Koji Matsuo

A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

Unsupervised feature computation-based feature selection robustly extracted resting-state functional connectivity patterns related to mental disorders

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

Comprehensive evaluation of pipelines for diagnostic biomarkers of major depressive disorder using multi-site resting-state fMRI datasets

The paper evaluates rs-fMRI analysis pipelines for MDD biomarkers using multi-site datasets, finding optimal combinations of parcellation, connectivity estimation, harmonization, and machine learning methods.

Common brain networks between major depressive disorder and symptoms of depression that are validated for independent cohorts

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

Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias

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%.