Emilie Salvia
email: emilie.salvia@parisdescartes.fr

Cloélia Tissier

Sylvain Charron

Paul Herent

Julie Vidal

Stéphanie Lion

Mathieu Cassotti

Catherine Oppenheim

Olivier Houdé

Grégoire Borst

Arnaud Cachia

The local properties of bold signal fluctuations at rest monitor inhibitory control training in adolescents

Keywords: Inhibitory control, Training, Adolescence, IFG, ACC, Striatum

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

INTRODUCTION

Inhibitory control (IC) -the ability to resist automatisms, temptations, distractions or interferences and to adapt to conflicting situations [START_REF] Diamond | Executive Functions[END_REF] -plays a critical role in cognitive and socio-emotional development [START_REF] Borst | Inhibition of misleading heuristics as a core mechanism for typical cognitive development: evidence from behavioural and brain-imaging studies[END_REF]. Indeed, IC abilities in childhood and adolescence predict later academic and professional success [START_REF] Diamond | Executive Functions[END_REF][START_REF] Kim | Effortful Control in "Hot" and "Cool" Tasks Differentially Predicts Children's Behavior Problems and Academic Performance[END_REF]. Several studies have also shown that the development of complex skills, such as arithmetic or reading [START_REF] Blair | Relating Effortful Control, Executive Function, and False Belief Understanding to Emerging Math and Literacy Ability in Kindergarten[END_REF][START_REF] Christopher | Predicting word reading and comprehension with executive function and speed measures across development: a latent variable analysis[END_REF][START_REF] Cragg | Skills underlying mathematics: The role of executive function in the development of mathematics proficiency[END_REF], reasoning (Houdé & Borst, 2015), theory of mind [START_REF] Marcovitch | A longitudinal assessment of the relation between executive function and theory of mind at 3, 4, and 5 years[END_REF], decision-making [START_REF] Cassotti | What have we learned about the processes involved in the Iowa Gambling Task from developmental studies[END_REF] and creativity [START_REF] Cassotti | Inhibitory Control as a Core Process of Creative Problem Solving and Idea Generation from Childhood to Adulthood[END_REF], rely on the increased efficiency of IC. IC is thus considered a core mechanism of typical as well as atypical development [START_REF] Borst | Inhibition of misleading heuristics as a core mechanism for typical cognitive development: evidence from behavioural and brain-imaging studies[END_REF][START_REF] Casey | The Teenage Brain: Self Control[END_REF][START_REF] Diamond | Executive Functions[END_REF].

Several studies have shown that the ability to solve a conflict in affectively neutral contexts (also referred to as 'Cool' IC, [START_REF] Aïte | Adolescents' inhibitory control: keep it cool or lose control[END_REF][START_REF] Prencipe | Development of hot and cool executive function during the transition to adolescence[END_REF] develops linearly from childhood to adulthood in close relation to the protracted maturation of the prefrontal cortex (PFC, [START_REF] Gogtay | Dynamic mapping of human cortical development during childhood through early adulthood[END_REF][START_REF] Luna | What has fMRI told us about the Development of Cognitive Control through Adolescence?[END_REF][START_REF] Luna | An Integrative Model of the Maturation of Cognitive Control[END_REF]. Indeed, IC relies on the activation of a number of PFC sub-regions (see recent fMRI meta-analyses: [START_REF] Criaud | Have we been asking the right questions when assessing response inhibition in go/no-go tasks with fMRI? A meta-analysis and critical review[END_REF]Simmonds et al., 2008;[START_REF] Swick | Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks[END_REF], namely, the dorsolateral PFC, the ventrolateral PFC, the inferior frontal cortex (IFG), especially in the right hemisphere, the anterior part of the insula, and the anterior cingulate cortex (ACC). IC also elicits activation in other brain regions, such as the parietal cortex [START_REF] Criaud | Have we been asking the right questions when assessing response inhibition in go/no-go tasks with fMRI? A meta-analysis and critical review[END_REF], the subthalamic nucleus [START_REF] Aron | Cortical and Subcortical Contributions to Stop Signal Response Inhibition: Role of the Subthalamic Nucleus[END_REF][START_REF] Aron | Frontosubthalamic Circuits for Control of Action and Cognition[END_REF] and the striatum (i.e., caudate nucleus and putamen) [START_REF] Beste | Striatal Microstructure and Its Relevance for Cognitive Control[END_REF]. Striatal regions, classically involved in learning [START_REF] Seger | The Basal Ganglia in Human Learning[END_REF][START_REF] Seger | How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback[END_REF][START_REF] Seger | The Roles of the Caudate Nucleus in Human Classification Learning[END_REF][START_REF] Seger | The Basal Ganglia in Human Learning[END_REF], are also involved in IC partly because of their structural and functional connectivity to the PFC [START_REF] Beste | Striatal Microstructure and Its Relevance for Cognitive Control[END_REF].

The early development of limbic regions, such as the ventral striatum and the amygdala [START_REF] Chein | Peers increase adolescent risk taking by enhancing activity in the brain's reward circuitry[END_REF], contributes to making adolescence a specific period with higher emotional reactivity and lower IC abilities in affectively charged contexts [START_REF] Hare | Biological Substrates of Emotional Reactivity and Regulation in Adolescence During an Emotional Go-Nogo Task[END_REF][START_REF] Somerville | Frontostriatal Maturation Predicts Cognitive Control Failure to Appetitive Cues in Adolescents[END_REF]. Several studies have investigated the developmental trajectory of the abilities to solve a conflict in an affectively charged context (also referred to as 'Hot' IC). In contrast to the linear development of Cool IC, Hot IC follows a non-linear inverted U-shaped developmental trajectory, in which adolescents are less effective than children and adults in solving conflicts in affectively charged contexts [START_REF] Aïte | Adolescents' inhibitory control: keep it cool or lose control[END_REF]. These results are consistent with the dual system neurodevelopmental models [START_REF] Shulman | The dual systems model: Review, reappraisal, and reaffirmation[END_REF] supporting an adolescent-specific developmental trajectory of Hot IC. For instance, the "maturational imbalance" model [START_REF] Casey | The Teenage Brain: Self Control[END_REF] proposes that lower Hot IC at adolescence would result from an imbalance between a hypersensitivity of the limbic network (involved in emotions) and the immaturity of the PFC (involved in the control of emotions). More recently, the "driven dual systems" model [START_REF] Luna | Adolescent brain development: Implications to the juvenile criminal justice system[END_REF] emphasizes the role of a hyperactive socioemotional system associated with an inverted U-shaped trajectory of the subcortical limbic system (e.g., ventral striatum) but with cognitive control that would plateau in mid-adolescence rather than continuing to increase until early adulthood.

IC abilities develop with age but may also be improved by cognitive training [START_REF] Dowsett | The development of inhibitory control in preschool children: Effects of "executive skills" training[END_REF][START_REF] Zhao | Training and transfer effects of response inhibition training in children and adults[END_REF].

