Predictive connectome subnetwork extraction with anatomical and connectivity priors. (January 2019)

• Record Type: Journal Article
• Authors: Brown, Colin J.; Miller, Steven P.; Booth, Brian G.; Zwicker, Jill G.; Grunau, Ruth E.; Synnes, Anne R.; Chau, Vann; Hamarneh, Ghassan
• Is Part Of: Computerized medical imaging and graphics. Volume 71(2019)
• Page Start: 67
• Page End: 78
• Publication Date: 2019-01
• Subjects: Connectome -- Machine learning -- Subnetwork -- Prediction -- Brain; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.08.009
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Predictive, subnetwork-based machine learning model for connectome data. Novel connectivity and backbone network priors regularize model. Approach validated on 168 structural connectomes and 1013 functional connectomes. Proposed priors outperform all other competing methods on prediction tasks. Resulting subnetworks are well connected and anatomically plausible. Abstract: We present a new method to identify anatomical subnetworks of the human connectome that are optimally predictive of targeted clinical variables, developmental outcomes or disease states. Given a training set of structural or functional brain networks, derived from diffusion MRI (dMRI) or functional MRI (fMRI) scans respectively, our sparse linear regression model extracts a weighted subnetwork. By enforcing novel backbone network and connectivity based priors along with a non-negativity constraint, the discovered subnetworks are simultaneously anatomically plausible, well connected, positively weighted and reasonably sparse. We apply our method to (1) predicting the cognitive and neuromotor developmental outcomes of a dataset of 168 structural connectomes of preterm neonates, and (2) predicting the autism spectrum category of a dataset of 1013 resting-state functional connectomes from the Autism Brain Imaging Data Exchange (ABIDE) database. We find that the addition of each of our novel priors improves prediction accuracy and together outperform other state-of-the-art prediction techniques. We then examine the structure of the learned subnetworks in terms of topological features and with respect to established function and physiology of different regions of the brain.