Supervised hub-detection for brain connectivity

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationSPIE Medical Imaging 2016: Image Processing
Number of pages9
PublisherSPIE - International Society for Optical Engineering
Publication date2016
Article number978409
ISBN (Print)9781510600195
DOIs
Publication statusPublished - 2016
EventSPIE Medical Imaging 2016: Image Processing - San Diego, United States
Duration: 27 Feb 20163 Mar 2016
Conference number: 9784

Conference

ConferenceSPIE Medical Imaging 2016
Number9784
CountryUnited States
CitySan Diego
Period27/02/201603/03/2016
SeriesProceedings of SPIE, the International Society for Optical Engineering
Volume9784
ISSN0277-786X

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