2sDM

This repository contains code for the paper

Non-linear manifold learning in fMRI uncovers a low-dimensional space of brain dynamics

A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data

Hierarchical nonlinear embedding reveals brain states and performance differences during working memory tasks

Dataset we used

• Human connectome project (HCP) dataset
• Philadelphia Neurodevelopmental Cohort (PNC) dataset

Citation

Gao, Siyuan, Gal Mishne, and Dustin Scheinost. "A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data." In International Conference on Information Processing in Medical Imaging, pp. 631-643. Springer, Cham, 2019.

For any questions, please contact Siyuan Gao (siyuan.gao@yale.edu)