Yasumasa Okamoto

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 paper identifies a thalamo-somatomotor functional connectivity biomarker that stratifies MDD patients into subtypes with distinct antidepressant treatment responses, demonstrating generalizability across independent 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.

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

The paper replicates a brain age gap in depression using a large-scale multisite study, finding a similar effect size to a prior study.

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.

This paper outlines a multi-site MRI study aiming to establish harmonized imaging biomarkers for brain disorders across the lifespan using standardized protocols and data from healthy and patient subjects.

The paper uses brain connectivity data to show overlapping but asymmetrical relationships between schizophrenia and autism.

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%.

The paper builds a working memory prediction model from whole-brain functional connectivity in healthy individuals and applies it to psychiatric disorders, finding correlations between connectivity patterns and working memory performance.