Previous studies on typically developed children used IC training of medium duration (5 to 10 h over several weeks) and child friendly versions of the Stop Signal or the Go-NoGo tasks. Some studies reported no transfer, namely, IC training induced improvements on the trained tasks but not on the nontrained tasks [START_REF] Thorell | Training and transfer effects of executive functions in preschool children[END_REF], while other studies detected a transfer, namely, an increased performance after IC training in not only the trained tasks but also the non-trained tasks (non-trained tasks involving either other executive functions such as working memory and task switching [START_REF] Zhao | Training and transfer effects of response inhibition training in children and adults[END_REF] or abstract reasoning abilities [START_REF] Liu | The effects of inhibitory control training for preschoolers on reasoning ability and neural activity[END_REF]). Of note, the results showed that such transfers on non-trained tasks had short-lived effects, i.e., they did not persist and were no longer evident 6 months after the IC training [START_REF] Zhao | Training and transfer effects of response inhibition training in children and adults[END_REF]. IC training in children was also associated with brain changes. Using EEG, [START_REF] Liu | The effects of inhibitory control training for preschoolers on reasoning ability and neural activity[END_REF] found, particularly in girls, a specific effect on the N2 component, a classical neuro-functional index of IC [START_REF] Daurignac | Negative Priming in a Numerical Piaget-like Task as Evidenced by ERP[END_REF][START_REF] Jonkman | Source localization of the Nogo-N2: A developmental study[END_REF].

In adults, previous studies showed that IC training of short (i.e., 1 h) to medium duration (i.e., 5 to 10 h over several weeks) -based on the Stop Signal, Go-NoGo, Stroop or Simon tasks -led to increased performance either in the trained task only [START_REF] Berkman | Training-Induced Changes in Inhibitory Control Network Activity[END_REF][START_REF] Manuel | Plastic modifications within inhibitory control networks induced by practicing a stop-signal task: an electrical neuroimaging study[END_REF][START_REF] Talanow | Effects of task repetition but no transfer of inhibitory control training in healthy adults[END_REF][START_REF] Zhao | Training and transfer effects of response inhibition training in children and adults[END_REF] or also in non-trained tasks, involving IC abilities [START_REF] Millner | Behavioral and electrophysiological correlates of training-induced cognitive control improvements[END_REF] or decision-making processes [START_REF] Verbruggen | Proactive Motor Control Reduces Monetary Risk Taking in Gambling Proactive Motor Control Reduces Monetary Risk Taking in Gambling[END_REF]. Such IC training in adults also led to functional brain changes in regions of the IC network [START_REF] Berkman | Training-Induced Changes in Inhibitory Control Network Activity[END_REF][START_REF] Chavan | Differential patterns of functional and structural plasticity within and between inferior frontal gyri support training-induced improvements in inhibitory control proficiency[END_REF][START_REF] Manuel | Plastic modifications within inhibitory control networks induced by practicing a stop-signal task: an electrical neuroimaging study[END_REF][START_REF] Spierer | Training-induced behavioral and brain plasticity in inhibitory control[END_REF] and parietal cortices [START_REF] Manuel | Brain dynamics underlying training-induced improvement in suppressing inappropriate action[END_REF]. Furthermore, behavioural changes in a trained task, i.e., the Stop Signal Task (SST), were found to be related to activity changes in the right IFG [START_REF] Berkman | Training-Induced Changes in Inhibitory Control Network Activity[END_REF]. No transfer of such a non-affective training task to a non-trained affective task (emotion regulation task) was observed at the behavioural level [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF]. However, brain activation changes, in regions associated with training on the SST [START_REF] Berkman | Training-Induced Changes in Inhibitory Control Network Activity[END_REF], during the execution of an emotion regulation task, appear to be the result of an SST-based IC training [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF], suggesting that the brain is subject to fast plastic changes. Brain changes following IC training are not limited to changes (either increases or decreases) in local activity but also to changes in long-distance functional connectivity during IC tasks [START_REF] Hu | Neural interactions mediating conflict control and its training-induced plasticity[END_REF]. In addition to studying the effects of IC training on functional connectivity during an IC task, resting-state fMRI (rs-fMRI) studies also reported the effects of IC training on functional connectivity at rest [START_REF] Cao | Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks[END_REF].

Recent brain imaging studies also showed that (i) local properties of BOLD signal oscillations at rest, namely, the Regional Homogeneity (ReHo, [START_REF] Jiang | Regional Homogeneity: A Multimodal, Multiscale Neuroimaging Marker of the Human Connectome[END_REF][START_REF] Zang | Regional homogeneity approach to fMRI data analysis[END_REF] and the fractional Amplitude of Low Frequency Fluctuations (fALFF, [START_REF] Zou | An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for restingstate fMRI: Fractional ALFF[END_REF] of the spatio-temporal coherence and amplitude of the local BOLD fluctuations, can serve as non-invasive proxies of brain metabolism [START_REF] Aiello | Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study[END_REF]; and (ii) individual differences in these BOLD fluctuations at rest are related to individual differences in Cool IC efficiency in adults (Liu, Chen et al., 2015 for ReHo andHu et al., 2014 for fALFF).

In this context, we tested whether ReHo and fALFF at baseline (before training) are associated with Cool and Hot IC efficiency in adolescents aged 16 to 17 years old. We focused on the critical role of two cortical regions (i.e., the IFG and ACC) and two subcortical regions (i.e., the caudate nucleus and putamen) playing a critical role in IC. In line with previous findings, we anticipated correlations at baseline between IC efficiency and ReHo and fALFF signals in these regions [START_REF] Hu | Changes in cerebral morphometry and amplitude of low-frequency fluctuations of BOLD signals during healthy aging: correlation with inhibitory control[END_REF][START_REF] Liu | Predicting Stroop Effect from Spontaneous Neuronal Activity: A Study of Regional Homogeneity[END_REF]. We also anticipated different correlation patterns in Cool IC compared to Hot IC tasks. In addition, we also tested the effect of a computerized 5-week IC training compared to an active control training in adolescents on Cool and Hot cognitive control efficiency and on the local properties of BOLD fluctuations at rest (i.e., ReHo and fALFF). Note that comparing the effect of IC to an active control training group allowed us to control for test-retest effects (see, e.g., [START_REF] Diamond | Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not[END_REF]. In line with previous studies, we anticipated behavioural changes after IC training on the trained task (i.e., Cool Stroop task) [START_REF] Berkman | Training-Induced Changes in Inhibitory Control Network Activity[END_REF][START_REF] Zhao | Training and transfer effects of response inhibition training in children and adults[END_REF] but not on the affective non-trained task (i.e., Hot Stroop task) [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF]. We also expected functional changes after IC training in the cortical (i.e., the ACC and IFG) and subcortical (i.e., the caudate nucleus and putamen) regions of the IC (e.g., [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF][START_REF] Berkman | Training-Induced Changes in Inhibitory Control Network Activity[END_REF].

