	4	5.86
	Right inferior frontal (BA44/45)		1431		58	20	24	5.51
	Left inferior frontal/insula (BA47)		413		-36	26	-4	5.20
	Right superior and middle temporal (BA22)	342		48	-26	-6	4.93
	Left inferior frontal (BA48/44)		763		-52	16	24	4.93
	Right inferior parietal/angular (BA39/40)		503		40	-58	48	4.31
	Left inferior parietal (BA40)		143		-46	-46	48	4.09
	Supplementary motor area (BA6)		159		2	12	62	3.98
	Left cerebellum crus1		89		-34	-68	-24	3.95
	[VerbSem-VerbRef]-[MusSem-MusRef]
	Left angular (BA39)		478		-54	-62	28	5.07
	Left superior frontal (BA9)		498		-12	52	38	4.79
	Left middle temporal (BA21)		339		-56	-4	-20	4.77
	Left middle cingulate		54		-16	-46	32	4.22
	Left medial superior frontral (BA10)		100		-8	58	4	4.09
	Conjunction (MusSem-MusRef) and (VerbSem-Verb Ref)
	Left middle temporal (BA21)		379		-56	-38	-4	5.87
	Left middle frontal (BA6)		252		-28	2	50	4.87
	Left inferior frontal (BA45/47)		490		-46	28	-8	4.60
	Right cerebellum crus1,2		81		12	-82	-28	4.35
	Contrast and anatomical location	Cluster size (in voxels)	x	y	Z	Z score
	Music-Verbal
	Right superior temporal (BA22)	1569		52	-10	2	7.06
	Left superior temporal (BA22)	1876		-46	-16	4	6.99
	Right insula (BA47)	60		36	32	0	4.32
	Left middle cingulate	64		-2	-20	30	4.05
	Verbal-Music
	Left middle temporal (BA21)	4802		-52	4	-20	6.10
	Right inferior frontal (BA47)	185		50	42	-14	5.30
	Left hippocampus anterior part	141		30	-6	-20	4.86
	Left medial superior frontal (BA10)	223		-4	62	32	4.63
	Right middle temporal pole (BA38)	102		50	12	-24	4.48
	Left inferior frontal (BA45)	182		-54	34	12	4.27
	Right cerebellum crus 1	69		30	-78	-28	3.91

Table 2 :

 2 Brain regions activated during the familiarity experiment. x, y, z coordinates (mm) are given in standard stereotactic MNI space. All regions listed are statistically significant at the p < 0.001 level (uncorrected).

	Contrast and anatomical location	Cluster size (in voxels)	x	y	z	Z score
	Musical Familiarity
	Left inferior frontal (BA45)	8947	-52 30	10	5.72
	Right medial superior frontal and cingulate
	(BA8)	2553	8	18	26	5.61
	Right inferior frontal (BA45)	644	60	32	14	4.60
	Right middle temporal (BA20)	134	54	-26	-12	4.46
	Left medial superior frontal (BA10)	86	-12 64	12	4.43
	Right inferior frontal (BA47)	245	42	32	-10	4.14
	Right superior temporal pole (BA38)	155	54	14	-16	4.13
	Left middle and inferior temporal (BA20/37)	277	-58 -48	-12	4.13
	Right middle frontal (BA9)	56	32	34	40	3.91
	Right rectus (BA11)	50	6	34	-20	3.69
	Verbal Familiarity
	Left inferior temporal (BA20)	181	-58 -42	-16	4.88
	Left putamen	581	-32 -10	-6	4.80
	Left medial superior frontal (BA10)	2006	-4	66	10	4.76
	Right middle cingulate	1088	6	-18	34	4.74
	Left inferior and middle frontal (BA47/46)	208	-34 40	6	4.68
	Left middle frontal (BA8)	426	-24 16	64	4.44
	Right hippocampus posterior part	51	28	-34	-4	4.38
	Right middle temporal (BA21)	174	64	0	-26	4.36
	Left middle frontal (BA11)	96	-28 60	-14	4.30
	Left parahippocampal cortex	386	-14 -30	-8	4.19
	Right middle frontal (BA8)	152	34	22	50	4.17
	Right cerebellum crus 1	341	48	-60	-24	4.06
	Left olfactory (BA47)	66	-18 8	-18	3.90
	Left inferior temporal (BA20)	77	-60 -12	-24	3.89
	Right inferior frontal (BA45)	56	50	42	2	3.70

Table 3 :

 3 Activated brain regions representing the effect of familiarity of verbal and musical material. x, y, z coordinates (mm) are given in standard stereotactic MNI space. All regions listed are statistically significant at the p < 0.001 level (uncorrected).

