Disconnection of network hubs and cognitive impairment after traumatic brain injury

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...

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Bibliographic Details
Published in:Brain (London, England : 1878) England : 1878), 2015-06, Vol.138 (Pt 6), p.1696-1709
Main Authors: Fagerholm, Erik D, Hellyer, Peter J, Scott, Gregory, Leech, Robert, Sharp, David J
Format: Article
Language:English
Subjects:
Adult
Axons - pathology
Brain Injuries - complications
Brain Injuries - pathology
Brain Injuries - physiopathology
Brain Mapping - methods
Case-Control Studies
Cognition Disorders - complications
Cognition Disorders - pathology
Cognition Disorders - physiopathology
Diffusion Tensor Imaging
Female
Humans
Male
Models, Statistical
Neural Pathways - pathology
Neural Pathways - physiopathology
Original
White Matter - physiopathology
Young Adult
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Summary: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 results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centr
ISSN:0006-8950
1460-2156
DOI:10.1093/brain/awv075