MATERIALS AND METHODS

Participants

Sixty healthy adolescents, aged 16 to 17 years old (mean age16.78 ± 0.49 y.o, 21 males), were recruited from high schools in Paris (France), coming from high-income families (mean income / month: 4375€ ± 1100€). The recruited participants were all of the regular age for their grade. Participants were randomly assigned to two different trainings: the Inhibitory Control (IC) group (n = 32, 16.85 ± 0.53 y.o, 11 males) and the Active Control (AC) group (n = 28, 16.71 ± 0.44 y.o, 10 males; for more information, see the 'Training procedure'). A computerized algorithm (randoweb) was used to ensure a balanced male to female ratio between the two training groups. Eight participants were discarded from the training analyses because they did not reach the minimum number of 15 training sessions and thus (i) did not have sufficient training (i.e., at least 15 sessions of 15 min, which means a minimum of 3 h 45 min) and / or (ii) stopped the training too early before the post-training session. Therefore, the sample for these training analyses included 52 participants (IC: n = 28, 16.83 ± 0.53 y.o, 8 males; AC: n = 24, 16.72 ± 0.44 y.o, 7 males). The participants in the AC and the IC groups completed an average of 24.21 ± 1.72 ([20 -26]) and 24.96 ± 2.10 ([21 -33]) training sessions, respectively. There was no performance difference at baseline (pre-training session) between the groups for the Cognitive Stroop Task (AC: 86.56 ± 88.45 vs. IC: 61.95 ± 89.55,F(1,50) = 0.987, p = .325, η 2 p = 0.019) or for the emotional version of the Stroop task (AC: 62.61 ± 123.81 vs. IC: 49.94 ± 80.10, F(1,50) = 0.197, p = .659, η 2 p = 0.004).

All participants were right-handed, as determined by the Edinburgh Handedness Inventory [START_REF] Oldfield | The assessment and analysis of handedness: the Edinburgh inventory[END_REF], born full-term, had normal or corrected-to-normal vision, had no history of neurological disease, and had no cerebral abnormalities. Parents or legal guardians gave written consent, and all adolescents agreed to participate. All participants were tested in accordance with the national and international norms that govern the use of human research participants. This study was approved by a national ethics committee (IRB 2015-A00811-48).

Experimental procedure

During the training period, each participant performed 25 training sessions on electronic tablets at home, 15 minutes per day, 5 days a week for 5 weeks. The participants were randomly assigned to the IC group or the AC group using a single-blind design. During the training period, each participant in the IC group performed both the classic version of the Stroop task (Colour-Word Stroop task / Cool Stroop task) and the Stop Signal Task (SST), and each participant in the AC group answered knowledge-and vocabulary-based questions [START_REF] Jaeggi | Short-and long-term benefits of cognitive training[END_REF]. To limit assessor-related bias, instructions and responses were given through the electronic tablets. This procedure ensured that all participants were exposed to similar experimental demands and similar levels of motivation [START_REF] Zhao | Training and transfer effects of response inhibition training in children and adults[END_REF]. This

Training sessions

In both IC and AC trainings, the level of difficulty was increased progressively and adapted in real time to the learning curve of each participant in order to maintain the motivation of the participant and to prevent automaticity [START_REF] Buschkuehl | Neuronal effects following working memory training[END_REF][START_REF] Green | Exercising Your Brain: A Review of Human Brain Plasticity and Training-Induced Learning[END_REF][START_REF] Klingberg | Training and plasticity of working memory[END_REF]. All tasks were implemented on tactile tablets using E-prime 2.0.10 (Psychology Software Tools, Pittsburgh, PA). Since (i) IC is a multidimensional construct [START_REF] Diamond | Executive Functions[END_REF] and (ii) transfer effects can be potentially larger when the same cognitive function is trained with different tasks [START_REF] Buschkuehl | Neuronal effects following working memory training[END_REF][START_REF] Green | Exercising Your Brain: A Review of Human Brain Plasticity and Training-Induced Learning[END_REF] In the Colour-Word Stroop task, participants were instructed to identify the ink colour (e.g., blue) of a printed word denoting a colour by pressing one of the four colour buttons (i.e., blue, green, yellow, red) on the tactile tablet. The word colour and the ink colour could be either congruent (e.g., "blue" written in blue) or incongruent (e.g., "blue" written in red). In addition, we designed control items (i.e., colour word written in black) in which participants were instructed to determine the colour denoted by the word. Control items were intended to force participants to encode the colour of the word throughout the training sessions. Words were presented on the centre of the screen in a 24-pt Bold Arial Font. The level of difficulty increased in the congruent and incongruent Stroop items as the stimulus onset asynchrony (SOA) decreased between the presentation of the word (written in light grey) and the colouring of this word in one of the four ink colours (blue, yellow, green or red, see, e.g., [START_REF] Appelbaum | The Dynamics of Proactive and Reactive Cognitive Control Processes in the Human Brain[END_REF]. At the beginning of the training, the SOA was set at 450 ms (Level 1) and decreased by 50 ms intervals at each level of task difficulty (up to an SOA of 100 ms in Level 8). At each level, participants performed 13 control, 13 congruent, and 13 incongruent Stroop items.

In the SST, participants were instructed to press one of two response buttons on the tactile tablet to indicate the direction of an arrow presented on the screen and to withhold their responses if an auditory signal (i.e., stop signal) was presented after the presentation of the arrow (e.g., [START_REF] Aron | Inhibition and the right inferior frontal cortex: one decade on[END_REF]. The task difficulty was also adjusted to the participants' level. The time interval between the presentation of the arrow and the stop signal (i.e., the SOA) was manipulated. To increase the difficulty, the SOA was increased, and to decrease it, the SOA was reduced. On Level 1, the SOA was set at 100 ms and was increased by 50 ms intervals at each level of task difficulty (up to an SOA of 450 ms in Level 8). At the beginning of each training session, participants performed 16 Go trials. Then, each level in each training session consisted of 30 Go trials and 10 Stop trials.

The AC training consisted of knowledge-and vocabulary-based tasks of increasing difficulty (see [START_REF] Jaeggi | Short-and long-term benefits of cognitive training[END_REF]. In each task, 4-choice trivia-like questions were presented, and participants were asked to answer by pressing one of the four answers presented on the screen. A maximum of 30 s was allowed for participants to answer each question. An online pre-test, administered to more than 1600 adolescents, was used to categorize the questions into 8 difficulty levels for each task. Ten questions were finally chosen for each difficulty level.

On any given level, for the four tasks, the task difficulty was increased when participants achieved 90% accuracy, decreased when participants failed to achieve 70% accuracy, and remained the same when participants reached between 70% and 90% accuracy. Each training session consisted of several blocks during which the participants had to earn a maximum number of points to upgrade level.

