Yong Fan A

AREION: A Foundation Model for Atlas-Agnostic Representation Learning of Brain Connectome

AREION learns atlas-agnostic representations of brain connectomes, outperforming baselines with fewer parameters and strong transferability across atlases.

Intrinsic timescale develops hierarchically from sensorimotor to association cortex in youth and stabilizes in adulthood

Resting-state fMRI intrinsic timescale lengthens with age in association, not sensorimotor, cortex during ages 8-22, following the sensorimotor-association axis in two cohorts; it is largely age-stable in young adults.

Discovering Heterogeneous Neurodegenerative Disease Patterns From MRI Data for Improved Prediction

This paper introduces a mixture-of-experts framework for identifying clinically meaningful subtypes in neurodegenerative diseases, improving prediction of progression to Alzheimer's disease.

Interpretable Local-to-Global Estimation of Brain Aging Speed From Morphological Changes Using Longitudinal Structural MRI Data

This paper estimates brain aging speed from longitudinal MRI data using deformation fields, providing local and global predictions with improved accuracy and interpretability.

Spatial Heterogeneity and Subtypes of Functional Connectivity Development in Youth

This study identifies subtypes of functional connectivity development in youth using fMRI data, revealing spatial patterns linked to cognitive performance and gene expression.

Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling

This study introduces an interpretable predictive model to identify functional connectivity patterns predictive of cognitive traits in youth, showing improved accuracy and fine-grained characterization.

dFCExpert: Learning Dynamic Functional Connectivity Patterns with Modularity and State Experts

dFCExpert learns dynamic functional connectivity patterns from fMRI data using modularity and state experts, showing superior performance on large-scale datasets.

SurfNet: Reconstruction of Cortical Surfaces via Coupled Diffeomorphic Deformations

SurfNet jointly reconstructs cortical surfaces from MRIs using diffeomorphic deformations, achieving state-of-the-art accuracy and topological correctness.

Precision Neuromodulation with Real-Time Brain Decoding for Working Memory Enhancement

This paper demonstrates that real-time brain decoding using personalized functional networks enhances the effectiveness of transcranial magnetic stimulation for working memory.

SegCSR: Weakly-Supervised Cortical Surfaces Reconstruction from Brain Ribbon Segmentations

This paper introduces a weakly-supervised method for cortical surface reconstruction using brain MRI ribbon segmentations, achieving comparable or better accuracy than supervised methods.

Reproducible Sex Differences in Personalized Functional Network Topography in Youth

The paper identifies robust sex differences in personalized functional brain network topography in youth, linked to X-linked genes and brain regions involved in psychiatric disorders.

Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning

This paper introduces a self-supervised deep learning framework to compute individualized multi-scale hierarchical functional networks from fMRI data and shows their association with biological phenotypes.

Effect of Stroma-directed Drugs in Combination with Chemotherapy Against Pancreatic Cancer- a Preclinical Study

This study compares chemotherapy with and without stroma-directed drugs in a mouse model of pancreatic cancer, finding enhanced tumor growth inhibition and changes in tumor microenvironment.

pNet: A toolbox for personalized functional networks modeling

pNet is an open-source toolbox for creating personalized functional networks from fMRI data, offering multiple interfaces and methods to enhance reliability and reproducibility.

A general exposome factor explains individual differences in functional brain network topography and cognition in youth

The paper investigates how an individual's exposome influences functional brain network organization and cognitive functioning in youth, finding that exposome factors predict cognitive performance better than neuroimaging data.

Enhancing Whole Slide Image Classification with Discriminative and Contrastive Learning

This study improves whole slide image classification by using discriminative and contrastive learning to create WSI-level positive and negative samples, enhancing classification performance and feature robustness.

Computing personalized brain functional networks from fMRI using self-supervised deep learning

This paper develops a self-supervised deep learning method to compute personalized brain functional networks from fMRI data, showing it can identify canonical networks and predict individual differences in behavior and schizophrenia status.

Development of Top-Down Cortical Propagations in Youth

This study tracks cortical activity propagations in youth, finding increased top-down propagations with cognitive control demands and development.

Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning

This paper develops a self-supervised deep learning method to compute transcranial magnetic stimulation electric fields more efficiently than traditional finite-element methods.

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

The paper defines personalized functional networks in children to predict cognitive differences, finding that fronto-parietal network representation correlates with general cognition.

Intrinsic Activity Develops Along a Sensorimotor-Association Cortical Axis in Youth

The paper shows that intrinsic brain activity decreases along a sensorimotor-association cortical axis during human development, with reduced activity in disadvantaged youth.

Novel genomic loci and pathways influence patterns of structural covariance in the human brain

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

The paper examines how individual differences in functional brain network topography relate to psychopathology dimensions in youth using machine learning and fMRI data.

Dissociable Multi-scale Patterns of Development in Personalized Brain Networks

The paper analyzes multi-scale functional brain networks in youths to identify developmental patterns of network re-organization and their cognitive implications.

Harmonizing Functional Connectivity Reduces Scanner Effects in Community Detection

This study identifies scanner effects in brain community detection and proposes new harmonization methods to reduce these effects in functional connectivity analysis.

Multi-scale semi-supervised clustering of brain images: deriving disease subtypes

This paper introduces MAGIC, a multi-scale semi-supervised clustering method for brain imaging data to identify disease subtypes, and evaluates its performance on simulated and clinical datasets.

Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks

This paper introduces RNcut, a method for robust and homogeneous brain network parcellation in neonates and adults, showing improved performance over existing methods in simulated and real rs-fMRI data.

Individual Variation in Control Network Topography Supports Executive Function in Youth

This study shows how functional topography of brain networks changes with age and relates to executive function and brain organization in youth.

Harmonization of large multi-site imaging datasets: Application to 10,232 MRIs for the analysis of imaging patterns of structural brain change throughout the lifespan

The paper harmonizes 10,232 structural brain MRIs from 18 studies to create a normative reference of brain development and aging across 3 to 96 years.

Neurostructural Heterogeneity in Youth with Internalizing Symptoms

This study identifies two neurobiologically distinct subtypes of youth with internalizing symptoms using structural brain imaging and machine learning.