Abhishek Datta

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

The paper evaluates the MNI152 brain template's fidelity to real human morphology and finds it systematically mismatches population anatomy, suggesting the need for more accurate templates.

This paper introduces an MRI-free and neuronavigation-free method for localizing deep brain targets using scalp measurements, achieving a mean localization error of 3.82 mm.

This study compares electric field models derived from ultra-low-field (ULF) and 3T MRI scans, finding significant differences in cortical regions but less so in deeper brain structures.

This paper shows that individually optimizing temporal interference stimulation improves focality in deep brain regions compared to common montages, with variability due to head anatomy and tissue conductivity.

The paper develops an MRI-free virtual neuronavigation method for TMS using scalp measurements and a standard head model, achieving 2.75 mm mean error in simulated tests.

This paper introduces a deep learning framework for automated closed-loop neuromodulation that uses diverse datasets and multi-modal stimulation techniques to improve intervention timing and performance.