For each task and each training session, participants started the session with the difficulty level just below the one they achieved during the previous training session. At the end of each training session, the participants were asked to complete a short auto-evaluation questionnaire on their motivation and engagement during the session, which provided the experimenters with details regarding (i) the number of sessions performed during the training phase and (ii) the quality of the training.

To determine whether the adolescents progressed in the IC and AC training groups, we ran separate t-tests on the average level of difficulty reached by the participants in the first and last sessions.

We found a significant difference between the two sessions in the AC group (first session: 2.26 ± 0.22, last session: 6.44 ± 0.40; t (26) = -12.71, p < .001, d= 2.45) and in the IC group (first session: 2.33 ± 0.13, last session: 5.08 ± 0.20; t (31) = -14.05, p < .001, d = 2.48). Importantly, participants made progress from the first to the last training session in both the AC and the IC training groups.

Evaluation of Cool and Hot inhibitory control

Both tasks were implemented with E-prime 2.0.10 (Psychology Software Tools, Pittsburgh, PA) and performed individually on a laptop computer with a resolution of 1366 x 768 pixels and a refresh rate of 60 Hz.

For the Colour-Word Stroop task, we designed sixteen standard Stroop items combining four different colour names (red, green, blue and yellow) with the four corresponding ink colours (RGB colour codes: 255;0;0, 0;255;0, 0;255, and 255;255;0, respectively). Four items were congruent (e.g., "red" written in red), and 12 were incongruent (e.g., "red" written in blue). Items were displayed in the centre of the screen in 24-pt Bold Courier New on a grey background (RGB code 135;135;135). The participants were instructed to identify the ink colours while ignoring the meaning of the words presented.

In the Emotional Stroop task, four black-and-white photographic images depicting happy, angry, sad, and fearful faces (two males and two females), taken from the NimStim set of facial expressions [START_REF] Tottenham | The NimStim set of facial expressions: Judgments from untrained research participants[END_REF], were combined with four words naming the corresponding emotions (e.g., happiness, anger, sadness and fear) to create four congruent items (e.g., the fearful face with "fear" written underneath) and 12 incongruent items (e.g., the fearful face with "anger" written underneath). The faces with the word written underneath were presented in the centre of the screen on a grey background (RGB code 135;135;135). The participants were instructed to indicate the emotion of the facial expressions while ignoring the word written underneath.

For both the Cool Stroop and the Hot Stroop tasks, the participants responded by manually pressing two keyboard buttons with their left and right index fingers (the 's' button for yellow or blue and the 'l' button for red or green in the Cool Stroop task; the 'd' button for happiness or sadness and the 'k' button for anger or fear in the Hot Stroop task). Each key was labelled with a sticker of the corresponding colour or emotion. Each trial started with the presentation of a black fixation cross on a grey background for 1500 ms, and then one of the colour words or faces / emotional words was displayed for a maximal duration of 3000 ms. The participants were asked to answer as quickly as possible while maintaining high accuracy. The response times (RTs) were measured from the stimulus onset to the button press. For each Stroop task, participants performed a block of 64 trials (i.e., 32 congruent and 32 incongruent trials), preceded by two training blocks. In the first training block, participants learned to associate each response button to the corresponding colour or emotion by pressing the key on the keyboard corresponding to the colour dot or facial expression presented on the screen (each colour dot or facial expression was presented two times for a total of 8 trials). In the second training block, participants performed 6 congruent and 6 incongruent Stroop items and received feedback on the correctness of their answers. The presentation of the trials was randomized in both training and experimental blocks. For both tasks, for each participant, we computed the mean RT for each condition the median value over the whole sample, was applied to replace any missing data and outliers. This procedure was used to obtain as much data as possible and thus to increase the statistical power of our statistical analyses.

MRI acquisition

Functional MRI data were acquired at the CIREN facility using a 3T MRI scanner (GE, DISCOVERY MR750 3T MR System, United Sates). We acquired anatomical MRI data using a high-resolution 3-D T1-weighted gradient sequence (field of view = 256 mm, slice thickness = 1 mm, matrix size = 256 x time = 400 ms) and an rs-fMRI using a T2*-weighted echo-planar imaging (EPI) sequence (field of view = 192 mm, slice thickness = 3 mm, 42 slices, matrix size = 64 x 64 mm, resolution = 3 x 3 x 3 mm 3 , flip angle = 90°, repetition time = 2000 ms, echo time = 23 ms). During the rs-fMRI sequence, the participants were asked to lay still with their eyes closed, but not to fall asleep, instead to try to think of nothing at all, and in the case of a sticky thought, to think of a starry sky. A seven-step approach was used to preprocess the rs-fMRI data. The first 10 time points were removed to avoid the non-equilibrium effects of magnetization, and the remaining 168 time points of functional BOLD images were then processed as follows:

MRI analysis

1. Slice time corrected for interleaved acquisition.

2. Realigned to correct for head movement by coregistering all the subsequent volumes to the first time point.

3. Repaired if necessary, using ArtRepair, for volumes with movements (rapid motion threshold > 0.5 mm / TR) using linear interpolation from the nearest unrepaired scans.

4. Spatially normalized. To normalize functional images, each participant's T1-weighted images were coregistered to the mean functional images and were subsequently segmented into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The parameters obtained from the segmentation were used to normalize each participant's functional images onto the Montreal Neurological Institute (MNI) space.

These functional images were then processed as follows:

5. Resampled to a voxel size of 3 x 3 x 3 mm 3 .

6. Smoothed with an 8 mm FWHM Gaussian Kernel. This smoothing step, during preprocessing, was applied only for fALFF analysis, and not ReHo analysis, to prevent artificially increasing the regional similarity [START_REF] Yan | DPARSF: a MATLAB toolbox for « pipeline » data analysis of restingstate fMRI[END_REF]. The smoothing of ReHo maps was thus performed after ReHo calculation.

7. To remove BOLD signal fluctuations unrelated to neural activity, the 6 head motion parameters plus their derivatives and the corresponding squared items (Friston 24;[START_REF] Friston | Movement-Related effects in fMRI time-series[END_REF] as well as WM, CSF and global mean signal were regressed out as nuisance variables; linear trend was also removed from each voxel's time course.

Estimation of ReHo and fALFF signals

If low-frequency fluctuations (0.01 -0.1 Hz) of the BOLD signal at rest reflect spontaneous neural activity [START_REF] Lu | Synchronized delta oscillations correlate with the resting-state functional MRI signal[END_REF], then high-frequency oscillations capture physiological signals such as cardiac and respiratory signals [START_REF] Cordes | Frequencies Contributing to Functional Connectivity in the Cerebral Cortex in "Resting-state" Data[END_REF]. Before ReHo estimation, data were therefore bandpass filtered (0.01-0.1 Hz) to minimize any confounding physiological effects. ReHo was then computed at the voxel level by calculating the Kendall's coefficient of concordance [START_REF] Kendall | Rank Correlation Methods[END_REF] of the BOLD time series of 27 neighbouring voxels [START_REF] Zang | Regional homogeneity approach to fMRI data analysis[END_REF]. ReHo is thus defined as the spatio-temporal coherence of the signal of a voxel with the signal of its nearest neighbours [START_REF] Zang | Regional homogeneity approach to fMRI data analysis[END_REF]. The ReHo maps were finally smoothed with an 8 mm FWHM Gaussian Kernel.

