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Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV

Luckett, Patrick PhDa; Paul, Robert H. PhDb; Navid, Jaimie BSa; Cooley, Sarah A. PhDa; Wisch, Julie K. PhDa; Boerwinkle, Anna H. BSa; Tomov, Dimitre MSa; Ances, Beau M. MD, PhD, MSca

JAIDS Journal of Acquired Immune Deficiency Syndromes: December 15, 2019 - Volume 82 - Issue 5 - p 496–502
doi: 10.1097/QAI.0000000000002181
Translational Research
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Background: Deep learning algorithms of cerebral blood flow were used to classify cognitive impairment and frailty in people living with HIV (PLWH). Feature extraction techniques identified brain regions that were the strongest predictors.

Setting: Virologically suppressed (<50 copies/mL) PLWH (n = 125) on combination antiretroviral therapy were enrolled. Participants averaged 51.4 (11.4) years of age and 13.7 (2.8) years of education. Participants were administered a neuropsychological battery, assessed for frailty, and completed structural neuroimaging.

Methods: Deep neural network (DNN) models were trained to classify PLWH as cognitively unimpaired or impaired based on neuropsychological tests (Hopkins Verbal Learning Test-Revised and Brief Visuospatial Memory Test-Revised, Trail making, Letter-Number Sequencing, Verbal Fluency, and Color Word Interference), as well as frail, prefrail, or nonfrail based on the Fried phenotype criteria (at least 3 of the following 5: weight loss, physical inactivity, exhaustion, grip strength, walking time).

Results: DNNs classified individuals with cognitive impairment in the learning, memory, and executive domains with 82%–86% accuracy (0.81–0.87 AUC). Our model classified nonfrail, prefrail, and frail PLWH with 75% accuracy. The strongest predictors of cognitive impairment were cortical (parietal, occipital, and temporal) and subcortical (amygdala, caudate, and hippocampus) regions, whereas the strongest predictors of frailty were subcortical (amygdala, caudate, hippocampus, thalamus, pallidum, and cerebellum).

Conclusions: DNN models achieved high accuracy in classifying cognitive impairment and frailty status in PLWH. Feature selection algorithms identified predictive regions in each domain and identified overlapping regions between cognitive impairment and frailty. Our results suggest frailty in HIV is primarily subcortical, whereas cognitive impairment in HIV involves subcortical and cortical brain regions.

aDepartment of Neurology, Washington University School of Medicine, St. Louis, MI; and

bDepartment of Psychological Sciences, University of Missouri Saint Louis, St. Louis, MI.

Correspondence to: Beau M. Ances, MD, PhD, MSc, Department of Neurology, Campus Box 8111 660 S. Euclid Avenue, St. Louis, MO 63110 (e-mail: bances@wustl.edu).

Supported by grants from the National Institutes of Health (R01NR012657 and R01NR014449). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.

The authors have no conflicts of interest to disclose.

Received April 16, 2019

Accepted September 02, 2019

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.