Biology subjects

Elsabbagh, M.

Publications and source records attributed to Elsabbagh, M..

8 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↗

bioRxiv2026

The Q1K Integrated EEG and Eye-Tracking Experimental Test Battery for Open Autism Science

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.

neuroscience↗

bioRxiv2024

Eye-movement artifact correction in infant EEG

BackgroundIndependent Component Analysis (ICA) is a well-established approach to clean EEG and remove the impact of signals of non-neural origin, such as those from muscular activity and eye movements. However, evidence suggests that ICA removes artifacts less effectively in infants than in adults. This study systematically compares ICA and Artifact Blocking (AB), an alternative approach proposed to improve eye-movement artifact correction in infant EEG. MethodsWe analyzed EEG collected from 50 infants between 6 and 18 months of age as part of the International Infant EEG Data Integration Platform (EEG-IP), a longitudinal multi-study dataset. EEG was recorded while infants sat on their caregivers laps and watched videos. We used ICA and AB to correct for eye-movement artifacts in the EEG and calculated the proportion of effectively corrected segments, signal-to-noise ratio (SNR), power-spectral density (PSD), and multiscale entropy (MSE) in manually selected EEG segments with and without eye-movement artifacts. ResultsOn the one hand, the proportion of effectively corrected segments indicated that ICA corrected eye-movement artifacts (sensitivity) better than AB. SNR and PSD indicated that both AB and ICA correct eye-movement artifacts with equal sensitivity. MSE gave mixed results. On the other hand, AB caused less distortion to the clean segments (specificity) for SNR, PSD, and MSE. ConclusionsOur results suggest that ICA is more sensitive (i.e., it better removes artifacts) but less specific (it distorts clean signals) than AB for correcting eye-movement artifacts in infant EEG.

neuroscience↗

bioRxiv2024

PyLossless: A non-destructive EEG processing pipeline

EEG recordings are typically long and contain large amounts of data, making manual cleaning a time-consuming and error-prone task. Automated pre-processing pipelines can facilitate the efficient and objective extraction of artifacts, enabling standardized and reproducible analyses. However, automated pre-processing pipelines typically remove data considered artifact, and return a subset of irreversibly transformed signals. This approach obfuscates pre-processing decisions, and often makes it impossible to recover the original data or modify the pre-processing steps. Further, it complicates collaboration between research teams working on a common dataset, because different analyses may require specific pre-processing routines. Given the large amount of resources that are devoted to collecting EEG, tools that can help efficiently and transparently pre-process data are greatly needed. PyLossless addresses this need by creating a non-destructive, automated pre-processing pipeline that maintains the continuous EEG structure. It offers a user-friendly API, it is well documented, tested through continuous integration, easily deployable, and integrates with the popular MNE-Python environment. The pipeline further provides a browser-based quality control review (QCR) dashboard that allows researchers to visualize and edit the automated artifact annotations on sensors, time-periods, and independent components. The end product of PyLossless is a lossless annotated data state that can be shared and used with analysis-specific artifact rejection policies, allowing for an optimal balance between flexibility and standardization.

neuroscience↗

bioRxiv2021

In-phase and in-antiphase connectivity in EEG

Zero-lag synchrony is generally discarded from functional connectivity studies to eliminate the confounding effect of volume conduction. Demonstrating genuine and significant unlagged synchronization between distant brain regions would indicate that most electroencephalography (EEG) connectivity studies neglect an important mechanism for neuronal communication. We previously demonstrated that local field potentials recorded intracranially tend to synchronize with no lag between homotopic brain regions. This synchrony occurs most frequently in antiphase, potentially supporting corpus callosal inhibition and interhemispheric rivalry. We are now extending our investigation to EEG. By comparing the coherency in a recorded and a surrogate dataset, we confirm the presence of a significant proportion of genuine zero-lag synchrony unlikely to be due to volume conduction or to recording reference artifacts. These results stress the necessity for integrating zero-lag synchrony in our understanding of neural communication and for disentangling volume conduction and zero-lag synchrony when estimating EEG sources and their functional connectivity.

