Multi-label, multi-domain learning identifies compounding effects of HIV and cognitive impairment

Abstract

Older individuals infected by Human Immunodeficiency Virus (HIV) are at risk for developing HIV-Associated Neurocognitive Disorder (HAND), i.e., from reduced cognitive functioning similar to HIV-negative individuals with Mild Cognitive Impairment (MCI) or to Alzheimer's Disease (AD) if more severely affected. Incompletely understood is how brain structure can serve to differentiate cognitive impairment (CI) in the HIV-positive (i.e., HAND) from the HIV-negative cohort (i.e., MCI and AD). To that end, we designed a multi-label classifier that labels the structural magnetic resonance images (MRI) of individuals by their HIV and CI status via two binary variables. Proper training of such an approach traditionally requires well-curated datasets containing large number of samples for each of the corresponding four cohorts (healthy controls, CI HIV-negative adults a.k.a. CI-only, HIV-positive patients without CI a.k.a. HIV-only, and HAND). Because of the rarity of such datasets, we proposed to improve training of the multi-label classifier via a multi-domain learning scheme that also incorporates domain-specific classifiers on auxiliary single-label datasets specific to either binary label. Specifically, we complement the training dataset of MRIs of the four cohorts (Control: 156, CI-only: 335, HIV-only: 37, HAND: 145) acquired by the Memory and Aging Center at the University of California - San Francisco with a CI-specific dataset only containing MRIs of HIV-negative subjects (Controls: 229, CI-only: 397) from the Alzheimer's Disease Neuroimaging Initiative and an HIV-specific dataset (Controls: 75, HIV-only: 75) provided by SRI International. Based on cross-validation on the UCSF dataset, the multi-domain and multi-label learning strategy leads to superior classification accuracy compared with one-domain or multi-class learning approaches, specifically for the undersampled HIV-only cohort. The 'prediction logits' of CI computed by the multi-label formulation also successfully stratify motor performance among the HIV-positive subjects (including HAND). Finally, brain patterns driving the subject-level predictions across all four cohorts characterize the independent and compounding effects of HIV and CI in the HAND cohort.

Keywords: Alzheimer’S disease; HIV-associated neurocognitive disorder; MRI; Multi-domain learning; Multi-label classification.

Figure 1:

HIV-Associated Neurocognitive Disorder (HAND) is a condition of cognitive impairment (CI) found in patients infected by the human immunodeficiency virus (HIV).

Figure 2:

The proposed domain-specific prediction model for multi-label classification.

Figure 3:

Prediction accuracy (averaged over 5 testing folds and last 10 epochs) for each cohort and the balanced accuracy (bAcc) over the four cohorts. The prediction accuracy of a null (random) classifier is 25%.

Figure 4:

(a) Distribution of bAcc over the last 10 epochs for each model. (b) Confusion matrix of the three-domain model.

Figure 5:

CI prediction scores for the stable and progressive CI cohorts produced by our three-domain model.

Figure 6:

(a) Finger tapping scores of the HIV-only and HAND cohorts; (b) The finger tapping score correlates with the CI prediction score in either the HIV-only or HAND cohort.

Figure 7:

(a) Patterns associated with HAND identified by the multi-domain model. Important cortical regions with high saliency are displayed on the pial surface (top), and important subcortical and cerebellar regions are displayed in the glass brain (bottom); (b) Critical regions for HIV prediction (blue) and for CI prediction (red).