Mayada Elsabbagh

The paper analyzes EEG data from 4,812 individuals to identify shared and distinct neurophysiological signatures across psychiatric conditions and rare genetic variants.

An open, instruction-free EEG plus eye-tracking task battery for autism families (344 to 587 participants per task so far), with timing-validated integrated recordings that enable gaze-locked ERP analysis.

PyLossless provides a non-destructive EEG pre-processing pipeline that maintains data integrity and allows flexible artifact handling.

The paper identifies DDX53 genetic variants in individuals with ASD, showing a link between DDX53 and ASD, and highlights implications for genetic testing and mouse modeling.

This study compares ICA and Artifact Blocking for correcting eye-movement artifacts in infant EEG, finding ICA more sensitive but less specific than AB.

The paper investigates how copy number variants in circadian pathway and insomnia risk genes contribute to ASD risk and sleep disturbances in children with ASD.

This study identifies 135 ASD-associated protein-coding genes using whole-genome sequencing data from 11,312 individuals and exome sequencing from over 18,000 trios, with a false discovery rate of 80%.

The paper confirms the presence of genuine zero-lag synchrony in EEG data, suggesting it is not solely due to volume conduction or artifacts.

This paper introduces 12 anatomical templates for EEG source reconstruction in infants aged 0-24 months, validated using EEG recordings and MRI data.

The paper shows that homotopic brain regions in humans exhibit zero-lag in-phase and in-antiphase functional connectivity, challenging the assumption that such connectivity is improbable due to neural delays.

The paper estimates the effect sizes of deletions and duplications on autism risk and IQ using statistical models applied to CNV data from multiple cohorts.