Evaluating the resource allocation index as a potential fMRI-based biomarker for substance use disorder. (1st November 2020)

• Record Type: Journal Article
• Authors: Moradi, Mahdi; Ekhtiari, Hamed; Kuplicki, Rayus; McKinney, Brett; Stewart, Jennifer L.; Victor, Teresa A.; Paulus, Martin P.
• Is Part Of: Drug and alcohol dependence. Volume 216(2020)
• Publication Date: 2020-11-01
• Subjects: Resting-state fMRI -- Independent component analysis -- Resource allocation index -- Substance use disorder -- Biomarker -- Default mode network -- Executive control network -- Salience network; Drug abuse -- Periodicals; Alcoholism -- Periodicals; 616.86
• DOI: 10.1016/j.drugalcdep.2020.108211
• Languages: English
• ISSNs: 0376-8716

Abstract

Highlights: Neuroscience-based biomarkers are lacking for substance use disorders (SUD). Resource allocation index (RAI) has yet to be extensively tested in SUD samples. RAI fell short to be a consistent biomarker for SUD. RAI was dependent on the approach that was used to define the network components. No difference in RAI scores between SUD and HC after controlling for multiple testing. 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.