Pauline Bezivin Frere

Nora C Vetter

Eric Artiges

Irina Filippi

Rubén Miranda

Hélène Vulser

Marie-Laure Paillère-Martinot

Veronika Ziesch

Patricia Conrod

Anna Cattrell

Henrik Walter

Jurgen Gallinat

Uli Bromberg

Sarah Rodehacke

Eva Menningen

Vincent Frouin

Dimitri Papadopoulos-Orfanos

Argyris Stringaris

Jani Penttilä

Betteke Van Noort

Yvonne Grimmer

Gunter Schumann

Michael N Smolka

Jean-Luc Martinot

Hervé Lemaître
email: herve.lemaitre@u-psud.fr

Sex effects on structural maturation of the limbic system and outcomes on emotional regulation during adolescence

Keywords: diffusion tensor imaging, T1-weigthed imaging, longitudinal, adolescence, sex difference, puberty

Though adolescence is a time of emerging sex differences in emotions, sex-related differences in the anatomy of the maturing brain has been under-explored over this period. The aim of this study was to investigate whether puberty and sexual differentiation in brain maturation could explain emotional differences between girls and boys during adolescence. We adapted a dedicated longitudinal pipeline to process structural and diffusion images from 335 typically developing adolescents between 14 and 16 years. We used voxel-based and Regions of Interest approaches to explore sex and puberty effects on brain and behavioral changes during adolescence. Sexual differences in brain maturation were characterized by amygdala and hippocampal volume increase in boys and decrease in girls. These changes were mediating the sexual differences in positive emotional regulation as illustrated by positive attributes increase in boys and decrease in girls. Moreover, the differential maturation rates between the limbic system and the prefrontal cortex highlighted the delayed maturation in boys compared to girls. This is the first study to show the sex effects on the differential cortico/subcortical maturation rates and the interaction between sex and puberty in the limbic system maturation related to positive attributes, reported as being protective from emotional disorders.

Introduction

Adolescence is a sensitive period of gradual transition from childhood to adulthood [START_REF] Spear | The adolescent brain and age-related behavioral manifestations[END_REF] through maturation of adult social and cognitive behaviors [START_REF] Sisk | The neural basis of puberty and adolescence[END_REF].

Adolescence is characterized by important pubertal changes and the passage from immature child brain to adult brain through complex maturational processes such as synaptic pruning, dendritic and axonal arborization and myelination [START_REF] Lenroot | Brain development in children and adolescents: insights from anatomical magnetic resonance imaging[END_REF]. It is also a period of emerging sex differences such as on brain and behaviors. Hormonal changes related to puberty are partly responsible for the development of the brain [START_REF] Spear | The adolescent brain and age-related behavioral manifestations[END_REF] and of the cognitive functions [START_REF] Blakemore | The Role of Puberty in the Developing Adolescent Brain[END_REF]. Onset of pubertal maturation occurs in the brain with some neural changes leading to hormone levels increase themselves responsible for other brain changes [START_REF] Dahl | Adolescent Brain Development: A Period of Vulnerabilities and Opportunities[END_REF]. The pubertal timing being different between boys and girls, age alone is unfit for looking at sex-related maturation differences during adolescence. Thus, reliance on pubertal landmarks rather than age appears more adapted for studying sex and maturation processes during adolescence.

Sex effects on brain macrostructural maturation as studied with Magnetic Resonance Imaging (MRI) has been described as a global grey matter (GM) volume peak reached earlier in girls followed by a steeper GM volume decrease rate compared to boys on a classical inverted U-shape maturation curve [START_REF] Aubert-Broche | A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood[END_REF]Herting and Sowell, 2017;[START_REF] Raznahan | Longitudinal four-dimensional mapping of subcortical anatomy in human development[END_REF].

Furthermore, global white matter (WM) volume follows a steeper linear WM volume increase in boys as compared to girls. Additionally, the sexual differences of white matter microstructure investigated with Diffusion Tensor Imaging (DTI) draw less consistent findings (Tamnes et al., 2017). Some studies reported sex differences in WM microstructure maturation [START_REF] Herting | The impact of sex, puberty, and hormones on white matter microstructure in adolescents[END_REF][START_REF] Schmithorst | Developmental differences in white matter architecture between boys and girls[END_REF][START_REF] Seunarine | Sexual Dimorphism in White Matter Developmental Trajectories Using Tract-Based Spatial Statistics[END_REF][START_REF] Simmonds | Developmental stages and sex differences of white matter and behavioral development through adolescence: A longitudinal diffusion tensor imaging (DTI) study[END_REF][START_REF] Wang | Sex differences in white matter development during adolescence: A DTI study[END_REF] while others studies have reported few or no significant sex-by-age interaction [START_REF] Bava | Longitudinal characterization of white matter maturation during adolescence[END_REF][START_REF] Eluvathingal | Quantitative Diffusion Tensor Tractography of Association and Projection Fibers in Normally Developing Children and Adolescents[END_REF][START_REF] Giorgio | Longitudinal changes in grey and white matter during adolescence[END_REF]. Regional patterns of sexual differences in macrostructural maturation trajectories have also been reported, notably in the limbic system with the amygdala, the hippocampus and the prefrontal cortex where girls showed an early maturational peak as compared to boys [START_REF] Eliot | Neurosexism: the myth that men and women have different brains[END_REF][START_REF] Goddings | The influence of puberty on subcortical brain development[END_REF][START_REF] Herting | Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes[END_REF][START_REF] Lenroot | Sexual dimorphism of brain developmental trajectories during childhood and adolescence[END_REF]. However, other studies did not find sex by age interaction during adolescence for subcortical regions such as basal ganglia, thalamus, hippocampus or amygdala [START_REF] Koolschijn | Sex differences and structural brain maturation from childhood to early adulthood[END_REF][START_REF] Wierenga | Unraveling age, puberty and testosterone effects on subcortical brain development across adolescence[END_REF].