Acknowledgments: This study was supported by a "Music and Memory" French National Research Agency (ANR) grant (NT05-3_45987) and by the French Ministry of Research. We thank N. Villain, P. Gagnepain and G. Chételat for their valuable contribution, and C. Mauger, J. Dayan, C. Schupp and the neuroimaging staff of the Cyceron center for their help with data acquisition.


Nicolas Villain

Brigitte Landeau

Mathilde Groussard

Katell Mevel

Marine Fouquet

Jacques Dayan

Francis Eustache

Béatrice Desgranges

Gaël Chételat
email: <chetelat@cyceron.fr

B Atrice Desgranges

Ga L Ch

A simple way to improve anatomical mapping

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Functional MRI (fMRI) is now the most widely used technique for functional neuroimaging studies of the human brain. The anatomical interpretation of fMRI results, termed as activations , almost systematically depends on their projection onto anatomical " "

T1-MRI images. However, while the focus of pioneer fMRI studies did not require highly specific anatomical accuracy in the location of activations, parallel advances in human functional brain research and neuroimaging techniques have progressively led to the need for improved anatomical precision. As examples, recent fMRI investigations focused on the differential functional connectivity of sixteen subparts of the anterior cingulate gyrus , or aimed at highlighting the differential roles of the hippocampal subfields or of eight 1 2 subregions of the left frontal inferior gyrus in memory and language functions, respectively. 3

Unfortunately, fMRI data suffer from geometrical distortions, whose intensity may vary according to the Echo-Planar Imaging (EPI) sequence and the brain region, but which anyway limit the anatomical accuracy of resulting fMRI findings. Thus, even with the most accurate EPI-to-T1 rigid coregistration, functional and anatomical images are imperfectly matched as they do not have the same shape. As a consequence, the anatomical interpretation of fMRI findings may be tricky since some findings, especially those located in brain regions suffering from high geometrical distortions (such as anterior brain areas), would appear mislocated onto the T1 image. Many illustrations of this issue can be found in the current literature, such as activations supposed to be located in the anterior cingulate cortex but that clearly appeared into the corpus callosum genu when superimposed onto T1 anatomical images (see .a, cluster A in Vanderwal et al. Figure 2 (2008) ; and and from [START_REF] Margulies | Mapping the functional connectivity of anterior cingulate cortex[END_REF] , for instance). Although activations were not misinterpreted in these studies, [START_REF] Vanderwal | Self, mother and abstract other: an fMRI study of reflective social processing[END_REF] Figures 4 [START_REF] Ch Telat É G | Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer s disease[END_REF] 1

these examples illustrate the potential gap between genuine and apparent locations of activations that may blur fMRI data interpretations and limit current and future investigations. Besides, recently emerging multi-modal voxel-based neuroimaging studies as well as tools 5

specifically designed for direct voxel-based comparisons between fMRI and morphometry (VBM) data , further stress on the need for an 6 accurate matching between functional and anatomical MRI data.

Although still only occasionally applied, different methods have been proposed to correct for geometrical distortions, the most-widely used being the FieldMap procedure , . This method relies on a measured field map for the object inside the scanner to compute the ' ' 7 8 existing distortions in the EPI images according to the relationships between spatial distortion and field inhomogeneity thanks to specific algorithms. This procedure depends upon the acquisition of two non-EPI T2 Star images with different echo time. It seems of interest to propose an alternative method that could be used in situations where field maps have not, or cannot, be collected or when the FieldMap ' ' method fails to provide correct field map estimation.

Our purpose with this study is thus to propose an alternative method very simple to implement as it only relies on a single non-EPI T2

Star image and on algorithms widely implemented and already used in most neuroimaging laboratories. This method is based on roughly warping distorted EPI data onto corresponding undistorted non-EPI T2 Star image. This procedure was tested on two independent groups of subjects undertaking the same paradigm but scanned with distinct EPI sequences showing different sensitivity to geometrical distortions.

Materials & Methods

Participants

Twenty-four right-handed native French-speaking participants were included in our study (11 males; 13 females; mean age: 30.0 6.5).

