Neurocognitive impairment continues to be common
comorbidity for people living with HIV (PLWH). Given the
chronic nature of HIV disease, identifying reliable biomarkers
of these impairments is essential to advance our understanding
of the underlying neural foundation and facilitate screening
and diagnosis in clinical care. While neuroimaging provides
immense potential for such biomarkers, to date, investigations in
PLWH have been mostly limited to either univariate mass techniques
or a single neuroimaging modality. In the present study,
connectome-based predictive modeling (CPM) was proposed to
predict individual differences of cognitive functioning in PLWH,
using resting-state functional connectivity (FC), white matter
structural connectivity (SC), and clinical relevant measures. We
also adopted an efficient feature selection approach to identify the
most predictive features, which achieved an optimal prediction
accuracy of r = 0.61 in the discovery dataset (n = 102) and r =
0.45 in an independent validation HIV cohort (n = 88). Two brain
templates and nine distinct prediction models were also tested for
better modeling generalizability. Results show that combining
multimodal FC and SC features enabled higher prediction
accuracy of cognitive scores in PLWH, while adding clinical
and demographic metrics may further improve the prediction by
introducing complementary information, which may help better
evaluate the individual-level cognitive performance in PLWH.