TY - GEN

T1 - Supervised hub-detection for brain connectivity

AU - Kasenburg, Niklas

AU - Liptrot, Matthew George

AU - Reislev, Nina Linde

AU - Garde, Ellen

AU - Nielsen, Mads

AU - Feragen, Aasa

PY - 2016

Y1 - 2016

N2 - 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. textcopyright (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

AB - 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. textcopyright (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

U2 - 10.1117/12.2216186

DO - 10.1117/12.2216186

M3 - Article in proceedings

SN - 978-1-51060-019-5

T3 - Progress in Biomedical Optics and Imaging

BT - Medical Imaging 2016

A2 - Styner, Martin A.

A2 - Angelini, Elsa D.

PB - SPIE - International Society for Optical Engineering

Y2 - 27 February 2016 through 3 March 2016

ER -