Supervised hub-detection for brain connectivity

Niklas Kasenburg; Matthew Liptrot; Nina Linde Reislev; Ellen Garde; Mads Nielsen; Aasa Feragen

doi:10.1117/12.2216186

21 March 2016 Supervised hub-detection for brain connectivity

Niklas Kasenburg, Matthew Liptrot, Nina Linde Reislev, Ellen Garde, Mads Nielsen, Aasa Feragen

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978409 (2016) https://doi.org/10.1117/12.2216186
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Niklas Kasenburg, Matthew Liptrot, Nina Linde Reislev, Ellen Garde, Mads Nielsen, and Aasa Feragen "Supervised hub-detection for brain connectivity", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978409 (21 March 2016); https://doi.org/10.1117/12.2216186

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