Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Jun;4(6):589-599.
doi: 10.1016/j.bpsc.2019.02.003. Epub 2019 Mar 1.

Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals

Ehsan Adeli  1 , Natalie M Zahr  2 , Adolf Pfefferbaum  2 , Edith V Sullivan  1 , Kilian M Pohl  3
Affiliations

Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals

Ehsan Adeli et al. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Jun.

Abstract

The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identifying structural brain systems that predicted diagnosis on an individual basis. Applying novel machine learning analysis to 549 participants (199 control subjects, 222 with AUD, 68 with HIV, 60 with AUD+HIV), 298 magnetic resonance imaging brain measurements were automatically reduced to small subsets per group. Significance of each diagnostic pattern was inferred from its accuracy in predicting diagnosis and performance on six cognitive measures. While all three diagnostic patterns predicted the learning and memory score, the AUD+HIV pattern was the largest and had the highest predication accuracy (78.1%). Providing a roadmap for analyzing large, multimodal datasets, the machine learning analysis revealed imaging phenotypes that predicted diagnostic membership of magnetic resonance imaging scans of individuals with AUD, HIV, and their comorbidity.

Keywords: Alcoholism; Brain imaging; Comorbidity; Disease patterns; HIV infection; Machine learning.

PubMed Disclaimer

Conflict of interest statement

Disclosures

All authors report no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1
Figure 1
The analysis approach used for identifying diagnostic pattern and score specific to AUD, HIV, and AUD+HIV. It includes three major steps: multivariate analysis for identification of diagnostic pattern and score (Step 1), cognitive correlations (Step 2), and individual-level predictions (Step 3).
Figure 2
Figure 2
The diagnostic patterns (see also Table 2) for alcohol use dependency (AUD), HIV-infection (HIV), and the comorbidity (AUD+HIV). Measures in the HIV (purple) or AUD pattern (green) also appearing in the comorbidity pattern are shown in plaid in AUD+HIV.
Figure 3
Figure 3
Cognitive measures significantly correlating with the diagnostic scores. The cognitive scores decline with increasing diagnostic scores. (a) Learning and Memory (LM) measure as a function of diagnostic score, for AUD vs. CTRL (b) Executive Function (EXF) measure as a function of diagnostic score, for AUD vs. CTRL (c) Learning and Memory (LM) measure as a function of diagnostic score, for HIV vs. CTRL (d) Speed of Information Processing (SIP) measure as a function of diagnostic score, for HIV vs. CTRL (e) Learning and Memory (LM) measure as a function of diagnostic score, for AUD+HIV vs. CTRL (f) Speed of Information Processing (SIP) measure as a function of diagnostic score, for AUD+HIV vs. CTRL
Figure 4
Figure 4
Diagnostic scores for each sample with respect to the three diagnosis-specific group comparisons (CTRL vs. AUD, CTRL vs. HIV, CTRL vs. AUD+HIV).
See this image and copyright information in PMC

Comment in

References

    1. Organization WH, Unit WHOMoSA (2014): Global status report on alcohol and health, 2014 World Health Organization.
    1. Gongvatana A, Morgan EE, Iudicello JE, Letendre SL, Grant I, Woods SP, et al. (2014): A history of alcohol dependence augments HIV-associated neurocognitive deficits in persons aged 60 and older. J Neurovirol 20:505–513. - PMC - PubMed
    1. Fama R, Sullivan EV, Sassoon SA, Pfefferbaum A, Zahr NM (2016): Impairments in Component Processes of Executive Function and Episodic Memory in Alcoholism, HIV Infection, and HIV Infection with Alcoholism Comorbidity. Alcohol Clin Exp Res 40:2656–2666. - PMC - PubMed
    1. Pfefferbaum A, Rosenbloom MJ, Sassoon SA, Kemper CA, Deresinski S, Rohlfing T, et al. (2012): Regional brain structural dysmorphology in human immunodeficiency virus infection: effects of acquired immune deficiency syndrome, alcoholism, and age. Biol Psychiatry 72:361–370. - PMC - PubMed
    1. Justice A, Sullivan L, Fiellin D, Veterans Aging Cohort Study Project T (2010): HIV/AIDS, comorbidity, and alcohol: can we make a difference? Alcohol Res Health 33:258–266. - PMC - PubMed

Publication types