Ganesan Venkatasubramanian

This study analyzes fMRI data from OCD patients and controls to identify age-specific differences in brain activation during inhibitory control and error processing.

The paper identifies functional connectivity differences in OCD patients who respond versus do not respond to SSRIs, linking altered temporal-posterior networks to treatment outcomes.

This study assesses how race, sex, and electrode montage affect brain-wide current flow during ECT using computational models.

This paper introduces a Hodge-Laplacian framework to analyze higher-order topology in OCD brain networks, identifying loop-level abnormalities not detectable by conventional functional connectivity analysis.

The paper presents a comprehensive genomic atlas of Indian populations, identifying novel variants and highlighting genetic diversity and pharmacogenomic implications.

This study compares deterministic and probabilistic tractography methods for the left arcuate fasciculus across four datasets, finding probabilistic methods yield higher inter-subject shape similarity but may obscure subtle pathological variations.

This paper generates synthetic diffusion MRI scalar maps from T1-weighted images using RevGAN, showing they retain microstructural features and perform well in downstream tasks.

This study analyzes fMRI data from OCD patients and controls to identify neural activation patterns during executive control tasks, finding reduced activation in frontoparietal regions and increased default mode network activity in OCD.

This study introduces MINT, a framework that jointly models white matter microstructure and macrostructure to better detect anomalies in MCI and dementia, showing improved sensitivity compared to traditional diffusion metrics.

This study compares explainable AI models for Alzheimer's disease classification using MRI scans, evaluating interpretability methods and generalization across different populations.

This paper introduces DNT, a variational autoencoder framework for detecting white matter abnormalities in Alzheimer's disease using macro- and microstructural data.

This paper evaluates CNN models for brain age prediction and dementia classification using diffusion MRI, showing improved performance after dataset harmonization across Indian and North American cohorts.

BundleCleaner denoises and subsamples diffusion MRI tractography data, improving alignment with atlas bundles and enabling efficient tractometry analysis in Alzheimer's disease studies.

This study uses whole exome sequencing in families with multiple affected individuals to identify rare variants in genes associated with both Mendelian and complex neuropsychiatric syndromes.

This study determines the minimum sample size needed for multisite harmonization using structural brain MRI features and shows how sample size requirements increase with site variability.

The paper analyzes whole exome sequencing data from individuals with severe mental illness to identify genes under positive selection and their biological functions.

The paper describes MRI acquisition, quality assurance, and quality check protocols for a longitudinal study on brain disorders using stem cells, showing consistent and reliable data acquisition.

This study identifies white matter microstructural alterations in adult OCD patients and links them to clinical features, while finding no such abnormalities in pediatric patients.

The paper develops and validates age-specific Indian brain templates and atlases for spatial normalization of MRI data across ages 6-60 years.

The paper examines the relationship between adverse childhood experiences (ACEs) and the age of onset of psychiatric diagnoses in individuals from families with multiple affected members.