The regional spontaneous activity was also examined using the complementary ALFF measure [START_REF] Biswal | Functional connectivity in the motor cortex of resting human brain using echo-planar mri[END_REF]. ALFF reflects the intensity of regional spontaneous brain activity [START_REF] Zou | An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for restingstate fMRI: Fractional ALFF[END_REF] and is particularly sensitive to physiological noise. The fractional Amplitude of Low Frequency Fluctuations (fALFF) -defined as the total signal amplitude within the low-frequency range (0.01 -0.1 Hz) divided by the total signal amplitude of the entire detectable frequency range (i.e., 0.01 -0.25 Hz; [START_REF] Song | REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing[END_REF] -was therefore used as it improves the sensitivity and specificity in detecting spontaneous brain activities [START_REF] Zou | An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for restingstate fMRI: Fractional ALFF[END_REF].

To increase the signal-to-noise ratio, ReHo and fALFF values were extracted from the AAL2 atlas [START_REF] Rolls | Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas[END_REF][START_REF] Tzourio-Mazoyer | Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain[END_REF] and averaged over the critical brain regions of the IC network: two cortical regions (the ACC and the IFG) and two subcortical regions (the caudate nucleus and the putamen) (see Figure 2).

Statistical analysis

To confirm that ReHo and fALFF signals were associated with IC efficiency [START_REF] Hu | Changes in cerebral morphometry and amplitude of low-frequency fluctuations of BOLD signals during healthy aging: correlation with inhibitory control[END_REF][START_REF] Liu | Predicting Stroop Effect from Spontaneous Neuronal Activity: A Study of Regional Homogeneity[END_REF][START_REF] Tian | Regional homogeneity of resting state fMRI signals predicts Stop signal task performance[END_REF], we conducted univariate linear models with Stroop interference scores as the co-variable and brain hemispheres (i.e., "right" vs. "left") as the within-factor. Relative (i.e., relative change = (post -pre) / pre)), instead of absolute (i.e., post -pre), changes were used in the analyses to control for baseline variability.

We then tested whether the ReHo and fALFF signals changed after 5 weeks of IC training, i.e., whether ReHo and fALFF might also be sensitive biomarkers of IC training. Thus, for each ROI, a univariate linear model analysis was conducted with group ("AC" vs. "IC") as the between-factor and brain hemisphere ("right" vs. "left") as the within-factor. The dependent variable corresponded to the relative difference in ReHo or fALFF signals between the pre-test and post-test. To test whether the whole pattern of relative regional changes (i.e., pattern of relative changes including all the ROIs selected) differed between the two groups (i.e., "AC" vs. "IC"), we then carried out multivariate linear models for ReHo and fALFF separately.

The main effects and interactions were investigated with F-tests. A two-tailed p-value of less than .05 was considered statistically significant. The analyses were not corrected for multiple comparisons but were controlled for the age and sex. We also controlled for the high school attended by the participants to control for possible fixed effects associated with the affiliation to specific high schools. All statistical analyses were carried out with R software version 3.3.2 software (http://www.rproject.org/) and the 'car' and 'effects' packages.

RESULTS

Behavioural analyses

The analysis of the cognitive changes after training, tested by comparing the relative changes after training for both Cool and Hot Stroop scores between the AC and IC groups, did not reveal significant group-specific training effects for the Cool Stroop (F(1,50) = 0.379, p = .541, η 2 p = 0.008) or for the Hot Stroop (F(1,50) = 0.214, p = .645, η 2 p = 0.004) scores (see Figure 1 and Supplementary Material).

ReHo / fALFF and IC efficiency

Univariate linear model analyses revealed the main effects of the Cool Stroop score on the fALFF signal, with positive associations between the Cool Stroop score and the fALFF signals in bilateral ACC (F(1,45) = 6.16, p = .017, η 2 p = 0.12) and IFG (F(1,45) = 8.01, p = .0069, η 2 p = 0.15) (see Figure 2).

There was no significant association between the Cool Stroop score and the ReHo signals.

Regarding Hot IC, analyses revealed the main effects of the Hot Stroop score on the ReHo signal with positive associations between the Hot Stroop score and the ReHo signals in the bilateral caudate nucleus (F(1,45) = 4.25, p = .045, η 2 p = 0.08), putamen (F(1,45) = 4.19, p = .047, η 2 p = 0.08) and ACC (F(1,45) = 6.45, p = .015, η 2 p = 0.12) (Figure 2). Similar results were observed for fALFF, with positive associations in the bilateral caudate nucleus (F(1,45) = 7.95, p = .0071, η 2 p = 0.15) putamen (F(1,45) = 8.16, p = .0065, η 2 p = 0.15) and ACC (F(1,45) = 10.77, p = .0020, η 2 p = 0.19) (Figure 2). For both ReHo and fALFF, analyses also revealed an interaction between the Hot Stroop interference score and hemisphere within the putamen (ReHo: F(1, 45) = 5.05, p = .030, η 2 p = 0.10; fALFF: F(1,45) = 8.56, p = .0054, η 2 p = 0.16), showing stronger associations between ReHo / fALFF signals and interference score within the left rather than the right hemisphere (Figure 2).

ReHo and fALFF sensitivity to IC training

Univariate linear model analysis of relative changes in ReHo signal revealed a significant groupby-hemisphere interaction in the caudate nucleus (F(1, 37) = 5.01, p = .031, η 2 p = 0.12) (see Figure 3 and Table 1). A similar group-by-hemisphere interaction (F(1,37) = 5.75, p = .022, η 2 p = 0.13) was detected for the relative change of the fALFF signal within this region (see Figure 3 and Table 2). In the right hemisphere, ReHo and fALFF signals increased after training in the AC group and decreased in the IC group, while in the left hemisphere, ReHo and fALFF signals increased in both AC and IC groups (see Figure 3 and Tables 12). A main group effect was also detected for the fALFF relative change within the putamen (F(1, 37) = 4.50, p = .041, η 2 p = 0.11), with a fALFF signal increasing after training for participants in the AC group and decreasing for participants in the IC group (see Figure 3 and Table 2). Univariate analysis did not reveal other significant results in the subcortical or the cortical brain regions.

The multivariate analysis on the whole pattern of relative changes after training indicated a significant main group effect for ReHo (F(8, 30) = 2.77, p = .02, η 2 p = 0.42) and fALFF (F(8, 30) = 2.45, p = .03, η 2 p = 0.39) (Figure 3), showing that ReHo and fALFF change patterns differed between the IC and AC groups. "IC") as the between-factor and brain hemisphere ("right" vs. "left") as the within-factor. Analyses were controlled for the age, sex, and high school of the participants.

