Sébastien Jacquemont

Understanding how the human brain matures from infancy through adolescence is essential for identifying factors that influence health outcomes. Magnetic resonance imaging (MRI) provides powerful tools for characterizing structural, microstructural, functional, and metabolic aspects of brain development; however, many pediatric imaging studies are constrained by small sample sizes, limited geographic diversity, lack of longitudinal measurement, restricted age ranges, and heterogeneous acquisition protocols. While recent large-scale neuroimaging consortia have begun to address these challenges, such resources remain limited in the Canadian context, creating a critical gap. The Canadian Pediatric Imaging Platform (C-PIP) has developed a multisite neuroimaging framework across three of Canada’s largest pediatric hospitals: Alberta Children’s Hospital (Calgary), Centre Hospitalier Universitaire Sainte-Justine (Montréal), and the Hospital for Sick Children (Toronto). These three sites were united through a national initiative, leveraging high-performance MRI systems to accelerate Canadian neurodevelopmental research. In addition, C-PIP has implemented a centralized data sharing infrastructure to facilitate open science and collaborative research across institutions. Here, we describe the development and implementation of a multimodal MRI acquisition protocol designed to support multimodal longitudinal studies of typical and atypical brain development spanning infancy through adolescence. Within-site stability and cross-site stability of C-PIP core MRI sequences were evaluated using a travelling-participant study. The C-PIP MRI protocol establishes a harmonized, multisite imaging framework that supports large-scale investigations of pediatric brain development and disorders. Highlights: The Canadian Pediatric Imaging Platform (C-PIP) is a national initiative acquiring longitudinal phenotyping and neuroimaging data from 820 children aged 0-18 years across three Canadian provinces (Alberta, Ontario and Québec). C-PIP has developed a harmonized, developmentally sensitive neuroimaging protocol to collect high-quality, comprehensive imaging data across multiple modalities. This paper outlines the development, considerations and implementation of the C-PIP neuroimaging protocol across sites. Preliminary cross-site validation analyses demonstrate good within- and between-site stability in neuroimaging measures. Graphical

Copy number variations (CNVs) at the 16p11.2 locus are major genetic risk factors for neurodevelopmental conditions and drive reciprocal alterations in cerebral white matter structure and connectivity. However, whether these alterations reflect differences in the axonal or myelin compartments remains unresolved with conventional diffusion MRI. We combined diffusion MRI with magnetization transfer saturation mapping across two ComBat-harmonized cohorts comprising 80 controls, 26 16p11.2 deletion carriers, and 13 duplication carriers. We quantified axonal volume, myelin volume, and aggregate g-ratio of white matter tracts forming the structural connectome. Metrics were evaluated using normative modeling across tract length, resting-state functional networks, and cortical nodes. We additionally examined 29 individuals carrying other neurodevelopmental CNVs. 16p11.2 gene dosage was associated with regional reciprocal differences in tract axonal volume, with greater volume in deletion carriers and lower volume in duplication carriers. In contrast, myelin volume did not differ significantly from controls. This mismatch resulted in pervasive aggregate g-ratio alterations, with higher g-ratios (relatively thinner myelin sheaths) in deletion carriers and lower g-ratios (relatively thicker sheaths) in duplication carriers. Regional g-ratio alterations also extended to other neurodevelopmental CNVs. These findings resolve some of the biological ambiguity underlying white matter alterations in 16p11.2 CNV carriers, implicating the axonal compartment while revealing comparatively stable myelin volume. The disrupted axon-myelin scaling results in connectome-wide g-ratio alterations and may represent a shared microstructural hallmark of genetic neurodevelopmental risk.

Metformin has emerged as a candidate drug for Fragile X Syndrome (FXS) after promising results in animal studies and small-scale trials. A three-site registered clinical trial (RCT) demonstrated effects of metformin on behavioral symptoms in FXS in participants aged 6–11. Resting state electroencephalography (RS-EEG) was also collected during the RCT. We present results from analyses of RS-EEG and associated adaptive behavior measures. We observed significant metformin treatment effects on EEG measures associated with cortical excitation/inhibition balance (periodic gamma), brain maturation (periodic alpha), and signal complexity. Significantly affected EEG measures moved toward values associated with typical development. We also observed associations between more typical EEG activity and improved adaptive behavior scores in the treatment group. Additional significant effects and associations were observed when considering only participants without molecular mosaic. Results confirm metformin’s potential as a treatment option and encourage further research into its effects on functional brain activity.

Autistic individuals show elevated rates of co-occurring neurodevelopmental and mental health conditions, yet the genetic architecture of those comorbidities remains unclear. Using phenotypic ( N = 74,204) and genetic ( N = 17,582) data from the SPARK study, we investigated the factor structure, heritability, genetic correlation with autism ( pleiotropy ) and corresponding conditions in the general population ( additivity ). First, confirmatory factor analysis identified three correlated factors mirroring general population patterns: behavioural (ADHD, disruptive behaviour disorders), cothymic (depression, anxiety), and thought disorder (schizophrenia, bipolar). Second, all three factors had significant SNP heritabilities whilst rare variants were not associated with the tested factors in our sample. Third, polygenic scores and genetic correlations revealed positive shared genetics between the three factors and corresponding conditions in the general population but not with autism, supporting the additivity hypothesis. Fourth, within-family analyses ( N = 5236 trios) demonstrated direct but not indirect genetic effects for the behavioural and cothymic factors. In sum, we find evidence for additive effects of other genetic factors in contributing to some latent co-occurring neurodevelopmental and mental health conditions in autism.

