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Mapping abnormal subcortical brain morphometry in an elderly HIV+ cohort.

Benjamin S C Wade¹, Victor G Valcour², Lauren Wendelken-Riegelhaupt²

¹Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA.

Neuroimage. Clinical

|December 8, 2015

Summary

Neuroimaging reveals significant subcortical brain atrophy in individuals with HIV (human immunodeficiency virus), particularly in the thalamus and hippocampus. Machine learning models could distinguish HIV+ individuals from controls with 72% accuracy.

Keywords:

CD4, cluster of differentiation Classification HIV JD, Jacobian determinant MRI RD, radial distance Random forest Shape analysis Subcortical

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Area of Science:

Neuroimaging
Neurology
Medical Science

Background:

Over 50% of individuals with HIV experience neurocognitive impairment and subcortical atrophy.
The precise brain abnormalities linked to HIV remain incompletely understood.

Purpose of the Study:

To map the 3D profile of subcortical morphometry in elderly individuals with HIV using surface-based shape analysis.
To investigate the effects of disease parameters and cognition on subcortical morphology.
To assess the distinguishability of HIV+ individuals from controls using machine learning.

Main Methods:

Surface-based shape analyses (Jacobian determinant and radial distances) were applied to high-resolution MRIs of 63 HIV+ participants and 31 controls.
Subcortical regions including the thalamus, caudate, putamen, and hippocampus were segmented.
A random forest model incorporating shape and volume features was used for classification and validated with cross-validation.

Main Results:

HIV+ participants showed reduced volumes in the bilateral thalamus, left pallidum, left putamen, and callosum, with enlarged ventricles.
Significant shape variations correlated with HIV status, time since diagnosis, and cognitive scores (Wechsler adult intelligence scale).
Diffuse atrophy was observed, especially in the caudate, putamen, hippocampus, and thalamus; extended time since diagnosis was linked to increased anterior right pallidum thickness.
The random forest model achieved an area under the curve of 72% for classifying HIV+ individuals.

Conclusions:

HIV is associated with widespread subcortical atrophy and ventricular enlargement, impacting cognitive function.
Surface-based morphometry and machine learning show potential for characterizing HIV-related brain changes and aiding in classification.