Kaustubh R. Kulkarni

This paper shows that dopamine and serotonin changes during deep brain stimulation predict symptom relief in treatment-resistant depression.

This paper develops a multiclass classification framework to identify shared and substance-specific neural circuits in cannabis and alcohol use disorder using functional connectivity features from cue-induced craving tasks.

This study shows human substantia nigra neurons encode reward expectations through firing rates modulated by trial outcomes and linked to reaction times.

This study identifies distinct neural patterns in PTSD patients for traumatic versus sad memories, showing divergent hippocampal representations and symptom severity effects in the posterior cingulate cortex.

This study identifies a computational mechanism linking momentary craving and decision-making in substance use disorders, showing differential effects between alcohol and cannabis users.

This paper develops an interpretable decoding model using functional connectivity to classify chronic marijuana users and non-users with high accuracy.

The paper finds that whole-brain functional network organization is similar across mental disorders and healthy controls, but subtle differences predict diagnosis.

The paper creates a large-scale functional network map of the entire human brain, including cortical and subcortical regions, and releases it as an open resource for future research.

The paper introduces information transfer mapping to show resting-state network topology predicts cognitive task information transfer between brain regions.

The paper identifies hierarchical activation states during rest and task conditions in fMRI data, revealing task-positive states occur frequently during rest.