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Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI.
- Source :
-
Neuroscience letters [Neurosci Lett] 2018 May 29; Vol. 676, pp. 27-33. Date of Electronic Publication: 2018 Apr 04. - Publication Year :
- 2018
-
Abstract
- 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.<br /> (Copyright © 2018 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1872-7972
- Volume :
- 676
- Database :
- MEDLINE
- Journal :
- Neuroscience letters
- Publication Type :
- Academic Journal
- Accession number :
- 29626649
- Full Text :
- https://doi.org/10.1016/j.neulet.2018.04.007