DISCUSSION

Consistent with the findings of previous studies in adults [START_REF] Deng | The relevance of fractional amplitude of lowfrequency fluctuation to interference effect[END_REF][START_REF] Liu | Predicting Stroop Effect from Spontaneous Neuronal Activity: A Study of Regional Homogeneity[END_REF][START_REF] Takeuchi | Degree centrality and fractional amplitude of low-frequency oscillations associated with Stroop interference[END_REF], we found that ReHo and fALFF signals were associated with IC efficiency during adolescence. Importantly, we are the first to provide evidence that the relation between ReHo and fALFF signals differ according to the type of IC tasks, namely, cognitive (Cool) or emotional (Hot)

Stroop tasks, in adolescents.

We found that the fALFF signal in the IFG and ACC, a non-invasive proxy of regional brain metabolism [START_REF] Aiello | Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study[END_REF], was positively correlated with the Cool Stroop interference score.

Hence, a lower fALFF signal was associated with a higher IC efficiency in adolescents. In the current study, neural activity at rest was investigated from the analysis of local properties of BOLD fluctuations, complimenting previous studies on IC at adolescence, based on functional and structural connectivity.

Resting-state fMRI studies reported functional connectivity changes within the executive network, encompassing the fronto-parietal and the cingulo-opercular networks, with decreased short-range connections (correlations between close regions, i.e., 'segregation') and increased long-range connections (correlations between distant regions, i.e., 'integration') during development [START_REF] Fair | Development of distinct control networks through segregation and integration[END_REF][START_REF] Fair | Functional Brain Networks Develop from a "Local to Distributed" Organization[END_REF]. Recently, a diffusion MRI study investigated the structural connectivity during development and revealed that both modular segregation and network efficiency are associated with enhanced executive performance and also mediate the improvement of executive functioning with age [START_REF] Baum | Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth[END_REF][START_REF] Satterthwaite | Functional Maturation of the Executive System during Adolescence[END_REF]. At the cellular level, the myelination process, which enables more effective long-distance neural pathways, favours integration, whereas synaptic pruning favours segregation [START_REF] Fair | Development of distinct control networks through segregation and integration[END_REF][START_REF] Fair | Functional Brain Networks Develop from a "Local to Distributed" Organization[END_REF].

A previous study on adults using a Multi-Source Interference Task (combining the Stroop, Simon and flanker effects) reported a negative correlation between fALFF signal and interference score [START_REF] Deng | The relevance of fractional amplitude of lowfrequency fluctuation to interference effect[END_REF]. We suspect that such inversion of the relation between fALFF and IC efficiency from adolescence to adulthood might stem from the protracted maturation of the prefrontal regions [START_REF] Gogtay | Dynamic mapping of human cortical development during childhood through early adulthood[END_REF][START_REF] Luna | What has fMRI told us about the Development of Cognitive Control through Adolescence?[END_REF][START_REF] Luna | An Integrative Model of the Maturation of Cognitive Control[END_REF], and in particular of the non-linear cortical thickness changes characterized by a thickening of the cortex from childhood to adolescence followed by cortical thinning from adolescence to adulthood [START_REF] Giedd | Structural MRI of Pediatric Brain Development: What Have We Learned and Where Are We Going[END_REF][START_REF] Gogtay | Mapping gray matter development: Implications for typical development and vulnerability to psychopathology[END_REF] associated with functional segregation [START_REF] Fair | Development of distinct control networks through segregation and integration[END_REF][START_REF] Fair | Functional Brain Networks Develop from a "Local to Distributed" Organization[END_REF].

ReHo and fALFF signals in subcortical (caudate nucleus and putamen) and cortical (ACC) regions were positively correlated with the Hot Stroop interference score, showing that adolescents with higher ReHo and fALFF signals within these brain regions of the IC network had lower IC efficiency.

These findings provide additional evidence for the role of the caudate nucleus and the putamen, subregions of the striatum, in solving conflict in an affectively charged context [START_REF] Chein | Peers increase adolescent risk taking by enhancing activity in the brain's reward circuitry[END_REF][START_REF] Hare | Contributions of amygdala and striatal activity in emotion regulation[END_REF]. We also found a stronger association between Hot IC efficiency and ReHo / fALFF signals in the putamen in the left compared to the right hemisphere. This leftward asymmetry may be associated with the hemisphere-specific maturational changes that occur during adolescence, with a greater decrease of putamen volume in the left hemisphere compared to that of the right hemisphere [START_REF] Dennison | Mapping subcortical brain maturation during adolescence: evidence of hemisphere-and sexspecific longitudinal changes[END_REF]. Hence, the putamen in the left hemisphere, by maturing earlier than the putamen in the right hemisphere, might play a greater role in solving conflict in an emotional context during adolescence. The association between performance in the Hot Stroop task and the ReHo and fALFF signals within the dorsal part of the ACC suggests that the Hot Stroop task might also require conflict detection and conflict resolution processes similar to those involved in affectively neutral contexts [START_REF] Botvinick | Conflict monitoring and decision making: Reconciling two perspectives on anterior cingulate function[END_REF][START_REF] Simon | Anterior cingulate cortex and intuitive bias detection during number conservation[END_REF].

In addition to the association observed between the ReHo and fALFF signals and the Cool and