Affective disorders are also part of the pattern of sexual differences with approximately 2:1 female:male prevalence ratio during adolescence [START_REF] Angold | Pubertal changes in hormone levels and depression in girls[END_REF][START_REF] Angold | Puberty and depression: the roles of age, pubertal status and pubertal timing[END_REF][START_REF] Angold | Puberty and Depression[END_REF]. Previously cited limbic regions had been implicated in the so-called "developmental mismatch hypothesis" proposing that the subcortical structures maturing earlier than the cortical structures was leading to the stereotypical adolescent behavior (see review by Mills et al. 2014). [START_REF] Simmonds | Developmental stages and sex differences of white matter and behavioral development through adolescence: A longitudinal diffusion tensor imaging (DTI) study[END_REF] found that frontosubcortical WM connections (uncinate fasciculus, superior longitudinal fasciculus and cingulum) implicated in emotional processing mature later than most white matter bundles during childhood. Further, another study found that depressed patients had lower fractional anisotropy in this corticalsubcortical connectivity [START_REF] Versace | Right Orbitofrontal Corticolimbic and Left Corticocortical White Matter Connectivity Differentiate Bipolar and Unipolar Depression[END_REF]. In the case of the limbic system, the maturational mismatch could be related to the increase emotional reactivity and sensitivity, and thereby to an increase risk for affective disorders during adolescence compared to childhood [START_REF] Casey | The Adolescent Brain[END_REF].

Research on emotion dysregulation during adolescence has given a large prominence to the emotional symptomatology (i.e. depression, bipolar disorder, and anxiety disorder) but less to positive attributes (e.g. generosity, reliability, good sense of humor) that are related to the adolescent's well-being [START_REF] Gillham | Character strengths predict subjective well-being during adolescence[END_REF] and may be protective from emotional disorders [START_REF] Vidal-Ribas | Positive attributes in children and reduced risk of future psychopathology[END_REF]. Once again, pubertal timing plays an important role in the emotional dysregulation with increased risks when girls mature too early or when boys mature too late [START_REF] Graber | Pubertal timing and the development of psychopathology in adolescence and beyond[END_REF].

In the literature, most of the results on sex differences in brain maturation during adolescence were based on cross-sectional study designs with large samples or large age range [START_REF] Koolschijn | Sex differences and structural brain maturation from childhood to early adulthood[END_REF][START_REF] Menzies | The effects of puberty on white matter development in boys[END_REF][START_REF] Satterthwaite | Sex Differences in the Effect of Puberty on Hippocampal Morphology[END_REF]. Although informative, crosssectional studies are limited because they can only provide estimated and not individual trajectories. The existing longitudinal studies neither included large sample size [START_REF] Bava | Longitudinal characterization of white matter maturation during adolescence[END_REF][START_REF] Giorgio | Longitudinal changes in grey and white matter during adolescence[END_REF][START_REF] Dennison | Mapping subcortical brain maturation during adolescence: evidence of hemisphere-and sex-specific longitudinal changes[END_REF] nor had a large age range, nor focused on sexual differences because of the non sex parity of their sample [START_REF] Bava | Longitudinal characterization of white matter maturation during adolescence[END_REF][START_REF] Dennison | Mapping subcortical brain maturation during adolescence: evidence of hemisphere-and sex-specific longitudinal changes[END_REF][START_REF] Giorgio | Longitudinal changes in grey and white matter during adolescence[END_REF][START_REF] Lebel | Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood[END_REF][START_REF] Wierenga | Unique developmental trajectories of cortical thickness and surface area[END_REF]. Recently, few longitudinal studies had the power to tackle the question of sex differences in brain maturation during adolescence [START_REF] Fish | Sex-biased trajectories of amygdalo-hippocampal morphology change over human development[END_REF][START_REF] Wierenga | Unraveling age, puberty and testosterone effects on subcortical brain development across adolescence[END_REF] but more a needed to disentangle the effect of sex, age and puberty.

For these reasons, this study investigated the sex and puberty effects on brain and behavioral changes during adolescence by -1. taking advantage of a two time point longitudinal design of a large sample of adolescents with the same age at 14 and 16 years old and a dedicated longitudinal preprocessing methodology [START_REF] Ashburner | Symmetric diffeomorphic modeling of longitudinal structural MRI[END_REF] -2. looking at the sexual differences of the brain maturation with a multimodal neuroimaging approach focusing on grey and white matter using whole brain and specific limbic system regions of interest analyses -3. linking during puberty the sexual maturation differences of the limbic system to the emotional dysregulation using psychopathological measures related to affective disorders and also personality traits that may constitute vulnerability factors. We hypothesized that boys and girls would have a different developmental mismatch in grey matter regions and white matter bundles of the limbic system, and that this differential maturation would be in return related to sex differences on emotion regulation and psychopathology during adolescence.

Materials and Methods

Participants

Longitudinal datasets from three hundred and thirty-five adolescents (175 females; 160 males) were drawn from the Imagen database, a larger sample recruited in eight European cities at the age of 14. Two sites (138 and 197 subjects from Paris and Dresden respectively) conducted an MRI exam at both 14 and 16 years old in addition to questionnaires and neuropsychological battery tests at both times. Written informed consent and assent had been given by both parents and participants. The study had been approved by the local ethic committees. A detailed description of recruitment and assessment procedures, and exclusion and inclusion criteria has been published [START_REF] Schumann | The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology[END_REF]. Notably, any obvious psychopathology (e.g. bipolar disorder, schizophrenia, or major neuro-developmental disorders) constituted non-inclusion criteria.