±

Subjects had no history of metabolic, psychiatric, serious head injury or neurological illness as confirmed by neurological clinical assessment. Signed informed consent was obtained prior to participation. The study was approved by the local ethical committee (CPP Nord-Ouest III) and was done in line with the declaration of Helsinki.

Design and Task

We designed an event-related self-referential paradigm known to consistently induce strong activations in the cortical midline structures including regions suffering from geometrical distortions such as the anterior cingulate gyrus and the medial prefrontal cortex9

Data acquisition

A Philips (Eindhoven, The Netherlands) Achieva 3.0 T scanner from the GIP Cyceron (Caen, France) was used for data acquisition.

For each participant, a high resolution T1-weighted anatomical image was first acquired using a 3D fast field echo sequence (3D-T1-FFE sagittal, TR 20 ms; TE 4.6 ms; flip angle 20 ; 170 slices; slice thickness 1 mm; no gap; FoV 256 256 mm ; matrix 256 256; in-plane

= = = °= = × 2 = ×
resolution 1 1 mm ), followed by a high resolution T2-weighted spin echo anatomical acquisition (2D-T2-SE sagittal, SENSE factor 2; Pre-processing and analysis of fMRI data EPI data were first checked for the lack of artifacts through the creation of variance volume for each run and each subject, allowing to ensure that most signal variability was restricted to the cortex. We also applied TSDiffana ( ) to each functional raw volume to confirm the lack of any isolated artifact into http://imaging.mrccbu.cam.ac.uk/imaging/DataDiagnostics the images.

The pre-processing steps of fMRI data, detailed and illustrated in , were performed using SPM5 (Statistical Parametric Figure 1 Mapping; Wellcome Dept of Cognitive Neurology, London, UK) and applied to the two data samples with distinct EPI sequence. For each data set, two pre-processing procedures were applied, the first one corresponding to the standard pre-processing procedure while the second one included a supplementary step intended to reduce geometrical distortions.

Standard pre-processing procedure ( ) Figure 1A andB After discarding the six first EPI volumes of each run (to allow for magnetic saturation effects), EPI volumes of each subject were corrected for slice timing and then realigned on the first volume of the first run. Individual T1-to-EPI rigid coregistration was then performed as followed: first, the mean EPI volume of each subject was rigidly coregistered onto the non-EPI T2Star acquisition; second, the non-EPI T2Star volume was rigidly coregistered onto the T2 image; and third the T2 volume was rigidly coregistered onto the T1 acquisition. The coregistration parameters resulting from these three steps were successively applied to all EPI volumes and those resulting from the third step were also applied to the non-EPI T2Star image (see ). The T1 image was then segmented/normalized using the Figure 1 SPM5 Segment procedure with the ICBM/MNI priors and the resulting parameters (of the so-called T1-to-MNI normalization) were

' '
15 applied to the coregistered T1, EPI, and non-EPI T2 Star volumes. Since the two data sets with distinct EPI sequences had different original spatial resolutions, differential smoothing was applied in order to obtain images of equivalent effective smoothness, and thus of identical resultant resolution , . To this end, we used an isotropic Gaussian kernel of 7.72 mm FWHM for the standard EPI sequence, 5 16 resulting in an effective smoothness identical to the SENSE EPI sequence images smoothed at 8 mm FWHM . 17

Additional EPI-to-non-EPI non-linear registration step ( ) Figure 1C

Non-EPI T2 Star volumes are not altered by geometrical distortion as they do not rely on a low bandwidth EPI acquisition, but they suffer from magnetic susceptibility artifacts since they are sensitive to the transverse magnetization decay of the signal measured with gradient echo sequences. Consequently, their global shape is a good approximate of the theoretical shape of undistorted EPI images. For this reason, we proposed here to correct for geometrical distortion by roughly warping distorted EPI images onto corresponding non-EPI T2 star image so that their global shape would tend to be similar to their theoretical undistorted one. Thus, after T1-to-EPI rigid coregistration, the mean EPI image was warped to roughly match the non-EPI T2 Star volume using the SPM5 Normalize: Estimate To avoid an extra-reslicing step as compared to the standard procedure, the so-called EPI-to-non-EPI warping parameters were combined to the T1-to-MNI normalization parameters described above, and applied to the coregistered T1, EPI, and non-EPI T2 Star volumes using the SPM5 Deformation Toolbox . Resulting images were finally smoothed as described above for the standard procedure.

