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Neurosci Lett. 2018 May 29;676:27-33. doi: 10.1016/j.neulet.2018.04.007. Epub 2018 Apr 4.

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

Author information

1
Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States. Electronic address: xi.zhu@nyspi.columbia.edu.
2
Data Scientist Team, Adtheorent, New York, NY, United States.
3
Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States.
4
Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States.
5
Clinical NeuroImaging Research Core, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States. Electronic address: rezam@mail.nih.gov.

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.

KEYWORDS:

Alcohol dependence; Functional connectivity; ICA; Resting state; fMRI

PMID:
29626649
PMCID:
PMC5960433
[Available on 2019-05-29]
DOI:
10.1016/j.neulet.2018.04.007

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