Self-Report Questionnaires

The pubertal measure was assessed with the Puberty Development Scale (PDS; [START_REF] Petersen | A self-report measure of pubertal status: Reliability, validity, and initial norms[END_REF]), a measure of physical development with separate items for males and females.

Questionnaires are adapted for each sex, such as menarche in females and voice changes in males. Substance use was reported using the Alcohol Use Disorders Identification Test (AUDIT).

The adolescent psychiatric symptoms and their psychosocial impact were assessed with the Development and Well-Being Assessment (DAWBA, www.dawba.com), a self-administered diagnostic questionnaire consisting of open and closed questions [START_REF] Goodman | The Development and Well-Being Assessment: Description and Initial Validation of an Integrated Assessment of Child and Adolescent Psychopathology[END_REF]. The DAWBA generates probabilities of having DSM-IV diagnoses that are subsequently validated by experienced clinicians from the IMAGEN consortium. Diagnoses from affective disorders (e.g. anxiety, depression bands) were tested here. Specifically, The Youth Strengths Inventory (YSI), within the DAWBA, asks about adolescent's positive attributes. The first part of the questionnaire is dedicated to "positive characteristics" (e.g. how generous, affectionate, caring he is) with 8 items. The second part of the questionnaire requests about "positive actions" that please others or things that the adolescent is proud of in 11 items (e.g. how good at sport, well behaved, polite he is proud of). Each item is scored on a three-point Likert scale (0: no, 1: a little, 2: a lot). Summing the score of each item per part generates two variables, "positive characteristics" (from 0 to 16) and "positive actions" (from 0 to 22). The sum of these two variables generates the global variable "total positive attributes" (from 0 to 38).

The Strengths and Difficulties Questionnaire (SDQ), a self-reported questionnaire [START_REF] Goodman | The Strengths and Difficulties Questionnaire: a pilot study on the validity of the self-report version[END_REF] generates a total difficulties score (reflecting emotional problems, conduct problems, hyperactivity and peer problems). Internalizing (i.e., anxious and depressive) and externalizing (i.e., aggressive and hyperactive) behaviors [START_REF] Achenbach | Manual for the Child Behavior Checklist/2-3 and 1992 Profile[END_REF]) can be measured with the SDQ.

Externalizing score is obtained by summing conduct problems score and hyperactivity score; internalizing score is obtained by summing emotional problems score and peer problems score, each scale being ranged from 0 to 20.

Imaging acquisitions

All subjects underwent imaging exams on a SIEMENS Trio 3T scanner, including an anatomical and a diffusion sequences. All exams were assessed by a clinical neuroradiologist for structural abnormalities.

Image processing

T1-weighted images. To correct for differences of neck rotation between each subject's acquisitions, all images were roughly realigned and cropped bellow the cerebellum. Then, intrasubject registration was performed using SPM12's Longitudinal Registration Toolbox [START_REF] Ashburner | Symmetric diffeomorphic modeling of longitudinal structural MRI[END_REF] involving combining rigid-body registration, intensity inhomogeneity correction, and non-linear diffeomorphic registration. This step generates the subject's mid-point image between 14 and 16 years, the maps of the Jacobian determinants and the deformation fields estimated from each time-point scan to the mid-point image. The subject's mid-point image was segmented into grey and white matter with SPM12's Segmentation Toolbox with tissue priors simulated at 15 years using TOM8 toolbox (http://www.neuro.uni-jena.de/software/tom/). Grey and white matter maps of the mid-point image were modulated by the Jacobian determinants of each time-point. All grey and white matter maps of the mid-point images were spatially normalized to the standard space of the Montreal Neurological institute (MNI) using the DARTEL nonlinear image registration procedure. This step involves the iterative creation of their representative template and the extraction of the deformation fields from each image to the aforementioned template. The deformation fields obtained were then applied to the modulated grey and white matter maps preserving the regional amount of signal. Finally, modulated normalized maps of grey and white matters were smoothed with an 8 mm full width at half maximum (FWHM) Gaussian kernel. Global GM, WM and cerebrospinal fluid (CSF) volumes were computed for each participant. Total intracranial volume (TIV) was defined by summing GM, WM and CSF volumes. GM volumes were extracted from the amygdala, the hippocampus and the prefrontal cortex such as defined by [START_REF] Mills | The developmental mismatch in structural brain maturation during adolescence[END_REF] using WFU PickAtlas (SPM toolbox; http://fmri.wfubmc.edu/software/PickAtlas). The prefrontal cortex was defined by combining the following subdivisions: precentral gyrus, superior frontal gyrus (dorsolateral, orbital, medial and medial orbital parts), middle frontal gyrus (middle and orbital parts), inferior frontal gyrus (opercular, triangular, orbital parts), Rolandic operculum, olfactory cortex and paracentral lobule. DTI. Diffusion data preprocessing was performed using FMRIB Diffusion Toolbox (FDT) in FMRIB Software Library (FSL) (www.fmrib.ox.ac.uk/fsl) and consisted of affine registration to the first b=0 image for head motion and eddy currents correction, brain extraction using the Brain Extraction Tool (BET), and voxel-wise diffusion tensor fitting to obtain images of fractional anisotropy (FA), mean diffusivity (MD), Axial Diffusivity (AD) and Radial Diffusivity (RD). FA maps were coregistered to the corresponding native white matter maps derived from the T1weighted image preprocessing. Then, the coregistered images were normalized into the standard space by applying successively the intra-subject (longitudinal) and inter-subject (DARTEL) registrations done during T1-weighted image preprocessing. Additional processing was performed using FSL's Tract-Based Spatial Statistics (TBSS) toolbox [START_REF] Smith | Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data[END_REF].

