Imperial College London
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ProfessorDavidSharp

Faculty of Medicine, Department of Brain Sciences

Professor of Neurology
 
 
 
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Contact

 

+44 (0)20 7594 7991david.sharp Website

 
 
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Location

 

UREN.927Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Jolly:2020:brain/awaa067,
author = {Jolly, A and Scott, G and Sharp, D and Hampshire, A},
doi = {brain/awaa067},
journal = {Brain: a journal of neurology},
pages = {1158--1176},
title = {Distinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injury},
url = {http://dx.doi.org/10.1093/brain/awaa067},
volume = {143},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - It is well established that chronic cognitive problems after traumatic brain injury (TBI) relate to diffuse axonal injury (DAI) and the consequent widespread disruption of brain connectivity. However, the pattern of DAI varies between patients and they have a correspondingly heterogeneous profile of cognitive deficits. This heterogeneity is poorly understood, presenting a non-trivial challenge for prognostication and treatment. Prominent amongst cognitive problems are deficits in working memory and reasoning. Previous functional magnetic resonance imaging (fMRI) in controls has associated these aspects of cognition with distinct, but partially overlapping, networks of brain regions. Based on this, a logical prediction is that differences in the integrity of the white matter tracts that connect these networks should predict variability in the type and severity of cognitive deficits after TBI.We use diffusion-weighted imaging, cognitive testing and network analyses to test this prediction. We define functionally distinct sub-networks of the structural connectome by intersecting previously published fMRI maps of the brain regions that are activated during our working memory and reasoning tasks, with a library of the white-matter tracts that connect them. We examine how graph theoretic measures within these sub-networks relate to the performance of the same tasks in a cohort of 92 moderate-severe TBI patients. Finally, we use machine learning to determine whether cognitive performance in patients can be predicted using graph theoretic measures from each sub-network.Principal component analysis of behavioural scores confirm that reasoning and working memory form distinct components of cognitive ability, both of which are vulnerable to TBI. Critically, impairments in these abilities after TBI correlate in a dissociable manner with the information-processing architecture of the sub-networks that they are associated with. This dissociation is confirmed when examining degree
AU  - Jolly,A
AU  - Scott,G
AU  - Sharp,D
AU  - Hampshire,A
DO  - brain/awaa067
EP  - 1176
PY  - 2020///
SN  - 0006-8950
SP  - 1158
TI  - Distinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injury
T2  - Brain: a journal of neurology
UR  - http://dx.doi.org/10.1093/brain/awaa067
UR  - https://academic.oup.com/brain/article/143/4/1158/5815602
UR  - http://hdl.handle.net/10044/1/77340
VL  - 143
ER  -
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