' '

Display of results and statistical analyses

First, in order to provide an overview of the warps induced by the EPI-to-non-EPI non-linear registration, warping parameters were transformed to jacobian determinants. The jacobian maps of each subject are first transformed (by subtracting one) so that negative values reflect expansion and positive values contractions, and then normalized onto MNI and smoothed at 8 mm FWHM. Areas of significant expansion (negative jacobians) and contraction (positive jacobians) were then assessed using a one-sample t-test for each EPI sequence group.

Second, in order to quantify the effect of the EPI-to-non-EPI non-linear registration step onto anatomical accuracy, we used the position of the most anterior voxel of the corpus callosum genu as an anatomical landmark. This landmark was selected because it is both expected to be concerned by geometrical distortion and easily identifiable on both T1 and EPI sequences. Indeed, due to magnetic susceptibility artifacts in EPI but not T1 images and to differences between T1 and EPI images in terms of intensity, resolution and contrast, we failed to find another reliable landmark that could be easily and accurately applied to both datasets. Thus, y and z MNI coordinates of the position of the most anterior voxel of the corpus callosum genu were obtained for each subject from the inter-hemispheric sagittal slice of the unsmoothed MNI-normalized T1, mean EPI, and mean EPI warped onto the non-EPI T2 Star volume. The Euclidian distances between the position of the corpus callosum genu in each of the two mean EPI images and that in the corresponding T1 volume were then calculated for each subject. These values were then entered in a 2 2 repeated-measures ANOVA × including the pre-processing procedure (with or without the EPI-to-non-EPI nonlinear registration step) as a repeated factor and the EPI ' '

sequence group (standard or SENSE) as a classification factor, and analyzed with the Statistica software (Statsoft, Tulsa, OK) to assess whether the additional EPI-to-non-EPI non-linear registration step significantly reduced the mismatch between anatomical and functional images.

Third, the effect of the additional EPI-to-non-EPI non-linear registration step was assessed onto activation maps. For this purpose, statistical analyses were conducted on functional images using SPM5 and the general linear model approach on a voxel-by-voxel basis with a random effects model implemented with a two-level procedure. Six different experimental conditions were modeled as functions δ at each stimulus onset according to the valence of the adjective (positive or negative) and the nature of judgment ( , and Self Other

). The ensuing hemodynamic response was modeled by convolving these functions with a canonical hemodynamic response Semantic δ function.

A single minus contrast was computed for each subject, and all resulting contrast images were then entered into

"Self
Semantic "

second-level random effect analyses. First, independent one-sample t-tests were performed for each methodological condition allowing to compare standard to modified pre-processing procedures and to confirm our prediction of lower geometrical distortions for the SENSE sequence by contrasting the standard and SENSE EPI sequence samples. Second, so as to directly and statistically assess the effect of the EPI-to-non-EPI non-linear registration step according to the EPI sequence samples, a 2 2 repeated-measures ANOVA including the × '

pre-processing procedure (including or not the EPI-to-non-EPI non-linear registration step) as a repeated factor and the EPI sequence ' group (standard or SENSE) as a classification factor was also performed using the SPM5 Flexible factorial design procedure.

' '

For all these analyses, only regions surviving a False Discovery Rate (FDR)-corrected p < .05 threshold with an extension of at least 20 contiguous voxels were considered as significant.

Results

Observation of geometrical distortions induced by the EPI-to-non-EPI non-linear registration ( ) Figure 2 The jacobian determinants of the EPI-to-non-EPI non-linear registration showed that the distortion correction included expansions within the dorso-lateral frontal cortex, medial and lateral parietal areas, as well as deep gray matter nuclei, temporal poles, medial temporal lobes and anterior cingulate cortex, and contractions in the occipital, orbito-frontal, medial prefrontal, retrosplenial and lateral temporal cortices. Although slightly higher in the group with the standard sequence, the jacobians appeared highly similar in terms of location and direction (contraction or expansion) in both EPI sequence groups.

Assessment of geometrical distortions from EPI images

EPI images (colorscale) were superimposed on T1 volumes (grayscale) to compare the mismatches between these two acquisitions (and thus the degree of geometrical distortion of EPI images) before and after distortion correction (

). This display shows Figure 3 mismatches in the temporal poles, caudate nuclei and orbito-frontal cortex (blue arrows on

). These mismatches were found to be Figure 3 reduced with either the SENSE sequence or the EPI-to-non-EPI non-linear registration and all the more with both factors combined.