Normalized FA maps were eroded and mean FA image created and thinned to obtain a mean FA skeleton, which represents the centers of all tracts common to all subject. This skeleton was then thresholded to FA>0.2 to keep only the main tracts. Each subject's FA, MD, AD and RD data were then projected onto the skeleton and the resulting data fed into voxel-wise statistics. Global FA, MD, AD and RD values have been extracted for each participant. FA, MD, AD and RD were extracted from the cingulum and uncinate using the Johns Hopkins University (JHU) tractography atlas from FSL.

Statistics

Participants with bad image quality or failed processing of T1-weigthed or diffusion images, as well as participants with invalid PDS (e.g. PDS decreasing between 14 and 16) or with any symptom of alcohol misuse (AUDIT score > 6 for girls; AUDIT score > 7 for boys) were excluded (See Supplementary Figure 1). Consequently, our final sample was constituted of 156 subjects (84 girls).

Voxel-Based Analyses. Macrostructural whole-brain voxel wise analyses were carried out within the general linear model (GLM) framework using SPM12. Subject, center, TIV, sex, PDS and sex-by-PDS interaction were included in a flexible factorial design. Analyses were performed on 312 GM and WM images (i.e. 156 subjects) with an explicit mask thresholded at 0.2. At the voxel level, statistical significance was set at p < 0.05 FWE (Family Wise Error) corrected for multiple comparisons. Microstructural whole-brain voxel wise comparisons on FA and MD maps were tested within a similar GLM framework using a randomization-based method within FSL (5,000 permutations) in the same sample as macrostructural analyses. AD and RD were compared when differences in FA values were observed. Subject, center, sex, PDS and sex-by-PDS interaction were included in the design. Statistical thresholds were set at p < 0.05 FWE corrected and Threshold-Free Cluster Enhancement (TFCE) corrected. Similar voxel-based analyses of macro-and micro-structures were conducted with age instead of PDS in the design. Cluster sizes were set at least to 50 voxels. Brain locations were reported as x, y and z coordinates in Montreal Neurological Institute (MNI) space.

Other Analyses. Extracted imaging values (global and regional grey and white matter volumes, and mean values of each DTI index: FA, MD, AD and RD) and behavioral data (DAWBA, SDQ, YSI variables) were analyzed using R Cran software (version 3.3.1 "Bug in Your Hair" (2016.06.21)). Sex-by-PDS related changes on longitudinal imaging and behavioral data were analyzed using linear mixed models with restricted maximum likelihood (REML), to account for the repeated measures on each individual (lme4 package, version 1.1-12). PDS at baseline, PDS difference, sex, and sex-by-PDS difference interaction were entered as fixed effects and subject and center as nested random effects. TIV was entered as confounding variable in macrostructural analyses. Similar analyses were conducted with age instead of PDS in the statistical models. In order to assess the benefit of using PDS instead of age, we compared models with age only and models with age and PDS. We used Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), that are standardized model-fit metrics, to compare the two models and tested the favored model with the lower AIC and BIC values using a log likelihood ratio test.

Causal mediation analyses were conducted to determine whether the sex effects on longitudinal changes in macro-and micro-structures within the ROI previously identified could mediate the sex effects on longitudinal behavioral changes along puberty between 14 and 16. As prerequisite, mediation analyses were conducted only on behavioral questionnaires and ROI that have a significant sex-by-PDS interaction. The analyses were performed using a set of GLM to derive the mediation and direct effects from the total effect (mediation package, version 4.4.5).

Behavioral changes (time 2time 1) were entered as a dependent variable, and PDS difference (time 2time 1), sex, PDS difference-by-sex interaction and PDS at 14 as independent variables within a regression model. Each ROI indices (time 2time 1) was entered as a mediator variable, sex as the treatment of the mediation, and center as confounding variable. This mediation model was performed using 5,000 Monte Carlo draws for nonparametric bootstrap. In causal mediation analysis, a significant mediating effect is defined as a 95% confidence interval that does not include 0.

Results

PDS, sex and self-report questionnaires

Within our sample of 156 subjects (84 girls and 72 boys) analyzed at both assessment times, girls had higher PDS scores than boys but not significant difference in age (see Table 1 and Supplementary Figure 2).

The YSI questionnaire yielded sex-by-PDS interaction with total positive attributes (p = 0.04, see Table 2) and more specifically on the subscale "positive characteristics" (p = 0.02). "Positive characteristics" and "total positive attributes" increased in boys and decreased in girls with puberty between 14 and 16 years.

No sex-by-PDS interaction was found in the SDQ or the DAWBA questionnaires (see Supplementary Table 1).

Using age instead of PDS, no significant sex-by-age interactions were found for all behavioral questionnaires but for the "positive characteristics" (p = 0.03, see Supplementary Table 2). We did not find a favored model comparing "age" and "age plus PDS" models (See Supplementary Table 3).

Imaging

Global measures

Global GM volume decreased along puberty, with a steeper rate in girls compared to boys (see Figure 1, Table 3). Global WM volume increased with a steeper rate in boys compared to girls.

Global FA increase and global MD decrease were found for all subjects but no sex-by-PDS interaction. Global GM and WM volumes followed similar changes when using age instead of PDS (see Supplementary Table 4). Global diffusion indices displayed significant sex-by-age interactions when using age instead of PDS. We did find favored models using "age plus PDS" instead of "age" only for global GM and WM volumes (See Supplementary Table 5).

