Bo‐yong Park

This paper introduces the R-ratio to quantify the balance between local and distributed excitation in the brain, showing it increases with aging and Alzheimer's disease.

This study finds reduced cortex-wide connectivity distance in ASD, indicating shorter average connection length and increased short-range connectivity, particularly in transmodal brain systems.

GAN-MAT synthesizes T2-weighted MRI from T1-weighted data and estimates microstructural features, validated on healthy controls and patients with neurological conditions.

This study investigates how BMI-related variations in brain structure-function coupling change with diffusion time, revealing associations with hierarchical brain organization and gene expression in specific brain regions.

This study examines how thalamocortical connectivity changes with age and contributes to the development of large-scale functional brain networks from infancy to young adulthood.

The paper investigates structural connectome asymmetry in autism, finding atypical asymmetry in specific brain networks and associations with communication symptoms and intelligence.

This study uses multimodal imaging and connectivity modeling to show that polysynaptic mechanisms are implicated in autism spectrum disorder, with reduced structure-function coupling in transmodal networks.

This study shows that social and interoceptive mental training leads to changes in brain function and microstructure, which predict behavioral improvements in attention and empathy.

This study identifies reduced microstructural differentiation in TLE patients, linking it to cognitive dysfunction and network reorganization.

This study identifies atypical intrinsic neural timescales in temporal lobe epilepsy, showing reduced timescales in multiple brain regions and high accuracy in classifying patients and lateralizing seizure foci.

The paper identifies a shared cortical morphology dimension across six psychiatric conditions using MRI data from 28,546 participants.

The paper shares a multimodal MRI dataset for studying multiscale brain organization, including connectomes and gradients from multiple imaging modalities.

Micapipe is an open pipeline for processing multimodal MRI data, generating structural and functional connectomes, geodesic distances, and microstructural profiles across multiple brain parcellations and surface spaces.

This paper predicts brain function from the structural connectome using a Riemannian approach based on diffusion maps and random walks, showing performance plateaus around the third walk.

The paper examines how genetic and phylogenetic factors influence structure-function coupling in the human transmodal cortex and finds reduced genetic control in regions linked to social cognition and memory.

This study identifies a structure-function substrate of cognitive imbalances in autism using multi-site neuroimaging data and dimensional analysis.

The paper identifies three subgroups of eating behavior traits independent of obesity using functional connectivity and autoencoder-based feature representation in healthy adults.

This study identifies shared and distinct structural brain differences in children with autism and anxiety, using neuroimaging data from 82 participants.

This study identifies divergent patterns of atypical cortical asymmetry and regional atrophy in temporal lobe epilepsy using MRI data from a large multi-site cohort.

The paper investigates how long-range brain connections relate to microarchitectural and cognitive hierarchies in the human brain, finding that transmodal association cortices have distributed long-range connections linked to similar microarchitecture.

This study examines cognitive state dynamics during story comprehension and finds that the brain enters functionally integrated states with increased DMN activation during moments of understanding.

This study examines structural and functional brain network development in adolescents, finding that structural wiring changes correlate with functional differentiation.

The paper introduces the ENIGMA Toolbox, a Python/Matlab ecosystem for integrating multisite neuroimaging datasets across disorders and contextualizing findings at multiple scales.

This study quantifies how population diversity affects brain-imaging prediction accuracy and pattern stability using two clinical cohorts.

The paper examines how BMI variations relate to functional brain organization by analyzing connectome structure in healthy young adults, finding increased segregation and disrupted hierarchy in higher BMI individuals.

The paper combines connectome analysis and computational models to show autism has altered subcortico-cortical interactions linked to microcircuit and macroscale network anomalies.

The paper uses manifold learning to analyze longitudinal structural connectome data, showing expansion in transmodal regions during adolescence and linking it to cognitive outcomes.

This study identifies higher functional network idiosyncrasy in ASD compared to TD, linked to ASD risk genes and symptom severity.

The paper identifies functional activation patterns during pattern separation in the hippocampus and neocortex, showing alignment with intrinsic connectivity gradients and performance differences in patients.