Masahiro Takamura

The paper develops a method to profile resting-state fMRI connectivity variations to improve the reliability and generalizability of psychiatric disorder biomarkers across multiple centers.

This study compares unsupervised feature selection with supervised methods to identify robust resting-state functional connectivity patterns associated with psychiatric disorders.

This study develops a generalizable neuromarker for ASD across multiple sites and developmental stages, showing significant performance in adults, children, and adolescents.

This study compares deep learning models for age prediction using resting-state fMRI data from the Japanese Brain Dock service, finding robust correlations across methods and highlighting the impact of atlas selection.

This study identifies resting-state brain connectivity patterns that predict individual aptitude in fMRI-neurofeedback training, independent of targeted brain regions.

The paper identifies common brain networks between major depressive disorder and depression symptoms that generalize across independent imaging sites.

The paper develops a harmonization method for resting-state fMRI data across sites by separating site differences into sampling and measurement biases, reducing measurement bias by 29% and improving signal-to-noise ratios by 40%.