Yoojean Kim

Explaining Deep Learning-Based Representations of Resting State Functional Connectivity Data: Focusing on Interpreting Nonlinear Patterns in Autism Spectrum Disorder

This paper introduces explainable variational autoencoders to interpret nonlinear patterns in resting state fMRI data from autism spectrum disorder and control groups.

Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

This paper classifies PTSD vs. controls using MRI data from multiple sites, finding better performance when distinguishing PTSD from trauma-unexposed controls than trauma-exposed controls, and showing DVAE improves generalizability.

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

This study finds smaller total and subregional cerebellar volumes in PTSD compared to controls using a large dataset and advanced parcellation methods.