Jaewon Chung

Network Biomarkers of Alzheimer's Disease Risk Derived from Joint Volume and Texture Covariance Patterns in Mouse Models

This study identifies network biomarkers of Alzheimer's disease risk in mouse models by analyzing joint volume and texture covariance patterns influenced by genetic, dietary, and immune factors.

When no answer is better than a wrong answer: a causal perspective on batch effects

This paper introduces causal methods to detect batch effects in multi-site data, showing they provide more accurate or 'no answer' results compared to non-causal methods.

Are human connectomes heritable?

This study investigates the heritability of human brain connectomes using a novel method, finding that heritability is insignificant after accounting for network variability.

A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis

This paper introduces a low-resource pipeline for estimating connectomes from MRI data, demonstrating its reliability across diverse datasets without manual intervention.

Connectal Coding: Discovering the Structures Linking Cognitive Phenotypes to Individual Histories

The paper introduces a formal statistical framework for connectal coding to link cognitive phenotypes to individual histories across different experimental modalities and phylogeny.