Chuanjun Zhuo

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.

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.

The paper develops a reliable network template and methods to estimate individualized functional networks from resting-state fMRI data across 100k+ individuals, showing how data length and spatial resolution affect network estimation.

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

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.

This study identifies 45 structural-functional component maps across 9 functional domains using independent component analysis in two cohorts, showing varying S-F coherence in different brain regions.

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.