Voxel-based and regional measures

The voxel-wise sex-by-PDS interaction showed a significant steeper GM volume decrease in girls in the prefrontal cortex, caudate, putamen, thalamus, Heschl's gyrus and post-central gyrus, while boys had a significant steeper GM volume increase in the amygdala-hippocampal complex, precentral gyrus and parts of the occipital pole (see Figure 3, Supplementary Table 6). A steeper WM volume increase was detected in boys compared to girls in most parts of the brain except in bilateral external capsule, where the volume decreased more in girls than in boys. No voxel-wise sex-by-PDS interaction was found in FA or MD. The voxel-wise sex-by-age interaction showed similar results in GM and WM volumes than the ones with PDS (see Supplementary Figure 3 and Supplementary Table 7). Unlike the PDS, we found significant voxel-wise sex-by-age interaction for FA and MD (see Supplementary Figure 4 and Supplementary Table 8).

ROI investigations of macrostructure confirmed sex-by-PDS interactions in amygdala, hippocampus and the prefrontal cortex, and concerning microstructure, we found only trends for a sex-by-PDS interaction in the cingulum and the uncinate but with no significant change in boys or girls taken separately (see Table 4). Boys displayed amygdala and hippocampus volumes increases and a prefrontal cortex volume low decrease whereas girls displayed amygdala and hippocampus volumes decreases and a prefrontal cortex volume low decrease. ROI investigations of macro-and microstructure showed the same sex-by-age interactions (see Supplementary Table 9).

Mediation analyses

Mediation analyses showed that amygdala volume change accounted for 32.5% (p = 0.024) and hippocampus volume change for 29.91% (p = 0.016) of the total effect between sex and "positive characteristics" along puberty (see Figure 3, Table 5). Amygdala and hippocampus volumes increases in boys were related to "positive characteristics" increase, while amygdala and hippocampus volumes decreases in girls were related to "positive characteristics" decrease.

No mediation effect of the prefrontal cortex volume or of the uncinate and cingulum microstructural measures was found with YSI scores.

Discussion

Sexual differences of the brain maturation were identified in global GM and WM volumes and in regions of the amygdalo-hippocampal complex using a longitudinal multimodal neuroimaging approach in adolescents between 14 and 16 years. In contrast, no sexual difference of the microstructure maturation was detected. Additionally, we found sex differences on emotional regulation as measured by positive personality traits and this effect was related to the maturation of regions of the limbic system.

The sex effects on the adolescents' "positive characteristics" changes, that are a subscale of the positive personality traits scale, were identified to be mediated by the hippocampus and amygdala maturation. Positive attributes are meant to gather (1) positive character items (e.g. how the adolescent feels generous, affectionate, caring, social, easy-going) and (2) positive action items (e.g. how the adolescent is proud to be good at sport, well behaved, polite, helpful at home). Globally, they are positively and closely related to current levels of adolescent's wellbeing [START_REF] Gillham | Character strengths predict subjective well-being during adolescence[END_REF]. "Positive characteristics" are assimilated to personality strengths that promote connections to other people which increase positive affect, suggesting that interpersonal interactions play an important role in the protection from depression [START_REF] Gillham | Character strengths predict subjective well-being during adolescence[END_REF][START_REF] Peterson | Character Strengths and Virtues: A Handbook and Classification[END_REF]. In our sample, the "positive characteristics" correlated negatively with internalizing, externalizing and total difficulties scores (see Supplementary Table 11). Externalizing behaviors describe disruptive and dysregulated behaviors such as hyperactivity or impulsivity whereas internalizing problems involve disturbances in emotion or mood [START_REF] Graber | Pubertal timing and the development of psychopathology in adolescence and beyond[END_REF][START_REF] Perle | The Association Between Internalizing Symptomology and Risky Behaviors[END_REF][START_REF] Yong | Mediators of the Associations Between Externalizing Behaviors and Internalizing Symptoms in Late Childhood and Early Adolescence[END_REF]. In this context, positive personality traits may contribute to a decreased risk of developing emotional disorders during early adulthood, as demonstrated by [START_REF] Bromley | Personality strengths in adolescence and decreased risk of developing mental health problems in early adulthood[END_REF][START_REF] Vidal-Ribas | Positive attributes in children and reduced risk of future psychopathology[END_REF]. The mediation by the amygdalo-hippocampal complex, limbic structures largely involved in the emotional regulation processing, has to be put in the light of the sex-related differences on the maturation of these regions [START_REF] Davidson | Neural and behavioral substrates of mood and mood regulation[END_REF][START_REF] Giedd | Structural Magnetic Resonance Imaging of the Adolescent Brain[END_REF][START_REF] Goddings | The influence of puberty on subcortical brain development[END_REF][START_REF] Herting | Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes[END_REF]. In normal development, the amygdala and hippocampus continued to increase in volume during puberty in both boys and girls with differential trajectories [START_REF] Goddings | The influence of puberty on subcortical brain development[END_REF][START_REF] Herting | Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes[END_REF]. Differences in the progression of brain structure could lead to important psychiatric disorders in post-adolescence, which prevalence is notable during this period [START_REF] Lebel | Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood[END_REF][START_REF] Paus | Why do many psychiatric disorders emerge during adolescence?[END_REF]. For example, variations of amygdala and hippocampus have been involved in affective disorders, where volumes decreases were demonstrated in patients with emotional symptomatology compared to controls [START_REF] Blumberg | Amygdala and hippocampal volumes in adolescents and adults with bipolar disorder[END_REF][START_REF] Rajmohan | The limbic system[END_REF]. In summary, emotion dysregulation leading to emotional disorders is related to limbic system maturation, in particular amygdala and hippocampus changes during adolescence.

