Mahdi Moradi1, Hamed Ekhtiari2, Rayus Kuplicki3, Brett McKinney4, Jennifer L Stewart5, Teresa A Victor6, Martin P Paulus7. 1. Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States; Department of Computer Science, J. Newton Rayzor Hall, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, United States. Electronic address: mmoradi@laureateinstitute.org. 2. Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States. Electronic address: hekhtiari@laureateinstitute.org. 3. Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States. Electronic address: rkuplicki@laureateinstitute.org. 4. Department of Computer Science, J. Newton Rayzor Hall, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, United States; Department of Mathematics, Keplinger Hall 3085, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, United States. Electronic address: brett-mckinney@utulsa.edu. 5. Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States; Department of Community Medicine, Oxley Health Sciences, The University of Tulsa, 1215 S. Boulder Ave, Tulsa, OK, 74119, United States. Electronic address: jstewart@laureateinstitute.org. 6. Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States. Electronic address: tvictor@laureateinstitute.org. 7. Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, United States; Department of Community Medicine, Oxley Health Sciences, The University of Tulsa, 1215 S. Boulder Ave, Tulsa, OK, 74119, United States; Department of Psychiatry, University of California, San Diego, United States. Electronic address: mpaulus@laureateinstitute.org.
Abstract
BACKGROUND: There is a lack of neuroscience-based biomarkers for the diagnosis, treatment and monitoring of individuals with substance use disorders (SUD). The resource allocation index (RAI), a measure of the interrelationship between salience, executive control and default-mode brain networks (SN, ECN, and DMN), has been proposed as one such biomarker. However, the RAI has yet to be extensively tested in SUD samples. METHODS: The present analysis compared RAI scores between individuals with stimulant and/or opioid use disorders (SUD; n = 139, abstinent 4-365 days) and healthy controls (HC; n = 56) who had completed resting-state functional magnetic resonance imaging (fMRI) scans within the context of the Tulsa 1000 cohort. First, we used independent component analysis (ICA) to identify the SN, ECN, and DMN and extract their time series data. Second, we used multiple permutations of automatically identified networks to compute RAI as reported in the fMRI literature. RESULTS: First, the RAI as a metric depended substantially on the approach that was used to define the network components. Second, regardless of the selection of networks, after controlling for multiple testing there was no difference in RAI scores between SUD and HC. Third, the RAI was not associated with any substance use-related self-report measures. CONCLUSION: Taken together, these findings do not provide evidence that RAI can be used as an fMRI-derived biomarker for the severity or diagnosis of individuals with SUD.
BACKGROUND: There is a lack of neuroscience-based biomarkers for the diagnosis, treatment and monitoring of individuals with substance use disorders (SUD). The resource allocation index (RAI), a measure of the interrelationship between salience, executive control and default-mode brain networks (SN, ECN, and DMN), has been proposed as one such biomarker. However, the RAI has yet to be extensively tested in SUD samples. METHODS: The present analysis compared RAI scores between individuals with stimulant and/or opioid use disorders (SUD; n = 139, abstinent 4-365 days) and healthy controls (HC; n = 56) who had completed resting-state functional magnetic resonance imaging (fMRI) scans within the context of the Tulsa 1000 cohort. First, we used independent component analysis (ICA) to identify the SN, ECN, and DMN and extract their time series data. Second, we used multiple permutations of automatically identified networks to compute RAI as reported in the fMRI literature. RESULTS: First, the RAI as a metric depended substantially on the approach that was used to define the network components. Second, regardless of the selection of networks, after controlling for multiple testing there was no difference in RAI scores between SUD and HC. Third, the RAI was not associated with any substance use-related self-report measures. CONCLUSION: Taken together, these findings do not provide evidence that RAI can be used as an fMRI-derived biomarker for the severity or diagnosis of individuals with SUD.
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