Yu‐Feng Zang

This paper develops a real-time TMS motor mapping method using rank correlation and posterior probability maps, improving efficiency and individualization over traditional sigmoid fitting.

The paper develops an individualized TMS targeting approach for ASD based on functional connectivity to the posterior cingulate cortex, showing symptom reduction in a case series.

This paper introduces a watershed algorithm GUI for fMRI-guided rTMS target localization, showing reduced spatial displacement and bias compared to conventional methods.

This study evaluates the reliability and validity of multi-band multi-echo fMRI for mapping individual differences in spontaneous brain activity, showing improved reliability and detectable differences in resting states.

The paper identifies sgACC functional connectivity abnormalities in depression using large-scale brain imaging data and develops an individualized TMS targeting algorithm to improve treatment outcomes.

The paper introduces a longitudinal dataset of school-age children and adolescents in China, detailing its design, sampling, and neuroimaging measurements.

The paper introduces PerAF, a new standardized metric for RS-fMRI that improves reliability and provides clearer insights into resting-state brain activity compared to existing methods like ALFF and fALFF.

The paper analyzes resting-state fMRI data from 1,300 MDD patients and 1,128 controls to find reduced default mode network functional connectivity in recurrent MDD, not first-episode MDD.

This paper shows that small effect sizes in resting-state fMRI studies lead to reproducibility issues, even after multiple comparison corrections, and recommends meta-analysis of un-thresholded data for better results.