Multivariate Pattern Analysis of Volumetric Neuroimaging Data and Its Relationship With Cognitive Function in Treated HIV Disease. (1st August 2018)

• Record Type: Journal Article
• Authors: Underwood, Jonathan; Cole, James H.; Leech, Robert; Sharp, David J.; Winston, Alan
• Is Part Of: Journal of acquired immune deficiency syndromes. Volume 78:Number 4(2018)
• Publication Date: 2018-08-01
• Subjects: HIV -- cognitive impairment -- neuroimaging -- machine learning -- multivariate analysis; AIDS (Disease) -- Periodicals; Acquired Immunodeficiency Syndrome -- Periodicals; AIDS (Disease); Periodicals; 616.9792005
• DOI: 10.1097/QAI.0000000000001687
• Languages: English
• ISSNs: 1525-4135

Abstract

Abstract : Background: Accurate prediction of longitudinal changes in cognitive function would potentially allow for 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 gray and white matter and spatially normalized before entering into machine learning models. Prediction of cognitive function at baseline and longitudinally was determined using leave-one-out cross-validation. In addition, a multivariate model of brain aging 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 (area under the receiver operator curve 0.59), prediction of the global T-score was better than a comparable linear model (adjusted R 2 = 0.08, P < 0.01 vs. adjusted R 2 = 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 to 2.53) years but was greatest in those with confounding comorbidities [5.87 (1.74 to 9.99) years] and prior AIDS [3.03 (0.00 to 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. Abstract : Supplemental Digital Content is Available in the Text.