Unsupervised Network Discovery for Brain Imaging Data

A common problem with spatiotemporal data is how to simplify the data to discover an underlying network that consists of cohesive spatial regions (nodes) and relationships between those regions (edges). This network discovery problem naturally exists in a multitude of domains including climate data (dipoles), astronomical data (gravitational lensing) and the focus of this paper, fMRI scans of human subjects. Whereas previous work requires strong supervision, we propose an unsupervised matrix tri-factorization formulation with complex constraints and spatial regularization. We show that this formulation works well in controlled experiments with synthetic networks and is able to recover the underlying ground-truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals.