Yuki Sakai

This paper introduces a Hodge-Laplacian framework to analyze higher-order topology in OCD brain networks, identifying loop-level abnormalities not detectable by conventional functional connectivity analysis.

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.

The paper identifies a neural network with increased functional connectivity in OCD patients and a subgroup of healthy individuals with imbalanced reinforcement learning.

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 models OCD as implicitly learned maladaptive behaviors in reinforcement learning, showing imbalanced memory traces for positive and negative prediction errors, and links this imbalance to OCD symptoms and therapeutic responses.

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

This study identifies white matter microstructural alterations in adult OCD patients and links them to clinical features, while finding no such abnormalities in pediatric 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.

The paper identifies a common neural network underlying state, trait, and pathological anxiety using fMRI data across multiple datasets.