Yu Huang

This paper evaluates two-array temporal interference stimulation for deep brain modulation, showing it achieves similar focality to multi-pair TI with faster optimization and fewer electrodes.

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 a deep learning model to detect hydrocephalus requiring treatment from MRI scans, achieving performance comparable to neuroradiologists.

This study shows that tDCS applied to the prefrontal cortex improves behavior and EEG markers of consciousness in some DoC patients.

The paper provides a tutorial on using Simpleware and COMSOL Multiphysics to create and solve FEM models for simulating tDCS current flow based on MRI-derived head anatomy.