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Abstract #1898

Evidence of altered degree centrality on patients with HIV: a machine learning approach

Danhui Fu1, Guanqiao Jin1, Lidong Liu1, Wenjuan Deng1, Sen Hong1, Qianlin Ding1, Long Qian2, Weiyin Vivian Liu2, and Danke Su1
1GuangXi Medical University Cancer Hospital, Nanning, China, 2MR Research, GE Healthcare, Beijin, China

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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