According to our results, girls could be more sensitive to emotional disorders via positive personality traits and limbic structures volumes decreases, suggesting that a faster and precocious maturation during adolescence reflects a vulnerable framework for emotional dysregulation in early adulthood. As an echo to that, we did find a significant PDS related increase of risk for separation anxiety in girls only (See Supplementary Table 1). These elements taken together seem to point out an increased risk for psychopathology in early maturation in girls. [START_REF] Graber | Pubertal timing and the development of psychopathology in adolescence and beyond[END_REF] extended this relation in boys maturing too early or too late, which presented elevated symptomatology of psychopathology. As for boys, we did find that amygdalo-hippocampal complex increase was related to "positive characteristics" increase. Another study found that amygdala-mPFC connectivity related to early life stress in adolescence was associated with anxiety and depression in girls but again not in boys [START_REF] Burghy | Developmental pathways to amygdalaprefrontal function and internalizing symptoms in adolescence[END_REF]. A long-standing explanation has been that men's more active responses to their negative moods may be more adaptive on average than women's less active, more ruminative responses [START_REF] Nolen-Hoeksema | Sex differences in unipolar depression: evidence and theory[END_REF]. In our study, boys with no amygdalo-hippocampal complex increase could be considered as late maturing boys with no increase in positive attributes, which, in turn, may not be protective for developing psychopathology, contrary to boys with amygdalo-hippocampal complex and positive attribute increases. Otherwise, no sex-by-PDS interaction was detected, neither in the variables about affective disorders of the DAWBA questionnaire nor in the SDQ questionnaire.

As only healthy adolescents were recruited in this study, the lack of pathological subjects might have decreased the statistical power of clinical variables to probe psychiatric dimensions.

The global patterns of brain maturation were confirmed in our study, with a global GM volume decrease and a global WM volume increase in macrostructure [START_REF] Giedd | Sexual dimorphism of the developing human brain[END_REF], that might be an indication of a reduction in neuropil in the grey matter (e.g. synaptic pruning, glial cell reduction) and an encroachment of white matter growth (K. L. Mills et al., 2014;[START_REF] Paus | Why do many psychiatric disorders emerge during adolescence?[END_REF]. In microstructure, a global mean FA increase and a global mean MD decrease were found, suggesting more organized fiber bundles [START_REF] Schmithorst | White matter development during adolescence as shown by diffusion MRI[END_REF][START_REF] Wang | Sex differences in white matter development during adolescence: A DTI study[END_REF].

We confirmed the sexual differences of brain maturation illustrated by a steeper global GM volume decrease in girls and a steeper global WM volume increase in boys [START_REF] Giedd | Sexual dimorphism of the developing human brain[END_REF][START_REF] Goddings | The influence of puberty on subcortical brain development[END_REF][START_REF] Lenroot | Sex differences in the adolescent brain[END_REF]. Regionally, the sexual differences were also confirmed in some specific regions as limbic regions and prefrontal cortex. These regions highlighted a sexual differentiation in maturation rates, with differential decreasing trajectories in prefrontal cortex volumes in boys and girls whereas trajectories were opposite in the amygdalohippocampal complex. According to the dual systems model, the prefrontal cortex involved in cognitive control follows a protracted development whereas limbic regions involved in processing affect follow a more dynamic model [START_REF] Casey | The Adolescent Brain[END_REF][START_REF] Gogtay | Dynamic mapping of human cortical development during childhood through early adulthood[END_REF][START_REF] Mills | The developmental mismatch in structural brain maturation during adolescence[END_REF]. In addition to confirming the differentiation in maturation rates between cortical and subcortical structures across puberty, we demonstrated that the sex plays an important role upon this mismatch. Through this design, we illustrated mainly a delayed maturation in boys and an accelerated maturation in girls. From one perspective, the relation in girls between what appears to be an accelerated amygdalo-hippocampal maturation and a decrease of "positive characteristics" could be interpreted as consistent with the dual system model where heightened reactivity of the subcortical regions would lead to more affectively driven behaviors and confer more risks for affective disorders [START_REF] Casey | The Adolescent Brain[END_REF]. From another perspective, we did not find the same relation for the prefrontal cortex, the second system of the dual system model. In this case, our results could be consistent with an alternative model where vulnerability to affective disorders is not due to a delayed prefrontal maturation and a failure of regulation and controls over the subcortical system [START_REF] Davey | The emergence of depression in adolescence: development of the prefrontal cortex and the representation of reward[END_REF]. From a general point of view, we can only consider our data in the context that a delayed and protracted maturation appears to be protective from emotional disorders.

No sex-by-PDS effect in the WM microstructural maturation between 14 and 16 was found, neither in whole brain nor limbic regions. Some studies have found sex-by-age interactions in FA from childhood to adulthood (Herting et al., 2017;[START_REF] Lebel | Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood[END_REF][START_REF] Schmithorst | Developmental differences in white matter architecture between boys and girls[END_REF][START_REF] Wang | Sex differences in white matter development during adolescence: A DTI study[END_REF]. These longitudinal studies had smaller sample size [START_REF] Bava | Longitudinal characterization of white matter maturation during adolescence[END_REF][START_REF] Wang | Sex differences in white matter development during adolescence: A DTI study[END_REF] or larger age range [START_REF] Lebel | Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood[END_REF], while others were crosssectional [START_REF] Herting | The impact of sex, puberty, and hormones on white matter microstructure in adolescents[END_REF]. With its longitudinal design on a large sample, our study should have the computational strength to detect such changes. However, we did use pubertal development scale instead of age, since it is more closely related to brain maturation and that our age range is rather narrow [START_REF] Goddings | The influence of puberty on subcortical brain development[END_REF]. As a confirmation, we did find sex differences for WM microstructural maturation when using age instead of PDS, but these results may be driven only by higher PDS increase for the same age range in boys as compared in girls giving in return an artificially sex-differential pace of brain maturation. Furthermore, brain maturation can be separated in distinct phases with rapid growth in childhood, followed by a slowing of growth in early-middle adolescence and an acceleration of growth again in late adolescence/early adulthood [START_REF] Simmonds | Developmental stages and sex differences of white matter and behavioral development through adolescence: A longitudinal diffusion tensor imaging (DTI) study[END_REF]. The limbic system appears to follow this pattern of maturation, with cingulum and uncinate undergoing substantial changes after adolescence [START_REF] Lebel | Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood[END_REF]. This period of little change that overlaps with our own study might account for the absence of significant sexual differences detected here on the WM maturation.

