Arun Venkataraman1, Yuchuan Zhuang2, Jennifer Marsella3, Madalina E Tivarus4,5, Xing Qiu6, Lu Wang6, Jianhui Zhong1,4, Giovanni Schifitto3,4. 1. Department of Physics and Astronomy, University of Rochester, Rochester, NY, USA. 2. Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA. 3. Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA. 4. Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY, USA. 5. Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA. 6. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.
Abstract
OBJECTIVE: To examine resting-state functional MRI (rs-fMRI) networks related to sleep in the context of HIV infection. METHODS: rs-fMRI data were collected in 40 HIV-infected (HIV+) individuals at baseline (treatment-naive), 12 week (post-treatment) and one year timepoints. A group of 50 age-matched HIV-negative (HIV-) individuals were also imaged at baseline and one year timepoints. The Pittsburgh Sleep Quality Index (PSQI) questionnaire was administered at all timepoints. Using group independent component analysis (ICA), maps of functional networks were generated from fMRI data; from these, sleep-related networks were selected. A generalized linear model (GLM) was used to analyze if these networks were significantly associated with the PSQI score, and if this relationship was influenced by HIV status/treatment or timepoint. RESULTS: HIV+ individuals had significantly lower PSQI score after treatment (p=0.022). Networks extracted from group ICA analysis included the anterior and posterior default mode network (DMN), central executive network (CEN), bilateral frontoparietal networks (FPNs), and the anterior cingulate cortex salience network (ACC SN). We found the posterior DMN, right FPN, and ACC SN GLMs showed significantly higher goodness-of-fit after incorporating PSQI data (p = 0.0204, 0.044, 0.044, respectively). Furthermore, the correlation between ACC SN and posterior DMN connectivity was significantly decreased in the HIV+ cohort. CONCLUSION: Functional networks such as the DMN, FPN, CEN, and ACC SN are altered in poor sleep, as measured by the PSQI score. Furthermore, the relationship between these networks and PSQI is different in the HIV+ and HIV- populations.
OBJECTIVE: To examine resting-state functional MRI (rs-fMRI) networks related to sleep in the context of HIV infection. METHODS: rs-fMRI data were collected in 40 HIV-infected (HIV+) individuals at baseline (treatment-naive), 12 week (post-treatment) and one year timepoints. A group of 50 age-matched HIV-negative (HIV-) individuals were also imaged at baseline and one year timepoints. The Pittsburgh Sleep Quality Index (PSQI) questionnaire was administered at all timepoints. Using group independent component analysis (ICA), maps of functional networks were generated from fMRI data; from these, sleep-related networks were selected. A generalized linear model (GLM) was used to analyze if these networks were significantly associated with the PSQI score, and if this relationship was influenced by HIV status/treatment or timepoint. RESULTS: HIV+ individuals had significantly lower PSQI score after treatment (p=0.022). Networks extracted from group ICA analysis included the anterior and posterior default mode network (DMN), central executive network (CEN), bilateral frontoparietal networks (FPNs), and the anterior cingulate cortex salience network (ACC SN). We found the posterior DMN, right FPN, and ACC SN GLMs showed significantly higher goodness-of-fit after incorporating PSQI data (p = 0.0204, 0.044, 0.044, respectively). Furthermore, the correlation between ACC SN and posterior DMN connectivity was significantly decreased in the HIV+ cohort. CONCLUSION: Functional networks such as the DMN, FPN, CEN, and ACC SN are altered in poor sleep, as measured by the PSQI score. Furthermore, the relationship between these networks and PSQI is different in the HIV+ and HIV- populations.
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