The effect of the EPI-to-non-EPI non-linear registration is illustrated in the native space of a single subject who underwent both EPI sequences (

), and on the two groups of subjects with different EPI sequence in the MNI space ( ). Single subject displays Figure 4 Figure 5 show that, after a single rigid T1-to-EPI coregistration (as performed in the standard pre-processing procedure), the corpus callosum genu of the EPI image is located behind the corpus callosum genu of the T1 image ( , red arrows), and relatively large distortions appear Figure 4 in the global shape of the parieto-occipital lobes on the EPI volume compared to the T1 image ( , blue arrows). These two effects Figure 4 are lessened with either the SENSE sequence or the EPI-to-non-EPI non-linear registration and even more when both factors are combined.

illustrates the effect of the EPI-to-non-EPI non-linear registration for the two groups of subjects by reporting a reference point Figure 5 from the mean T1 image onto the EPI images of each group (red crosses). With the standard pre-processing procedure, the corpus callosum genu on the mean T1 volume (y 34) appears up to 8 mm forward (y 26) onto the mean EPI image of the group with the standard

= =
EPI sequence, and up to 4 mm forward (y 30) in the group with the SENSE EPI sequence (blue arrows). Introducing the EPI-to-non-EPI = non-linear registration reduces this difference as only a 2 mm (y 32) forward gap is observed in the group with the standard EPI sequence = while there is no more gap in the group with the SENSE EPI sequence ( , blue arrows). Figure 5 These visual assessments were confirmed by the ANOVA on the individual measures of the Euclidian distance between the corpus callosum genu coordinates on the EPI and that on the T1 images, which revealed a significant effect of the pre-processing procedure (F [START_REF] Margulies | Mapping the functional connectivity of anterior cingulate cortex[END_REF]22) after the EPI-to-non-EPI non-linear registration ( ). To assess the reliability of this measure, the coordinates were measured a Figure 6 second time by the same rater. The mean Euclidian error distance between the two measures was 1.11 ( 1.06) mm for T1 volumes and ± 1.59 ( 1.24) mm for EPI volumes which is inferior to the in-plane resolution of the images used for these measures (2 2mm ).

± ×

Assessment of geometrical distortions from activation maps

illustrates one-sample t-tests representing main effects of the EPI sequence group and the pre-processing procedure from Figure 7 group analyses projected onto corresponding T1 and EPI images. This Figure shows, first, some activations located onto the corpus callosum genu after anatomical rendering onto the T1 when using the standard pre-processing procedure, while they are correctly rendered onto anterior cingulate cortex of the T1 anatomical image when introducing the EPI-to-non-EPI non-linear registration step. Second, effects of EPI-to-non-EPI non-linear registration appear increased for the group with the standard EPI sequence compared to the group with the SENSE EPI sequence.

Finally, the voxel-based ANOVA revealed a significant effect of the pre-processing procedure while no voxel reached significance for the main effect of the EPI sequence group or for the interaction between the EPI sequence group and the pre-processing procedure.

Post-hoc comparisons between standard and modified procedures more specifically pointed, mainly, to increased activations into the corpus callosum genu and anterior cingulate gyrus using the standard procedure, while the modified procedure led to significantly higher activation in the medial part of the superior frontal gyrus (see ). Note that differences were also observed in smaller and less Figure 8 significant clusters, with decreased activations with the modified compared to the standard procedure in a very anterior part of the temporal pole and the right lateral parietal cortex and increased activations in a more posterior part of the temporal pole (

). Figure 8 Discussion

The objective and quantitative assessment of geometrical distortions of EPI images is a complex issue. In the present study, the comparison of the degree of image distortion between different conditions was based on both visual assessment and quantitative measurement. First, we simply visually assessed the location and direction of the distortions induced by the method proposed in this study to correct for EPI geometrical distortions (

). Second, we used both individual and group displays illustrating the mismatches Figure 2 between anatomical and functional data reflecting the degree of distortions that can occur individually (as for the parieto-occipital lobes on ) on the one hand, and on group findings on the other hand ( , and ). Third, we used the differences between the Figure 4 Figures 3 5 7 position of the corpus callosum genu on the T1 image and that on the EPI images as a quantitative measure of these mismatches. Finally, we also performed statistical analyses onto these measures so as to provide statistical insights onto geometrical distortion effects (Figures 6 and). 8

