Early detection of cognitive impairments in HIV carriers is essential. Neuroimaging studies has shown brain structure and function of HIV-positive patients are associated with cognition malfunction. However, the conclusions reported from previous studies are mainly based on the univariate analysis, such as t-test. In recent days, increasing attention have focused on the multivariate pattern analysis. Hence, in current study, we demonstrated the aberrant degree centrality (DC) using a machine learning approach. Our results suggested that DC might be an indicator to early detect neurocognitive impairments.
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