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

CHARTER Group

Research output: Contribution to journalArticlepeer-review

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 andwhite matter and spatially normalized before entering into machine learning models. Prediction of cognitive function at baseline and longitudinally was determined using leave-oneout 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 R2 = 0.08, P < 0.01 vs. adjusted R2 = 0.01, P = 0.14). Accurate prediction of longitudinal changes in cognitive function was not possible (P = 0.82). Brainpredicted age exceeded chronological age by mean (95% confidence interval) 1.17 (20.14 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.

Original languageEnglish (US)
Pages (from-to)429-436
Number of pages8
JournalJournal of acquired immune deficiency syndromes
Volume78
Issue number4
DOIs
StatePublished - 2018

Keywords

  • Cognitive impairment
  • HIV
  • Machine learning
  • Multivariate analysis
  • Neuroimaging

ASJC Scopus subject areas

  • Infectious Diseases
  • Pharmacology (medical)

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