As expected, the group with the standard EPI sequence showed the highest geometrical distortions, as indicated by the degree of parieto-occipital distortions ( ), of temporal pole and deep gray matter mismatches ( ) and by the difference between the Figure 4 Figure 3 corpus callosum genu position on EPI and that on T1 images ( , , and ). Consistently, the standard EPI sequence also Figures 4 5 6 7 appeared to be more sensitive to the EPI-to-non-EPI non-linear registration ( and ), indicating higher differences between EPI Figures 2 7

and non-EPI T2 Star images and thus greater distortions. Nonetheless, no statistically significant effect of the EPI sequence group was found in our analyses which may arise from our limited sample sizes.

The EPI-to-non-EPI non-linear registration was found to strongly reduce the geometrical distortions for both EPI sequence groups. Indeed, this procedure decreased parieto-occipital distortions ( ), mismatches between T1 and EPI images in temporal poles and Figure 4 deep gray matter nuclei (

) and the gap in the corpus callosum genu position onto EPI and T1 images ( , , and ). Figure 3 Figures 4 5 6 7 More specifically, the gap was found to be quantitatively lowered by about 50 percent after EPI-to-non-EPI non-linear registration for both groups with the standard and SENSE EPI sequences ( ), and this reduction was found to be statistically significant. The effect of Figure 6 EPI-to-non-EPI non-linear registration was also visible on anterior cingulate activations: these activations overlapped the corpus callosum of the T1 using the standard procedure while they accurately matched the anterior cingulate gyrus of the T1 with the modified procedure ( ). Finally, the voxel-based ANOVA on activation maps revealed significant activation decrease in the corpus callosum genu after Figure 7 the EPI-to-non-EPI non-linear registration, while a significant increase was observed into the most anterior part of the frontal lobe. This result provides statistical insight on the mislocation of anterior cingulate and medial-prefrontal EPI activations onto the T1 image when using the standard pre-processing procedure (

). The effects found in the same analysis within the temporal pole and lateral Figure 8 parietal cortex are consistent with the anatomical mismatches observed in these two regions ( and ). Altogether these results Figures 3 4

highlight both the efficiency of the EPI-to-non-EPI non-linear registration to correct for EPI geometrical distortions, providing improved matching between anatomical and functional MRI data, and its robustness over different EPI sequences.

The method proposed here, instead of calculating distortion maps as in the FieldMap procedure , , roughly corrects the distorted

' ' 7 8
EPI images according to their theoretical undistorted global shape provided by the non-EPI T2 star image. Thus, even if it may not fully remove the geometrical distortions since it does not rely on an accurate measure of these distortions, our procedure appears as an efficient alternative, that may notably prove to be useful if the FieldMap method fails to provide correct field map estimation (e.g. in cases of noisy ' '

acquisitions with unusable phase images leading to incorrect field map estimation) or if field maps have not, or cannot, be collected.

Indeed, the SPM Normalize algorithm is designed to be as independent as possible to the type and noise properties of the images and the 18 degree of freedom of the algorithm was not found to influence the distortion correction through this method (data not shown). Moreover, this non-linear registration procedure is both fast and easy to implement as it simply relies on Normalize algorithms, which are widely implemented in brain analysis software, and acquisition of a single non-EPI T2 Star volume, which only lasted three minutes in the present study. Note that we did not specifically assess the effects of using different Normalize algorithms here, although the lack of significant influence of the number of degrees of freedom suggest they would be minimal.

EPI geometrical distortions are in-plane and in one direction (the phase encoding direction), and contrary to the FieldMap procedure, ' '

the calculated warps with the method proposed here can be out of plane and in any direction. Hence, the use of in-plane non-linear registration algorithms could help to deal with that limitation and would improve this method. Nevertheless, visual assessments of the warps induced by our correction method showed no evidence of aberrant distortion corrections ( , , , and ). Figures 2 3 4 5 7

Finally, distortion correction was also found to have an effect on other brain structures, such as the medial temporal and the occipital lobes (see ). We failed to provide quantitative information onto the degree of distortion and the effect of correction for other brain Figure 2 location than the corpus callosum genu (see Materials & Methods), which is a limitation of the current study. However, yet only based on visual/qualitative assessment, our findings in the medial temporal and the occipital lobes further reinforce the relevance of distortion correction for fMRI paradigms involving these areas.