neuroscience↗

bioRxiv2020

Structural templates for imaging EEG cortical sources in infants

Electroencephalographic (EEG) source reconstruction is a powerful approach that helps to unmix scalp signals, mitigates volume conduction issues, and allows anatomical localization of brain activity. Algorithms used to estimate cortical sources require an anatomical model of the head and the brain, generally reconstructed using magnetic resonance imaging (MRI). When such scans are unavailable, a population average can be used for adults, but no average surface template is available for cortical source imaging in infants. To address this issue, this paper introduces a new series of 12 anatomical models for subjects between zero and 24 months of age. These templates are built from MRI averages and volumetric boundary element method segmentation of head tissues available as part of the Neurodevelopmental MRI Database. Surfaces separating the pia mater, the gray matter, and the white matter were estimated using the Infant FreeSurfer pipeline. The surface of the skin as well as the outer and inner skull surfaces were extracted using a cube marching algorithm followed by Laplacian smoothing and mesh decimation. We post-processed these meshes to correct topological errors and ensure watertight meshes. The use of these templates for source reconstruction is demonstrated and validated using 100 high-density EEG recordings in 7-month-old infants. Hopefully, these templates will support future studies based on EEG source reconstruction and functional connectivity in healthy infants as well as in clinical pediatric populations. Particularly, they should make EEG-based neuroimaging more feasible in longitudinal neurodevelopmental studies where it may not be possible to scan infants at multiple time points. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/162131v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8d705dorg.highwire.dtl.DTLVardef@1cc0ac7org.highwire.dtl.DTLVardef@d791caorg.highwire.dtl.DTLVardef@a4707d_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITwelve surface templates for infants in the 0-2 years old range are proposed C_LIO_LIThese templates can be used for EEG source reconstruction using existing toolboxes C_LIO_LIA relatively modest impact of age differences was found in this age range C_LIO_LICorrelation analysis confirms increasing source differences with age differences C_LIO_LISources reconstructed with infants versus adult templates significantly differ C_LI

neuroscience↗

bioRxiv2020

Intracranial recordings reveal ubiquitous in-phase and in-antiphase functional connectivity between homologous brain regions in humans

Whether neuronal populations exhibit zero-lag (in-phase or in-antiphase) functional connectivity is a fundamental question when conceptualizing communication between cell assemblies. It also has profound implications on how we assess such interactions. Given that the brain is a delayed network due to the finite conduction velocity of the electrical impulses traveling across its fibers, the existence of long-distance zero-lag functional connectivity may be considered improbable. However, in this study, using human intracranial recordings we demonstrate that most interhemispheric connectivity between homotopic cerebral regions is zero-lagged and that this type of connectivity is ubiquitous. Volume conduction can be safely discarded as a confounding factor since it is known to drop almost completely within short inter-electrode distances (< 20 mm) in intracranial recordings. This finding should guide future electrophysiological connectivity studies and highlight the importance of considering the role of zero-lag connectivity in our understanding of communication between cell assemblies. Significance statementBy analysing the functional connectivity between pairs of intracranial electrodes, we demonstrate that corresponding regions from the two cerebral hemispheres show oscillatory activity synchronized without any delay. Most often, these synchronized oscillations have an inverted amplitude, suggesting that the hemispheres inhibit one another in alternance. The existence of such synchronous activity between homotopic brain regions has important implications on how we understand communication between brain regions and on how we assess neuronal functional connectivity. Our results show that controlling the confounding effect of volume conduction by discarding instantaneous synchrony may result in missing important brain dynamics. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY Graphical abstract textOffset PLI and standard PLI (sensitive and insensitive to zero-lag connectivity, respectively) for pairs of electrodes separated by more than 105 mm, plotted against their homotopic distance. Significant connectivity (dots above the red dashed line) is found only between homotopic regions and mostly for zero-lag connectivity (offset-PLI).

neuroscience↗

bioRxiv2020

Effects-sizes of deletions and duplications on autism risk across the genome

ObjectiveDeleterious copy number variants (CNVs) are identified in up to 20% of individuals with autism. However, only 13 genomic loci have been formally associated with autism because the majority of CNVs are too rare to perform individual association studies. To investigate the implication of undocumented CNVs in neurodevelopmental disorders, we recently developed a new framework to estimate their effect-size on intelligence quotient (IQ) and sought to extend this approach to autism susceptibility and multiple cognitive domains. MethodsWe identified CNVs in two autism samples (Simons Simplex Collection and MSSNG) and two unselected populations (IMAGEN and Saguenay Youth Study). Statistical models integrating scores of genes encompassed in CNVs were used to explain their effect on autism susceptibility and multiple cognitive domains. ResultsAmong 9 scores of genes, the "probability-of-being loss-of-function intolerant" (pLI) best explains the effect of CNVs on IQ and autism risk. Deletions decrease IQ by a mean of 2.6 points per point of pLI. The effect of duplications on IQ is three-fold smaller. The odd ratios for autism increases when deleting or duplicating any point of pLI. This increased autism risk is similar in subgroups of individuals below or above median IQ. Once CNV effects on IQ are accounted for, autism susceptibility remains mostly unchanged for duplications but decreases for deletions. Model estimates for autism risk overlap with previously published observations. Deletions and duplications differentially affect social communication, behaviour, and phonological memory, whereas both equally affect motor skills. ConclusionsAutism risk conferred by duplications is less influenced by IQ compared to deletions. CNVs increase autism risk similarly in individuals with high and low IQ. Our model, trained on CNVs encompassing >4,500 genes, suggests highly polygenic properties of gene dosage with respect to autism risk. These models will help interpreting CNVs identified in the clinic.

genetics↗