Dhivya Srinivasan

The paper develops and evaluates new batch effect correction methods for neuroimaging data that account for spatial correlations, showing improved performance over existing methods.

This paper develops a self-supervised deep learning method to compute personalized brain functional networks from fMRI data, showing it can identify canonical networks and predict individual differences in behavior and schizophrenia status.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

This study identifies scanner effects in brain community detection and proposes new harmonization methods to reduce these effects in functional connectivity analysis.

The paper harmonizes 10,232 structural brain MRIs from 18 studies to create a normative reference of brain development and aging across 3 to 96 years.