Multivariate pattern analysis of volumetric neuroimaging data and its relationship with cognitive function in treated HIV-disease

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Title: Multivariate pattern analysis of volumetric neuroimaging data and its relationship with cognitive function in treated HIV-disease
Authors: Underwood, J
Cole, JH
Leech, R
Sharp, DJ
Winston, A
Item Type: Journal Article
Abstract: BACKGROUND: Accurate prediction of longitudinal changes in cognitive function would potentially allow targeted intervention in those at greatest risk of cognitive decline. We sought to build a multivariate model using volumetric neuroimaging data alone to accurately predict cognitive function. METHODS: Volumetric T1-weighted neuroimaging data from virally suppressed HIV-positive individuals from the CHARTER cohort (n=139) were segmented into grey and white matter and spatially normalised before were entering into machine learning models. Prediction of cognitive function at baseline and longitudinally was determined using leave-one-out cross validation. Additionally, a multivariate model of brain ageing was used to measure the deviation of apparent brain age from chronological age and assess its relationship with cognitive function. RESULTS: Cognitive impairment, defined using the global deficit score, was present in 37.4%. However, it was generally mild and occurred more commonly in those with confounding comorbidities (p<0.001). Although multivariate prediction of cognitive impairment as a dichotomous variable at baseline was poor (AUC 0.59), prediction of the global T-score was better than a comparable linear model (adjusted R=0.08, p<0.01 vs. adjusted R=0.01, p=0.14). Accurate prediction of longitudinal changes in cognitive function was not possible (p=0.82).Brain-predicted age exceeded chronological age by mean (95% confidence interval) 1.17 (-0.14-2.53) years, but was greatest in those with confounding comorbidities (5.87 [1.74-9.99] years) and prior AIDS (3.03 [0.00-6.06] years). CONCLUSION: Accurate prediction of cognitive impairment using multivariate models using only T1-weighted data was not achievable, which may reflect the small sample size, heterogeneity of the data or that impairment was usually mild.
Issue Date: 1-Aug-2018
Date of Acceptance: 1-Mar-2018
URI: http://hdl.handle.net/10044/1/58653
DOI: https://dx.doi.org/10.1097/QAI.0000000000001687
ISSN: 1525-4135
Publisher: Wolters Kluwer Health, Inc.
Start Page: 429
End Page: 436
Journal / Book Title: Journal of Acquired Immune Deficiency Syndromes
Volume: 78
Issue: 4
Copyright Statement: © 2018 Wolters Kluwer Health, Inc. All rights reserved. This is a non-final version of an article published in final form in Journal of Acquired Immune Deficiency Syndromes available at https://dx.doi.org/10.1097/QAI.0000000000001687
Keywords: Science & Technology
Life Sciences & Biomedicine
Immunology
Infectious Diseases
HIV
cognitive impairment
neuroimaging
machine learning
multivariate analysis
COMBINATION ANTIRETROVIRAL THERAPY
NEUROCOGNITIVE DISORDERS
BRAIN STRUCTURE
INFECTION
CHARTER
MORPHOMETRY
IMPAIRMENT
COHORT
ERA
AGE
CHARTER group
Virology
Publication Status: Published
Conference Place: United States
Online Publication Date: 2018-03-30
Appears in Collections:Department of Medicine
Faculty of Medicine



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