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Abstract #3964

A machine learning investigation of resting-state fMRI abnormalities in HIV-associated neurocognitive disorder

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

Frascati criteria for diagnosis of HIV-associated neurocognitive disorder lacks sensitivity, specificity, susceptible to learning effect, socioeconomic position and other confounding factors. fMRI can sensitively discover pathophysiological changes in HIV-infected central nervous system. In our study, ALFF and fALFF separately well classified HIV-positive and HIV-negative groups with AUC = 0.65 and 0.62, respectively. Our findings of injured frontal,parietal and occipital cortex associated with execution and attention function were consistent with previous studies, indicating activations reduced in the frontal lobe but increased in occipital lobe among patients with HAND. Both ALFF and fALFF could be biomarkers for objectively classifying HIV individuals.

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