Hidehiko Takahashi

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 compares CycleGAN and ComBat for harmonizing T1-weighted MRI images across sites, finding CycleGAN preserves image quality better with minimal preprocessing.

This study finds that higher superiority illusion scores in older adults correlate with greater gray matter volume and functional connectivity in the right precuneus compared to younger and middle-aged groups.

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.

This paper applies a 3D convolutional autoencoder to structural brain MRI data from schizophrenia and healthy subjects to extract disease-related features without diagnostic labels.

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

This study examines semantic representations in schizophrenia patients using fMRI and finds reduced intra- and inter-individual variability compared to healthy controls.

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

The paper investigates how spatial frequency affects metacognitive performance in schizophrenia patients and controls, finding atypical SF dependence in patients.

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.