Nilakshi Vaidya

The paper proposes the amygdala burnout hypothesis, showing early-life stress leads to initial amygdala volume increases in adolescence followed by reductions in older age across rodents and humans.

The paper identifies two reproducible reward imaging phenotypes linked to dopamine D2 receptor function and shows differential responses to antipsychotic drugs.

The paper examines how childhood adversity dimensions relate to frontostriatal network connectivity and anhedonia in young adults, finding sex-specific associations.

The paper uses longitudinal fMRI data to show that proactive and reactive control networks stabilize after mid-adolescence, predicting cognitive stability and psychopathology.

This study examines how different types of adversity affect grey and white matter development in adolescents across two cohorts, finding distinct associations between adversity types and brain structure metrics.

The paper uses longitudinal neuroimaging and behavioural data to show how brain-behaviour relationships shift from externalising to internalising symptoms across adolescence, identifying age 14 as a critical transition point.

This study links bullying victimization to structural brain changes in adolescents and young adults, showing sex-specific effects on brain regions involved in emotion and motor function.

The paper develops a digital twin brain model that simulates and manipulates functional brain networks to predict mental illness symptoms and responses to interventions.

MOCHA identifies latent population subgroups with distinct genetic effects from multi-omics data, revealing two neurodevelopmental clusters linked to adolescent inhibitory control.

The paper introduces DeepTaskGen, a deep learning method that generates synthetic task-based contrast maps from resting-state fMRI data, outperforming benchmarks and enabling biomarker development for cognitive tasks.

This study maps the coupling between cortical geometry and white matter tract connectivity, showing how these aspects jointly shape brain function.

The paper identifies a neural archetype associated with chronic pain susceptibility by analyzing resting-state networks in population and clinical samples.

The paper introduces Brain-DiT, a deep generative model for analyzing task-fMRI data, identifying cognitive regions and subgroups linked to psychiatric conditions like depression and alcohol abuse.

The paper identifies a genetic-dependent brain signature of resilience linked to childhood abuse and depression risk in girls, showing its predictive value for emotional disorders in adolescence and early adulthood.

The paper links regional human cortex development and aging to molecular and cellular brain organization patterns using neuroimaging and gene expression data.

This study examines how variability in brain state engagement changes with age and its relationship to executive function in youth.

This study develops an individualized in vivo marker of excitation-inhibition balance in adolescents using biophysical network models and finds increased inhibition in association cortices during development.

This study shows that a brain network for sustained attention at age 14 predicts increased cannabis and cigarette use by age 23 in a longitudinal cohort.

This study investigates environmental and genetic factors influencing anxiety and depression in children and adolescents across three cohorts, identifying significant environmental impacts and a novel SNP associated with the disorders.

This study uses federated analysis in COINSTAC to examine functional network connectivity in adolescent brains linked to smoking and alcohol consumption, finding hypoconnectivity and reduced synchronization.

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