Publications

The electroencephalogram (EEG) provides a direct measure of brain electrical activity but is typically contaminated by artifacts, most notably those arising from eye movements. Such artifacts are often removed using blind source separation techniques such as independent component analysis (ICA). However, it remains challenging to determine whether subtracting eye-movement-related components identified by techniques like ICA inadvertently removes neurogenic activity. Addressing this issue is critical to prevent bias in EEG analyses. To this end, we developed complementary deep learning and biophysical modeling approaches for the deconfounding of eye-movement artifacts, leveraging eye-tracking (ET) information. Using a multimodal, open-access dataset, we trained a deep learning model within-recording to predict the component of the EEG explainable from ET. In parallel, we employed a realistic head model to simulate artifacts generated by eye movements. Using this framework, we can distinguish neural and nonneural correlates of eye movements from neural activity not associated with eye movements. Further, this ET-informed framework enabled us to evaluate the sensitivity and specificity of techniques used to isolate and remove eye-movement artifacts. These advances provide a solid framework for future work aiming to further disentangle neural components using tailored experimental paradigms, for example, by dissociating eye movements and visual feedback through virtual reality.

Autism spectrum disorder (ASD), like many other neurodevelopmental conditions, arises from complex interactions between genetics and environmental factors. In the last 50 years, significant efforts have been made to identify biomarkers and risk factors that can improve our understanding of autism etiology. However, high heterogeneity in causes, symptomology, and developmental trajectories has made this task challenging. One strategy to combat heterogeneity is to characterize individuals based on their brain phenotypes, commonly referred to as "neuro-subtyping". In this study, we analyzed electroencephalography (EEG) recordings from 144 infants aged 6-7 months and employed subtyping methods (latent profile analysis and hierarchical clustering) to identify subgroups. Our analyses revealed three distinct subgroups based on various language-associated EEG measures. We found that group membership was predictive of expressive and receptive language trajectories based on the Mullen Scales of Early Learning, with infants displaying high connectivity in language regions and lefthemisphere lateralization achieving the highest scores, while infants with overactivation of connectivity in the auditory network achieved lower scores. Notably, EEGderived subgroups did not predict a later ASD diagnosis, suggesting a lack of evidence for an ASD-specific phenotype during the first year of life. Results from this project contribute to a large body of research that supports using stratification approaches to decode heterogeneity in autism and its role in predicting behavioural outcomes.

The lateralization of brain activity is important for language processing and attention, and atypical patterns of lateralization have been linked to many neurodevelopmental disorders, including autism spectrum disorder (ASD). However, the developmental timing of these patterns and their relationship to emerging ASD characteristics are unclear. In this study, we used data from EEG-IP (International Infant EEG Data Integration Platform), a longitudinal cohort bringing together infants at elevated likelihood for ASD and age-equivalent controls across two sites. We examined brain lateralization in electroencephalography (EEG) power during the first year of life. Overall, we identified differences in gamma band lateralization in infants later diagnosed with ASD at 12 months but not at 6 months. Additionally, we observed a shift from high left gamma band asymmetry at 6 months toward more symmetry by 12 months in our control group, highlighting between-group differences in developmental trajectories in brain oscillatory activity. We found key differences in the lateralization across groups in brain regions within the auditory network, which is thought to be important for language learning. Overall, examining the developmental trajectories of lateralization is a crucial step toward creating more accurate models of brain development and better understanding the underlying mechanisms of neurodevelopmental disorders.