Whether the heterogeneity of psychiatric conditions converges on shared neurophysiological alterations or translates into distinct signatures remains unclear. We assembled high-density electroencephalogram (hd-EEG) resting-state recordings from 4,812 individuals aged 5 months to 66 years across 11 psychiatric conditions, a broad spectrum of rare genetic variants, and typically developing (TD) individuals. We established normative developmental trajectories of source-space EEG across spectral organization, connectivity, and signal complexity. Psychiatric conditions showed small deviations, revealing a shared transdiagnostic profile. In contrast, single rare variants showed substantially larger, distinct and sometimes mirror-opposite signatures that collapsed toward the psychiatric profile when pooled. Autism Spectrum Disorder showed some of the smallest group-level effects yet the largest individual deviations, indicating substantial but directionally inconsistent alterations. EEG deviations followed a cortical gradient, with larger effects in sensorimotor regions. We demonstrate that sample sizes in the hundreds are required for robust associations with psychiatric diagnoses. This interactive open resource provides normative scores to benchmark future results.

Motivation: Copy-number variants (CNVs) contribute to human disease and population trait variation. CNV detection from large whole-genome sequencing cohorts remains computationally demanding, as most methods require BAM or CRAM files. Genomic VCF (gVCF) files are smaller, routinely generated by standard variant-calling workflows, and contain the read depth and allelic information needed for CNV detection. However, gVCF files are not directly compatible with established CNV callers that rely on Log R Ratio (LRR) and B Allele Frequency (BAF) signals. Results: We present gVCF2CNV, a Nextflow pipeline that converts gVCF files into Log R Ratio and B Allele Frequency signals compatible with established CNV callers. Applied to 12,509 individuals from the SPARK cohort, gVCF2CNV generated signals at an average of 2.7 million SNV positions per individual and completed signal extraction in 4 hours using 192 CPUs. CNV calling with PennCNV and QuantiSNP identified candidate CNVs across a broad size range, with trio-based Mendelian precision reaching approximately 80% or higher for deletions of at least 30 kb and duplications of at least 5 kb. Application to 414,824 individuals from the All of Us cohort was completed in 96 hours, demonstrating feasibility at biobank scale. These results show that gVCF files can serve as a scalable input for CNV detection in large WGS cohorts. Availability and Implementation: gVCF2CNV is available at https://github.com/JacquemontLab/gVCF2CNV , implemented as a Nextflow pipeline with Perl and Python components, supported on Linux. Contact: mame.seynabou.diop@umontreal.ca .

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are highly heterogeneous neurodevelopmental disorders characterized by substantial phenotypic overlap and frequent co-occurrence. Traditional diagnostic categories may therefore fail to capture meaningful variation in symptom presentation. We investigated whether data-driven behavioral profiles derived independently of diagnosis correspond to distinct resting-state electroencephalography (EEG) signatures. A total of 237 children and adolescents (4.13–18.91 years) with ASD, ADHD, co-occurring ASD+ADHD, other neurodevelopmental conditions, or no diagnosis completed behavioral assessments and resting-state EEG recordings. Five behavioral measures reflecting social cognition, social communication, restrictive and repetitive behaviors, ADHD traits, and nonverbal IQ were entered into an unsupervised machine learning pipeline. Resting-state EEG spectral and nonlinear features were subsequently compared across clusters using linear models controlling for age and sex. A four-cluster solution was selected based on cluster stability and interpretability. The identified clusters were transdiagnostic, containing heterogeneous mixtures of diagnostic categories rather than diagnosis-specific groupings. The clusters differed in both symptom severity and behavioral profile. After false discovery rate correction, the most severe behavioral cluster exhibited significantly elevated periodic delta power, low- and high-gamma power, Hurst exponent, and Higuchi fractal dimension relative to the least severe cluster. No additional between-cluster EEG comparisons remained significant after correction. Our study suggests that neurodevelopmental heterogeneity may be better characterized by dimensional behavioral profiles than by diagnostic labels alone. Moreover, resting-state EEG features appear to track behavioral severity across neurodevelopmental conditions, supporting their potential utility as neurophysiological markers of transdiagnostic symptom severity.

The Quebec 1000 Families (Q1K) platform has been designed to recruit, phenotype, and collect biospecimens from a large cohort of families with at least one member with autism spectrum disorder or a related neurodevelopmental condition. As part of the Q1K protocol, an experimental test battery was developed and validated using simultaneous high-density electroencephalography (EEG) and eye tracking (ET). We report on the general approach and design principles, multimodal EEG/ET integration, the tasks, and their validation, providing a blueprint for implement such a project. It also describes the cohort and its methods as a reference for future studies using this dataset. By releasing openly this experimental test battery, we aim to support task standardization and multi-project data pooling in autism and related neurodevelopmental disorders.