Matcheri S. Keshavan

The paper introduces DataPlex, a large labeled MRI dataset of the human lateral ventricle choroid plexus to improve segmentation accuracy for clinical and research applications.

This study identifies three psychosis neurosubtypes using imaging data, showing distinct connectivity patterns and supporting their biological validity through relative alignment.

The paper uses causal discovery analysis to show that statistically non-significant fMRI regions causally influence psychopathology severity and working memory performance.

The paper analyzes psychosis biotypes using ancestry-adjusted polygenic risk scores and identifies genomic associations with specific biotypes in brain transcriptomes.

The paper examines sleep spindle activity and memory consolidation in early psychosis patients and their relatives, finding spindle deficits and memory issues in patients but not relatives.

The paper examines DMN hyperconnectivity in psychosis risk individuals and links two distinct connectivity patterns to specific symptom clusters.

The paper compares functional brain measures between 22qDel and CHR cohorts, finding distinct patterns of connectivity and variability disruptions.

This study finds that individuals at clinical high risk for psychosis have larger choroid plexus volumes, associated with neuroanatomical changes and plasma analyte markers.

The paper examines resting-state fMRI data from high-risk individuals to determine if abnormal functional connectivity in the dentate nuclei precedes psychosis onset.

This paper introduces regression dynamic causal modeling (rDCM) for resting-state fMRI, providing directional connectivity estimates and scalability to whole-brain networks.

The paper investigates genetic risk for psychosis spectrum symptoms in youth, finding a significant link with ADHD polygenic scores and age-specific effects.

The paper evaluates the ComBat method for multi-site diffusion MRI harmonization, finding that it preserves biological variability in some sites but not others and is sensitive to preprocessing differences.

The paper links individual variation in brain network topography to schizophrenia negative symptom severity using resting state fMRI.

The paper identifies distinct functional recovery trajectories in first episode psychosis patients using data-driven methods and explores associated risk factors.

The paper identifies brain-wide bi-directional connectivity alterations in cortico-striato-cerebellar circuits in schizophrenia using resting-state fMRI and validates findings with clustering and classification methods.

The paper identifies network disruptions in brain regions associated with mania in both lesion-induced mania and bipolar disorder.

The paper discusses challenges in genetic analysis of deep phenotyping projects in common disorders and highlights advances in imputation and GWAS methods to address these challenges.

The paper reports GWAS findings linking common variants in NRXN1, LRP1B, and RORA to increased ventricular volumes in psychosis, using deep phenotyping data from 1,115 cases and controls.

The paper investigates the association between schizophrenia genetic risk and cognitive performance in healthy individuals, finding a link with current cognitive abilities but not with premorbid intelligence or education.