Ryuichiro Hashimoto

This study examines reduced multiscale entropy (MSE) in adults with autism spectrum disorder (ASD) and its association with social communication, using EEG data during rest and movie viewing conditions.

The paper examines intrinsic neural timescales in autism spectrum disorder and typically developed controls, finding preserved hierarchical organization but individual variability linked to sensory traits.

This study examines iron deposition and functional connectivity in the substantia nigra of adults with autism, finding increased iron and altered connectivity patterns associated with symptom severity.

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

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 study examines white matter abnormalities in ASD and ADHD using DTI and finds shared and distinct relationships between white matter and sensory profiles.

The paper shows that spontaneous motor variability in vocal pitch predicts adaptive compensation for auditory feedback shifts in humans.

The paper examines how paternal and maternal-child relationships affect brain connectivity and psychobehavioral problems in adolescents, finding sex-specific effects on aggression and internalizing issues.

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

This study uses functional magnetic resonance imaging to predict aspects of subjective well-being from social relationships, finding distinct functional connectomes for different well-being dimensions.

The paper identifies cortical sulcal depth as an ASD endophenotype and shows neural correlates for clinical diagnosis differ from this endophenotype when accounting for TD sibling differences.

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 uses multi-voxel pattern analysis to identify altered functional connectivity in schizophrenia patients, finding a link between cerebellar and default-mode network connectivity and auditory verbal hallucination severity.