Biology subjects

Chabane, N.

Publications and source records attributed to Chabane, N..

6 recordsLinked to original sources

bioRxiv2026

Heterogeneity of Brain Dynamics in Genetic and Psychiatric Conditions

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.

neuroscience↗

bioRxiv2025

Sample size critically shapes the reliability of EEG case-control findings in psychiatry

Electroencephalography (EEG) studies in psychiatry have produced highly inconsistent findings, particularly in case-control designs. Small sample sizes are widely assumed to contribute to this variability, yet their impact on the reliability of EEG group comparisons has not been systematically quantified at scale. Here, we address this key gap using a large multisite resting-state EEG dataset comprising 2,874 participants aged 5-18 years, including individuals with attention-deficit/hyperactivity disorder, autism spectrum disorder, anxiety, learning disorders, and healthy controls. We extracted a comprehensive set of spectral, temporal, and complexity EEG features and performed repeated case-control comparisons across a wide range of sample sizes using extensive random subsampling. Results from small samples were unstable, with inflated and highly variable effect sizes across iterations. By contrast, larger samples produced consistent and reproducible findings, converging on uniformly small but robust effects. Statistical power rose steeply with sample size, whereas false positive rates remained relatively stable. These results demonstrate the central role of sample size in shaping EEG study outcomes and challenge the utility of conventional case-control approaches for biomarker discovery in psychiatry.

neuroscience↗

bioRxiv2025

Transdiagnostic electrophysiological subtypes reveal brain-behavior dimensions in youth psychiatry

Conventional psychiatric diagnoses often fail to reflect the underlying neurobiological and behavioral complexity of mental health conditions. Here, we propose a transdiagnostic, data-driven framework for stratifying youth based on large-scale multisite electroencephalography (EEG) data from 1,707 individuals aged 5-18 years, including healthy controls and individuals diagnosed with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), anxiety disorders (ANX), and learning disabilities (LD), along with their common comorbidities. By applying normative modeling to quantify individual deviations from typical brain functional maturation, and integrating multidimensional EEG features across spectral, temporal, complexity, and dynamical domains via similarity network fusion clustering, we identified three robust neurophysiological biotypes. These biotypes showed distinct electrophysiological and behavioral profiles, and captured meaningful brain-behavior relationships. Our findings suggest that biologically informed subtypes capture meaningful neuropsychiatric heterogeneity in youth, challenging conventional diagnostic boundaries in psychiatric nosology.

neuroscience↗

bioRxiv2025

Social, cognitive and sensory dimensions of cortical network overconnectivity in young children with autism spectrum disorder

The heterogeneity in both the neurobiological mechanisms and the phenotypic presentation of autism spectrum disorder (ASD) poses a major challenge to clinical and translational research. Early inefficiencies in functional connectivity (FC) have been associated with ASD, yet it remains unclear whether and how abnormal brain network properties may account for individual differences across ASD-related symptomatology and behaviors. We applied source-level reconstruction to resting-state high-definition EEG data in a cohort of 113 young children (40 with ASD) to identify early global and local alterations of cortical network connectivity. We subsequently used regularized canonical correlation analysis (rCCA) to characterize specific FC patterns linked to variation in cognitive, social and sensory dimensions within the autism spectrum. We found increased low-frequency FC in frontotemporal cross-hemispheric networks and lateral-occipital regions of young ASD children. RCCA revealed three distinct FC patterns in recurrent ASD-related networks, each contributing to predict individual differences in cognitive, social and sensory features. These linked FC-behavior dimensions may shed light on atypical brain network topology conferring risk for specific phenotypic manifestations of ASD, which may implicate unique underlying neurobiological mechanisms.

neuroscience↗

bioRxiv2024

Beyond homogeneity: Charting the landscape of heterogeneity in psychiatric electroencephalography

Electroencephalography (EEG) has been thoroughly studied for decades in psychiatry research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patients heterogeneity, overlooked in psychiatric EEG research relying on a case-control approach. We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics -spectral power and functional connectivity-across a cohort of 1674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls. Normative models showed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. Deviation spatial overlap across patients did not exceed 40% and 24% for spectral and connectivity, respectively. Considering individual deviations in patients has significantly enhanced comparative analysis, and the identification of patient-specific markers has demonstrated a correlation with clinical assessments, representing a crucial step towards attaining precision psychiatry through EEG.

neuroscience↗

bioRxiv2019

Superior temporal sulcus hypoperfusion in children with autism spectrum disorder: an arterial spin-labeling magnetic resonance study

Advances in neuroimaging techniques have significantly improved our understanding of the neural basis of autism spectrum disorder (ASD). Several attempts have been made to label the main neuroimaging phenotype of ASD, mostly by anatomical and functional activation studies, but none of the frameworks have been without controversy. Over the past decade, a renewed interest for rest brain functioning has emerged in the scientific community, reflected on a large number of resting state fMRI (rs-fMRI) studies, but results remain heterogeneous. It is possible today to investigate rest brain functioning by measuring rest cerebral blood flow (CBF) with MRI using arterial spin labeling (ASL). Here, we investigated rest CBF abnormalities using non-invasive ASL-MRI in 18 children with ASD without cognitive delay (10.4 {+/-} 2.8 y) and 30 typically developing children (10.6 {+/-} 3.0 y). Following quality control, images from a final sample of 12 children with ASD (11.2 {+/-} 2.9 y) and 28 typically developing children (10.1 {+/-} 2.5 y) were analyzed. Whole brain voxel-by-voxel analysis showed significant rest CBF decrease in temporal regions, mainly in the superior temporal sulcus (STS), in children with ASD. This hypoperfusion was individually detected in 83% of children with ASD. Finally, negative correlation was observed between ASD severity scores and rest CBF in the right posterior STS. Strikingly, despite the small sample studied here, our results are extremely similar to previous PET and SPECT findings describing decreased rest CBF in the same superior temporal regions at group and individual levels, as well as correlation with symptoms severity. The congruence between these results, with different methods and in different ASD profiles, reinforce the strength of rest functional abnormalities within these superior temporal regions in ASD and strongly indicates it might be a core characteristic of the disorder. Identifying a core dysfunctional region in ASD bears direct implications to the development of novel therapeutic interventions, such as transcranial magnetic stimulation. In addition, if confirmed in a larger sample, rest temporal hypoperfusion could become a reliable brain imaging biomarker in ASD.

neuroscience↗