As a conclusion, these findings altogether provide strong evidence for the effectiveness of the EPI-to-non-EPI non-linear registration to decrease geometrical distortions of EPI images, and thus to improve anatomical accuracy of EPI-based fMRI results. While more sophisticated methods might prove to be more accurate, this method is interestingly very simple, easy to implement and widely available.

Thus, the EPI-to-non-EPI non-linear registration procedure, is an easy way to improve the anatomical accuracy of fMRI results labelization and interpretation, and may be useful for current and future research challenges.

Footnotes:

No conflict of interest to disclose.

= × 2 = 2 = 3 . 2 " 2 volumes

 22322 TR 5500 ms; TE 80 ms; flip angle 90 ; 81 slices; slice thickness 2 mm; no gap; FoV 256 256 mm ; matrix 256 256; in-plane ) and a non-EPI T2 Star image (2D-T2 Star-FFE axial, SENSE factor 2; TR 3505 ms; TE 30 ms; flip angle 90 ; 70 mm; no gap; FoV 256 256 mm ; matrix 128 128; in-plane resolution 2 2 mm ). Functional acquisitions of ; mean age: 30.0 7.3) were done using an interleaved 2D T2 Star EPI sequence (2D-T2 Star-FFE-EPI axial, TR ± = 2200 ms; TE 35 ms; flip angle 80 ; 35 slices; slice thickness 3.5 mm; no gap; matrix 64 64; FoV 224 224 mm ; in-plane resolution 5 3.5 mm ; 185 volumes per run) termed as standard EPI sequence in what follows. Twelve additional subjects (5 males; mean age: × " 30.1 6.0) underwent an interleaved 2D T2 Star SENSE (SENSitivity Encoding) EPI sequence designed to reduce geometrical distortions ± by using parallel imaging, shorter echo time and smaller voxels (2D-T2 Star-FFE-EPI axial, SENSE factor 2; TR 2382 ms; TE 30 ms; = = = flip angle 80 ; 44 slices; slice thickness 2.8 mm; no gap; matrix 80 80; FoV 224 224 mm ; in-plane resolution 2.8 2.8 mm ; 172 per run), and termed as the SENSE EPI sequence . " "

  and Template images Smoothing: 4mm FWHM; Affine regularization: Average sized template).

  Nationale de la Recherche, Long vit et Vieillissement, 2007), Inserm, including Inserm-Liliane Bettencourt Funding Source: é é MD-PhD Program and R gion Basse-Normandie. é We acknowledge the financial/scientific support of the ANR (Agence Nationale de la Recherche, Long vit et Vieillissement, 2007), Inserm, é é including Inserm-Liliane Bettencourt MD-PhD Program and R gion Basse-Normandie for this project. We are grateful to the two anonymous é reviewers for their helpful comments regarding this manuscript. We thank R. La Joie and C. Duval for their time and help in preliminary tests of the fMRI paradigm, F. Lamberton and N. Delcroix for their help with the EPI sequences and the aborted FieldMap corrections, F. M zenge é and A. P lerin for help with recruitment of subjects, G. Perchey and M.H. No l for help with neuroimaging data acquisition, and the é ëvolunteers of this study.

  18 . Ashburner JT , Friston KJ . Editor: Friston KJ , Ashburner JT , Kiebel SJ , Nichols TE , Penny WD . Non-linear Registration . Statistical Parametric Mapping -The Analysis of Functional Brain Images . 1 Amsterdam Elsevier ; 2007 ; 63 -80

Figure 5 Figure 6 Figure 7 Figure 8

 5678 Figure 5

  26.91, p< .001). The effect of the EPI sequence group (F(1,22) 2.35, p> .1), and the interaction between EPI sequence group and

= =

pre-processing procedure, failed to reach significance (F(1,22) 0.26, p> .6) ( ). Quantitatively, the mean Euclidian distance = Figure 6 between the position of the corpus callosum genu on the EPI and that on the T1 images dropped from 5.97 ( 2.39) mm to 2.77 ( 1.33) mm ± ± for the group with the standard EPI sequence and from 5.08 ( 1.54) mm to 2.45 ( 1.20) mm for the group with the SENSE EPI sequence ± ±

Tomogr . 1995 ; 19 : 788 -796