Ayumu Yamashita

The paper prunes neural networks to isolate computational subspaces for style-invariant object recognition and tests their perceptual relevance through image synthesis.

The paper identifies recurring brain states across tasks and links their dynamics to trial-to-trial performance and individual response strategies.

The paper investigates semantic representations in body-selective brain regions by linking natural scene captions to neural responses, finding that implied body motion is the primary contributor.

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.

This study evaluates the impact of MRI acquisition upgrades and harmonization methods on brain parameters, showing that harmonization reduces protocol effects but requires sufficient participants.

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 identifies two brain states during a sustained attention task, showing one is behaviorally optimal and the other suboptimal, and examines their modulation by motivation, mind wandering, and ADHD.

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%.