The longitudinal image processing and use of linear mixed-models specifically designed for repeated-measures constitute the main strengths of our study. Paired images underwent a dedicated processing pipeline to measure individual changes before performing spatial normalization and group analysis. In the first step of the model all time-points were registered to some form of within-subject average image, in order to avoid introducing an asymmetric bias and to ensure all images undergo the same number of interpolations [START_REF] Ashburner | Symmetric diffeomorphic modeling of longitudinal structural MRI[END_REF][START_REF] Reuter | Within-subject template estimation for unbiased longitudinal image analysis[END_REF][START_REF] Reuter | Highly accurate inverse consistent registration: A robust approach[END_REF][START_REF] Reuter | Avoiding asymmetry-induced bias in longitudinal image processing[END_REF]. This step is essential to guarantee the symmetry in the longitudinal processing. We were able to adapt our processing to diffusion images in order to adjust precisely both modalities in the same space. We used also appropriate statistical longitudinal models to take into account the dependence of repeated measurements within subject, and by doing so, providing increased statistical power reducing the confounding effect of between-subject variability [START_REF] Bernal-Rusiel | Statistical Analysis of Longitudinal Neuroimage Data with Linear Mixed Effects Models[END_REF].

The findings of this study must be considered in the light of some limitations. First, we ran all our analyses using the pubertal development scale (PDS). First, it is a self-report measurement based on only five questions and can be prompted to subjectivity. Second, it measures not exactly the same physical characteristics in both sexes (e.g. breast development, testis size) which can bias the scale when comparing boys and girls brain maturations. In our study, we did not measure the Tanner stage where a clinician examines the participant and evaluates the degree of puberty (Marshall andTanner, 1970, 1969). However, reliable studies have concluded that despite its limitations, PDS still constitutes a suitable tool to measure the degree of puberty [START_REF] Bond | A comparison of self-reported puberty using the Pubertal Development Scale and the Sexual Maturation Scale in a school-based epidemiologic survey[END_REF][START_REF] Dorn | Measuring Puberty[END_REF][START_REF] Petersen | A self-report measure of pubertal status: Reliability, validity, and initial norms[END_REF] and remained useful and fundamental as predictor in assessing longitudinal changes within subjects, much more precise than age [START_REF] Brooks-Gunn | Validity of self-report measures of girls' pubertal status[END_REF]Herting et al., 2017;[START_REF] Petersen | A self-report measure of pubertal status: Reliability, validity, and initial norms[END_REF]. Given that girls in our sample have more advanced pubertal development than boys for the same age, our strategic choice seems to be the right one. Moreover, analyses conducted with age showed less significant results than with PDS within behavioral questionnaire and model fitting for the global T1-weighted measures was improved by adding the PDS.

In a second point regarding the temporal resolution, the current study had only two measures per subject, allowing for only a linear model to be examined as an estimate of change within a single individual (Herting et al., 2017). The two visits were close in time with a 2-year interval, necessary to detect subtle changes during puberty but maybe quite too narrow in view of changes during this period. Changes in brain maturation do not follow a linear curve; additional time points will allow the testing of non-linear slopes at the individual level and to detect medium effects of puberty. In the same vein considering the spatial resolution, we used a predefined set of brain ROI and, for example, the different subparts of the prefrontal region were not specifically considered in relation to their functions. Further investigations are needed to clarify the role of each region in the maturational mismatch of the limbic system.

The third limitation of this study is the use of Youth Strength Inventory questionnaire to study positive personality traits. Although part of the DAWBA, this questionnaire is often overlooked and not studied in the literature for symptomatology. Indeed, it is interesting that positive personality traits mirror emotional symptomatology in a study on healthy adolescents. Although being a self-report evaluation instead of a clinical measurement, it is, to our knowledge and available data, the only scale currently existing to measure positive personality traits subjectively.

As an unexpected finding, externalizing and internalizing disorders and diagnoses scores of the DAWBA didn't show any interaction between sex and puberty but correlated negatively with the positive personality traits. This should be confirmed in future studies.

Conclusion

We demonstrated that the vulnerability of emotional disorders could be explained by the mismatch of maturation rates of cortico/subcortical regions between sexes across puberty. The delayed brain maturation in boys compared to girls showed to be related with positive personality traits changes. These findings support that, beyond age, sex and puberty effects contribute to neurodevelopmental trajectories and emotional regulation in girls and boys during adolescence. (in red) on the relationship between sex and "positive characteristics" changes between 14 and 16 years using causal mediation analysis.

