NIH Research Festival
Currently, diagnosis of Alcohol Use Disorder (AUD) is made on clinical grounds; however, robust evidence shows that chronic alcohol use leads to neurochemical and neurocircuitry adaptations. Identifications of the neuronal networks that are affected by alcohol would provide a more reliable way of diagnosis and provide novel insights into the pathophysiology of AUD. In this study, we applied machine-learning algorithms to quantify resting state (RS) within-network and between-network connectivity features in a multivariate fashion and that are diagnostic and potentially predictive of AUD. RS-fMRI were collected from 46 controls and 46 AUDs. Probabilistic Independent Component Analysis (PICA) was used to extract brain functional networks and their corresponding time-course. Random forest was applied for pattern classification. The results showed that within-networks features were able to identify AUD and control with 87.0% accuracy and 90.5% precision. Networks that were most informative included Executive Control Networks (ECN), and Reward Network (RN). The between-network features achieved 67.4% accuracy and 70.0% precision. In conclusion, within-network connectivity offered maximal information for AUD diagnosis, when compared with between-network connectivity. Further, our results suggest that connectivity within the ECN and RN are informative in predicting AUD. To our knowledge, this is the first study using RS connectivity to classify AUD. Our findings suggest that machine-learning algorithms provide an alternative technique to quantify large-scale network differences and offer new insights into the identification of potential biomarkers for the clinical diagnosis of AUD.
Scientific Focus Area: Neuroscience
This page was last updated on Friday, March 26, 2021