Hot IC efficiencies at baseline, we also found IC training-related changes in these two measures of BOLD signal fluctuations at rest in the sub-regions of the striatum (i.e., caudate nucleus and putamen), key regions of the inhibitory control network [START_REF] Beste | Striatal Microstructure and Its Relevance for Cognitive Control[END_REF][START_REF] Grahn | The cognitive functions of the caudate nucleus[END_REF][START_REF] Rubia | Progressive increase of frontostriatal brain activation from childhood to adulthood during eventrelated tasks of cognitive control[END_REF] but also involved more generally in learning [START_REF] Seger | The Basal Ganglia in Human Learning[END_REF][START_REF] Seger | How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback[END_REF][START_REF] Seger | The Roles of the Caudate Nucleus in Human Classification Learning[END_REF][START_REF] Seger | The Basal Ganglia in Human Learning[END_REF]. These changes in striatal regions (i.e., the caudate nucleus and putamen) after a IC training are consistent with previous studies reporting (i) an increased caudate activation in the Stop versus Go condition during an SST task [START_REF] Berkman | Training-Induced Changes in Inhibitory Control Network Activity[END_REF] and (ii) fronto-basal changes by SST practice [START_REF] Manuel | Plastic modifications within inhibitory control networks induced by practicing a stop-signal task: an electrical neuroimaging study[END_REF][START_REF] Spierer | Training-induced behavioral and brain plasticity in inhibitory control[END_REF]. These results are thus in line with fMRI studies reporting activations within the sub-thalamic nucleus [START_REF] Aron | Cortical and Subcortical Contributions to Stop Signal Response Inhibition: Role of the Subthalamic Nucleus[END_REF][START_REF] Aron | Frontosubthalamic Circuits for Control of Action and Cognition[END_REF] and the striatum [START_REF] Beste | Striatal Microstructure and Its Relevance for Cognitive Control[END_REF] during IC tasks. The involvement of the striatal regions in IC is partly due to their structural and functional connectivity to the PFC [START_REF] Beste | Striatal Microstructure and Its Relevance for Cognitive Control[END_REF]. These ReHo and fALFF changes related to IC training were also found to be hemisphere-dependent. This hemispheric specificity is in line with previous studies in adolescents [START_REF] Houdé | Mapping numerical processing, reading, and executive functions in the developing brain: an fMRI meta-analysis of 52 studies including 842 children[END_REF][START_REF] Rubia | Progressive increase of frontostriatal brain activation from childhood to adulthood during eventrelated tasks of cognitive control[END_REF] and in adults [START_REF] Garavan | Right hemispheric dominance of inhibitory control: An event-related functional MRI study[END_REF], which reported a rightward hemispheric dominance for response inhibition. For instance, [START_REF] Rubia | Progressive increase of frontostriatal brain activation from childhood to adulthood during eventrelated tasks of cognitive control[END_REF] reported increased fronto-striatal activations, especially in the right caudate and putamen, from adolescence to adulthood in motor and cognitive IC tasks. Moreover, a meta-analysis of functional MRI studies in children and adolescents reported an age-and hemisphere-specific activation during executive tasks, with a larger activation in the right than in the left anterior insular cortex (AIC, a region involved in cognitive control; [START_REF] Bunge | Immature Frontal Lobe Contributions to Cognitive Control in Children: Evidence from fMRI[END_REF][START_REF] Dosenbach | A Core System for the Implementation of Task Sets[END_REF] in adolescents but not in children [START_REF] Houdé | Mapping numerical processing, reading, and executive functions in the developing brain: an fMRI meta-analysis of 52 studies including 842 children[END_REF]. Taken together, these different studies support the notion that the IC neural network is right-lateralized with an increasing lateralization with age [START_REF] Houdé | Mapping numerical processing, reading, and executive functions in the developing brain: an fMRI meta-analysis of 52 studies including 842 children[END_REF][START_REF] Rubia | Progressive increase of frontostriatal brain activation from childhood to adulthood during eventrelated tasks of cognitive control[END_REF].

Given that a 5-week computerized IC training induced changes in brain function, assessed by ReHo and fALFF signals at rest, we argue that such proxies of regional metabolism [START_REF] Aiello | Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study[END_REF] could provide more sensitive markers of the potential effect of intensive and sustained cognitive training compared to behavioural outcomes. This combination of brain changes associated with the lack of behavioural changes after cognitive training was recently observed by [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF], who did not find behavioural changes but detected activation changes, during an emotion regulation task, in the left IFG and supramarginal gyrus, after an SST-based IC training. Notably, unlike the study by [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF], which investigated task-evoked BOLD signal changes, the present study measured spontaneous BOLD signal fluctuation changes at rest. Such functional changes at rest may reflect the consolidation of previous experience [START_REF] Lewis | Learning sculpts the spontaneous activity of the resting human brain[END_REF], which in turn may lead to behavioural changes in the near future. On the other hand, the lack of training-induced cognitive changes may have also resulted from slight differences between the cognitive Stroop trained task and the cognitive version of the Stroop task performed during the pre-and the post-training sessions. Indeed, while improvements are often observed in the trained tasks, little or no transfer may be detected for nontrained tasks even if they are very similar to the trained tasks [START_REF] Green | Exercising Your Brain: A Review of Human Brain Plasticity and Training-Induced Learning[END_REF]. Regarding the emotional IC, the lack of performance increase in the Hot Stroop task after a Cool Stroop training is consistent with a previous study reporting no transfer effect from a non-affective training task to a nontrained affective task [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF]. As already suggested by previous studies [START_REF] Green | Exercising Your Brain: A Review of Human Brain Plasticity and Training-Induced Learning[END_REF][START_REF] Schmidt | New Conceptualizations of Practice: Common Principles in Three Paradigms Suggest New Concepts for Training[END_REF], we may have to vary the task items to be learned and the context in which they appear during the training phase to attempt to facilitate the transfer from the trained task to non-trained tasks (e.g., Hot Stroop task). Indeed, this may force the participants to extract general principles about the trained task and thus may ensure that the learned skills are reused.

Moreover, given that functional changes in the brain following IC training seem to precede behavioural changes, we suspect that increasing the training dose by increasing the session duration and / or the number of sessions [START_REF] Beauchamp | Does inhibitory control training transfer?: behavioral and neural effects on an untrained emotion regulation task[END_REF][START_REF] Jolles | Training the developing brain: a neurocognitive perspective[END_REF][START_REF] Kelly | Human Functional Neuroimaging of Brain Changes Associated with Practice[END_REF] may facilitate the training transfer and thus enable the detection of changes at the behavioural level.

The present study has several limitations that call for caution when interpreting the results. First, despite the large sample size associated with a longitudinal design, the statistical effects are moderate.

Our findings should thus be taken with caution and replicated with a larger group of independent samples. Future studies should investigate the effects of IC training at both the brain and behavioural levels not only on adolescents but also on younger and older participants (i.e., children and young adults)

  training programme was designed by the authors. In before (pre-test) and after (post-test) training sessions, participants performed a battery of cognitive tests (~2 h) and a multimodal MRI acquisition (~1 h 30). During the cognitive evaluation, the participants individually performed different cognitive tests involving executive functions (i.e., working memory, flexibility and inhibition), including an affectively neutral (Colour-Word Stroop Task / Cool Stroop) and an affectively charged (Emotional Stroop Task / Hot Stroop) version of the Stroop task, following instructions displayed on the computer screen. We focused on these two versions of the Stroop task because they are sensitive to developmental differences in Cool versus Hot IC (Aïte et al., 2018). Indeed, Aïte et al. (2018) reported a linear development of Cool IC abilities and a quadratic developmental trajectory of Hot IC abilities from childhood to adulthood, showing that adolescence is a critical developmental period during which adolescents are less efficient than both children and adults in solving conflicts in affectively-charged contexts. The MRI session included anatomical and resting state functional sequences. Other MRI acquisition sessions -functional MRI during the Stop-Signal Task, Attention Network Task, Dot Task and Delayed Discounting Task along with a diffusion MRI sequence -out of the scope of the present study, were also acquired.

  , IC training included two tasks involving interference control (Colour-Word Stroop task / Cool Stroop task) and response inhibition (Stop Signal Task).