Captions to figures

  T1-weighted imaging. High-resolution T1-weighted images were collected using a magnetization prepared rapid acquisition gradient-echo (MPRAGE) sequence [Paris: repetition time (TR) = 2300 ms, echo time (TE) = 2.93 ms, inversion time (TI) = 900 ms, voxel size = 1.1×1.1×1.1 mm, flip angle = 9°; matrix size = 256x256x160 mm; Dresdren: TR=1900 ms, TE=2.26 ms, TI=900 ms, voxel size=1.0×1.0×1.0 mm, flip angle=9°; matrix size = 256x256x176 mm]. Diffusion Tensor imaging. The diffusion tensor images (DTI) were acquired using an Echo Planar imaging sequence (4 b-value=0 s/mm 2 and 32 diffusion encoding directions with b-value=1300 s/mm 2 ; 60 oblique-axial slices (angulated parallel to the AC/PC line); echo time ≈ 104 ms; 128x128 matrix; field of view 307x307mm; voxel size 2.4 x 2.4 x 2.4 mm).

  Notes: PDS: Puberty Developmental Scale; DTI: Diffusion Tensor Imaging; GM: Grey Matter; FA: Fractional Anisotropy; MD: Mean Diffusivity.

Figure 1 :

 1 Figure 1: Longitudinal effect of PDS on global GM and WM volumes, and global FA and MD

Figure 2 :Figure 3 :

 23 Figure 2: Voxel-based sex-by-PDS interaction for GM (top row) and WM (bottom row). Steeper

Table 1 :

 1 Sample demographics.

			girls	boys	total
		time-point				p-value
			(N = 84)	(N = 72)	(N = 156)
	non-European descents (N)	Baseline	5	6	11	0.79
	parent's Education Level (Mean± sd)	Baseline	4.21 ± 1.52	4.06 ± 1.54	4.14 ± 1.53	0.56
	Pubertal Development	Baseline	3.15 ± 0.47	2.55 ± 0.55	2.87 ± 0.59	2.62e -11
	Scale (Mean ± sd)	Follow-up	3.70 ± 0.25	3.20 ± 0.40	3.47 ± 0.41	2.23 e -15
	age in years	Baseline	14.43 ± 0.42	14.36 ± 0.41	14.40 ± 0.42	0.32
	(Mean ± sd)	Follow-up	16.70 ± 0.48	16.59 ± 0.53	16.65 ± 0.50	0.15

Notes: p values from t-test or X 2 tests.

Table 2 :

 2 Effects of PDS by sex on psychometric measures.

	task	measure	sex	score change (per PDS point)	t-test (degree of freedom)	p-value	interaction sex-by-PDS p-value
		Positive characteristics	boys girls	1.06 -0.50	t(1.48) = 1.32 t(1.42) = 0.62	0.35 0.62	0.02
	YSI	Positive actions	boys girls	0.08 -0.71	t(8.23) = 0.13 t(6.49) = 1.11	0.89 0.30	0.34
		Total positive attributes	boys girls	1.18 -1.22	t(6.51) = 1.28 t(5.26) = 1.38	0.24 0.22	0.04
	Notes: YSI: Youth Strengths Inventory; PDS: Puberty Developmental Scale

Table 3 :

 3 Effects of PDS by sex on global imaging measures.

	imaging	global measure	sex	change estimate (per PDS point)	t-test (degree of freedom)	p-value	interaction sex-by-PDS p-value
		GM (cm 3 )	girls boys	-14.34 -2.52	t(1.35) = 6.40 t(1.30) = 1.13	0.05 0.42	3.77e -11
	T1-weighted
		WM (cm 3 )	girls boys	1.48 8.24	t(1.01) = 0.79 t(1.07) = 4.55	0.57 0.124	5.68e -16
			boys	0.0084 3	t(1.12) = 2.54	0.21
		FA					0.83
			girls	0.00814	t(1.15) = 2.42	0.22
	DTI	MD (10 -3 mm 2 /sec)	boys girls	-1.02e -05 -7.241e -06	t(23.64) =7.11 t(23.73) = 4.95	2.56e-07 4.85e-05	0.15

Notes: PDS: Puberty Developmental Scale; DTI: Diffusion Tensor Imaging; GM: Grey Matter; WM: White Matter; FA: Fractional Anisotropy; MD: Mean Diffusivity.

Table 4 :

 4 Effects of PDS by sex on Region Of Interest measures.

Table 5 :

 5 Mediation of brain volume changes of the amygdala and the hippocampus on the relationship between sex and "positive characteristics" changes between 14 and 16 years using causal mediation analysis.

	effect type	estimate	95% confidence	p-value
	mediator variable: amygdala volume change
	mediation effect	0.09218	[0.00826 -0.21]	0.0244
	direct effect	-0.37583	[-0.71529 --0.13]	<2e -16
	total effect	-0.28364	[-0.54818 --0.09]	0.0016
	proportion mediated	-0.32500	[-0.86246 --0.04]	0.0260
	mediator variable: hippocampus volume change
	mediation effect	0.0848	[0.0174 -0.16]	0.0160
	direct effect	-0.3685	[-0.6742 --0.15]	<2e -16
	total effect	-0.2836	[-0.5512 --0.09]	0.0008
	proportion mediated	-0.2991	[-0.9116 --0.06]	0.0168

Acknowledgments

This work received support from the European Union-funded FP6 Integrated Project IMAGEN (LSHMCT-2007-037286), the French funding agency ANR (ANR-12-SAMA-0004), an Eranet-Neuron grant (project AF12-NEUR0008-1-WN2NA) and the Fondation de France (2012-00033703), the Fondation pour la Recherche Médicale (FRM; DPA20140629802; DPP20151033945), the Fédération pour la Recherche sur le Cerveau (FRC Neurodon 2015), the Fondation de l'Avenir (AP-RM-17-013), authors also thank the Strasbourg University for study promotion in France, the Bundesministerium für Bildung und Forschung (BMBF Grants 01EV0711 and 01EE1406B), and the Deutsche Forschungsgemeinschaft (DFG SFB 940/1 and SFB 940/2). PBF was supported by a doctoral fellowship from the Fondation de France (2014-0052246).