Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI

• Published in: Neuroscience letters Vol. 676; pp. 27 - 33
• Main Authors: Zhu, Xi, Du, Xiaofei, Kerich, Mike, Lohoff, Falk W., Momenan, Reza
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 29-05-2018
• Subjects: Alcohol dependence; fMRI; Functional connectivity; ICA; Resting state; Functional connectivity; fMRI; Alcohol dependence; Resting state; ICA
• ISSN: 0304-3940; 1872-7972
• DOI: 10.1016/j.neulet.2018.04.007

•Diagnosis of alcohol use disorder using resting state functional connectivity.•Quantify resting-state within-network and between-network connectivity features in a multivariate fashion.•Offer new insights into the identification of potential biomarkers for the clinical diagnosis of AUD.
Currently, classification 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 systematic way of diagnosis and provide novel insights into the pathophysiology of AUD. In this study, we identified network-level brain features of AUD, and further quantified resting-state within-network, and between-network connectivity features in a multivariate fashion that are classifying AUD, thus providing additional information about how each network contributes to alcoholism.
Resting-state fMRI were collected from 92 individuals (46 controls and 46 AUDs). Probabilistic Independent Component Analysis (PICA) was used to extract brain functional networks and their corresponding time-course for AUD and controls. Both within-network connectivity for each network and between-network connectivity for each pair of networks were used as features. 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, respectively. 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. The between-network connectivity between RN-Default Mode Network (DMN) and RN-ECN contribute the most to the prediction.
In conclusion, within-network functional connectivity offered maximal information for AUD classification, when compared with between-network connectivity. Further, our results suggest that connectivity within the ECN and RN are informative in classifying 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.