  (i.e., congruent [C] and incongruent [I]) on correctly performed trials. Then, the Stroop score (i.e., the interference score [IS], which is the difference in RTs between congruent and incongruent items), was computed for each participant, where a higher Stroop score revealed lower IC efficiency. Stroop scores were screened and cleaned for possible aberrant values using a nonparametric approach: values lower than median -2 MAD (MAD: median absolute deviation) or greater than median + 2 MAD were considered outliers. Data imputation, based on the median value over the whole sample, was also applied to replace any missing data and outliers. Receptivity of cognitive training was assessed from the relative change after training (i.e., relative change = (post -pre) / pre) for both Stroop scores (i.e., Colour-Word Stroop task and Emotional Stroop task). For each task, the relative change after training was computed from Stroop scores already screened and cleaned for aberrant values. Then, data imputation, based on

Figure 1 :

 1 Figure 1: Relative cognitive changes after training. These violin plots represent the relative changes after training [relative change = (post -pre) / pre)] in both the Cool Stroop (first row) and the Hot Stroop (second row) scores, for both training groups: Active Control (left) and Inhibitory Control (right). Each dot represents a participant data. Data imputation, based on the median value over the whole sample, was applied to replace any missing data and outliers.

Figure 2 :

 2 Figure 2: Correlations between ReHo / fALFF signals and Inhibitory Control (IC) efficiency assessed with the Cognitive Stroop Task (Cool IC) and the Emotional Stroop task (Hot IC). Regions of Interest (ROI), represented on an axial slice (middle panel), were derived from the standard AAL2 atlas and colour coded as follows: caudate nucleus in yellow, putamen in purple, ACC in green, and IFG in orange. Graphs on the top of the figure correspond to the ReHo signal, and graphs in the middle and bottom correspond to the fALFF signal. Blue lines / dots on the graphs are used for Cool Stroop data and orange lines / dots for Hot Stroop data. Box colours are as described above, with the dispersion graph ReHo / fALFF versus interference score of Cool / Hot Stroop task codes for the analysed ROI. Dark and light lines / dots represent data from the left and right hemispheres, respectively.

Figure 3 :

 3 Figure 3: Global patterns of the relative changes of ReHo and fALFF after training. The radar graphs represent relative changes in the ReHo signal (left panel) and the fALFF signal (right panel) in the subcortical (caudate nucleus, putamen) and cortical (ACC, IFG) regions of the IC network in the Inhibitory Control (in red) and Active Control (in blue) training groups.

  to determine whether the IC training-related ReHo and fALFF signal changes are training-or agespecific. In addition to an immediate post-training effect, future work should also test the possible lasting effects (e.g., 3-or 6-months follow-up sessions) of an IC training. Furthermore, developmental studies, involving children, adolescents and adults should also investigate to what extent age can, or not, modulate these effects (Zhao et al., 2018). In conclusion, we demonstrated for the first time that ReHo and fALFF signals, i.e., local properties of the BOLD signal fluctuations, provide information on the relationship between individual differences in the activity of the brain at rest and individual differences in both Cool and Hot IC efficiency and may also help monitor the effects of IC training. Future studies should determine whether ReHo and fALFF signals before training could possibly constitute reliable predictors of potential IC efficiency gain after training in both typical and clinical populations.

2.4.1. Preprocessing of rs-fMRI data MRI

  preprocessing and statistical analyses were performed using SPM12 (Wellcome Department of Imaging Neuroscience, London, UK) running on MATLAB 2014b (Mathworks Inc., Natick, USA) with dedicated toolboxes (the advanced version of Data Processing Assistant for Resting-State fMRI,

	DPARSF;	Yan	&	Zang,	2010,	http://www.restfmri.net)	and	ArtRepair

(http://cibsr.stanford.edu/tools/human-brain-project/artrepair-software.html;

[START_REF] Mazaïka | Artifact Repair of fMRI data from High Motion Clinical Subjects[END_REF]

.

These software were used to pre-process the fMRI data and compute the ReHo and fALFF images. An in-house MATLAB function was used to estimate the median value of the ReHo and fALFF signals within individual Regions of Interest (ROIs) derived from the AAL2 atlas

[START_REF] Rolls | Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas[END_REF][START_REF] Tzourio-Mazoyer | Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain[END_REF]

.

  Data from participants in both the AC and IC training groups were pooled into a common sample as was done in the pre-test session; participants had not yet followed any training.Univariate linear models were used to test whether the relative changes in Stroop scores after training in both the Cool and the Hot Stroop tasks differed between the training groups (i.e., CA vs. CI).

Both the Colour-Word Stroop task (Cool IC) and the Emotional Stroop task (Hot IC) were analysed.

Table 1 : Relative changes in ReHo after training in the Active Control (AC) and Inhibitory Control groups (IC).

 1 Data for the AC and IC training groups are represented as the means ± standard errors.The main and interactive effects are derived from univariate linear models with group ("AC" vs. "IC") as the between-factor and brain hemisphere ("right" vs. "left") as the within-factor. Analyses were controlled for the age, sex, and high school of the participants.

	ROIs	AC	IC		Statistics
		Mean (± SE)	Mean (± SE)	Group effect	Group x Hemisphere
				F-value	p-value F-value p-value
	R ACC	0.024 (0.028)	0.061 (0.032)	0.60	0.443	0.64	0.429
	L ACC	0.040 (0.023)	0.053 (0.033)
	R IFG	0.004 (0.24)	0.015 (0.020)	0.42	0.522	0.55	0.464
	L IFG	0.028 (0.021)	0.056 (0.026)
	R Caudate	0.035 (0.029)	-0.046 (0.024)	1.42	0.241	5.01	0.031
	L Caudate	0.016 (0.027)	0.012 (0.034)
	R Putamen	0.072 (0.026)	-0.013 (0.031)	1.61	0.213	0.59	0.446
	L Putamen	0.042 (0.029)	0.002 (0.037)
	ROIs	AC	IC		Statistics
		(Mean ± SE)	(Mean ± SE)	Group effect	Group x Hemisphere
				F-value	p-value F-value p-value
	R ACC	0.024 (0.015)	0.030 (0.014)	0.05	0.828	2.95	0.094
	L ACC	0.026 (0.017)	0.016 (0.015)
	R IFG	0.008 (0.015)	0.027 (0.013)	0.40	0.530	1.14	0.293
	L IFG	0.022 (0.020)	0.030 (0.012)
	R Caudate	0.021 (0.019)	-0.006 (0.013)	1.25	0.271	5.75	0.022
	L Caudate	0.018 (0.020)	0.015 (0.015)
	R Putamen	0.041 (0.019)	-0.0007 (0.014)	4.50	0.041	1.21	0.279
	L Putamen	0.052 (0.023)	-0.001 (0.015)

Table 2 : Relative changes in fALFF after training in the Active Control (AC) and Inhibitory Control groups (IC).

 2 Data for the AC and IC training groups are represented as the means ± standard errors. The main and interactive effects are derived from univariate linear models with group ("AC" vs.

ACKNOWLEDGEMENTS

The study was supported by research grants from the French National Research Agency (ANR-14-CE30-0014-01 APEX) and personal grants from the Fondation pour la Recherche Médicale and Institut Universitaire de France.