Imperial College London

Dr Peter Hellyer

Faculty of MedicineDepartment of Brain Sciences

Honorary Lecturer
 
 
 
//

Contact

 

+44 (0)20 7594 9568peter.hellyer

 
 
//

Location

 

4.35Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Fagerholm:2015:brain/awv075,
author = {Fagerholm, ED and Hellyer, PJ and Scott, G and Leech, R and Sharp, DJ},
doi = {brain/awv075},
journal = {Brain},
pages = {1696--1709},
title = {Disconnection of network hubs and cognitive impairment after traumatic brain injury.},
url = {http://dx.doi.org/10.1093/brain/awv075},
volume = {138},
year = {2015}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 0.25). These resul
AU - Fagerholm,ED
AU - Hellyer,PJ
AU - Scott,G
AU - Leech,R
AU - Sharp,DJ
DO - brain/awv075
EP - 1709
PY - 2015///
SN - 0006-8950
SP - 1696
TI - Disconnection of network hubs and cognitive impairment after traumatic brain injury.
T2 - Brain
UR - http://dx.doi.org/10.1093/brain/awv075
UR - http://hdl.handle.net/10044/1/23523
VL - 138
ER -