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Lookup NU author(s): Professor Andrew Blamire,
Dr Yujiang Wang
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Objective: Studies of outcome after Traumatic Brain Injury (TBI) patients are hampered by the lack of robust injury severity measures that can accommodate spatial-anatomical and mechanistic heterogeneity. In this study we introduce a Mahalanobis distance measure (M) as an intrinsic injury severity measure that combines in a single score the many ways a given injured brain’s connectivity can vary from that of healthy controls. Our objective is to test the hypotheses that M is superior to univariate measures in (i) discriminating patients and controls, and (ii) correlating with cognitive assessment.Methods: Sixty-five participants (34 mild TBI; 31 controls) underwent diffusion tensor MRI, and extensive neuropsychological testing. Structural connectivity was inferred for all subjects for 22 major white matter connections. Twenty-two univariate measures (one per connection) and one multivariate measure (M), capturing and summarizing all connectivity change in a single score, were computed.Results: Our multivariate measure (M) was able to better discriminate between patients and controls (AUC=0.81) than any individual univariate measure. M significantly correlated with cognitive outcome (Spearman rho =0.31; p < 0.05). No univariate measure showed significant correlation after correction for multiple comparisons.Conclusions: Heterogeneity in the severity and distribution of injuries after TBI has traditionally complicated the understanding of outcomes after TBI. Our approach provides a single, continuous variable that can fully capture individual heterogeneity. M’s ability to distinguish even mildly-injured patients from controls, and its correlation with cognitive assessment, suggest utility as an imaging-based marker of intrinsic injury severity.
Author(s): Taylor PN, da Silva NM, Blamire A, Wang Y, Forsyth RJ
Publication type: Article
Publication status: Published
Pages: epub ahead of print
Online publication date: 14/01/2020
Acceptance date: 03/09/2019
Date deposited: 10/09/2019
ISSN (print): 0028-3878
ISSN (electronic): 1526-632X
Publisher: American Academy of Neurology
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