Hongming Li

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

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

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

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

The paper maps personalized functional topography in the neonatal human brain using neuroimaging data and links it to neurocognitive outcomes in early childhood.

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

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

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

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

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.

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.

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

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.

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.

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.

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

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

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

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

The paper uses machine learning to analyze functional topography in youth brains, finding sex differences linked to X-linked genes and specific 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.

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