Supervised hub-detection for brain connectivity

Abstract

A structural brain network consists of physical connections between brain regions. Brain network analysis aims to find features associated with a parameter of interest through supervised prediction models such as regression. Unsupervised preprocessing steps like clustering are often applied, but can smooth discriminative signals in the population, degrading predictive performance. We present a novel hub-detection optimized for supervised learning that both clusters network nodes based on population level variation in connectivity and also takes the learning problem into account. The found hubs are a low-dimensional representation of the network and are chosen based on predictive performance as features for a linear regression. We apply our method to the problem of finding age-related changes in structural connectivity. We compare our supervised hub-detection (SHD) to an unsupervised hub-detection and a linear regression using the original network connections as features. The results show that the SHD is able to retain regression performance, while still finding hubs that represent the underlying variation in the population. Although here we applied the SHD to brain networks, it can be applied to any network regression problem. Further development of the presented algorithm will be the extension to other predictive models such as classification or non-linear regression.

Label-Informed Non-negative Matrix Factorization with Manifold Regularization for Discriminative Subnetwork Detection

“…While this last issue was addressed in a recent work by Ghanbari et al [7] under a graph-embedding framework, their method is also unsupervised and thus ignores label information. On the other hand, although supervised subnetwork detection frameworks have been introduced in some recent works [2, 8], these methods do not account for the manifold structure underlying the data.…”