Cognitive Challenges Are Better in Distinguishing Binge From Nonbinge Drinkers: An Exploratory Deep‐Learning Study of fMRI Data of Multiple Behavioral Tasks and Resting State. Issue 3 (9th July 2022)

• Record Type: Journal Article
• Authors: Li, Guangfei; Zhang, Zhao; Chen, Yu; Wang, Wuyi; Bi, Jinbo; Tang, Xiaoying; Li, Chiang‐Shan R.
• Is Part Of: Journal of magnetic resonance imaging. Volume 57:Issue 3(2023)
• Page Start: 856
• Page End: 868
• Publication Date: 2022-07-09
• Subjects: alcohol use disorder -- binge drinking -- gamble -- interpretability -- neural network; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.28336
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Studies have identified imaging markers of binge drinking. Functional connectivity during both task challenges and resting state was shown to distinguish binge and nonbinge drinkers. However, no studies have compared the efficacy of task and resting data in the classification. Hypothesis: Task outperforms resting‐state functional magnetic resonance imaging (fMRI) data in the differentiation of binge and nonbinge drinkers. We tested the hypothesis via multiple deep learning algorithms. Study Type: Cross‐sectional; retrospective. Population: A total of 149 binge (107 men) and 151 demographically matched, nonbinge (92 men) drinkers curated from the Human Connectome Project, with 80% randomly selected for model development and 20% for validation/test. Field Strength/Sequence: A 3 T; fMRI with a blood oxygen level‐dependent (BOLD) gradient‐echo echo‐planar sequence. Assessment: FMRI data of resting state and seven behavioral tasks were acquired. Graph convolutional network (GCN), long short‐term memory, convolutional, and recurrent neural network models were built to distinguish bingers and nonbingers using connectivity matrices of 8, 116, and 268 regions of interest (ROI). Nodal metrics including betweenness centrality, degree centrality, clustering coefficient, efficiency, local efficiency, and shortest path length were calculated from the GCN model. Statistical Tests: Model performance was quantified by the area under the curve (AUC) in receiver operating characteristic analysis. A P value < 0.05 was considered statistically significant. Results: Task outperformed resting data in classification by approximately 8% by AUC in the test set. Across models and ROI sets, the gambling, motor, language and working memory tasks, each with AUC of 0.614, 0.612, 0.605, and 0.603, performed better than resting data (AUC = 0.548). Models with 116 ROIs (AUC = 0.602) consistently outperformed those with 8 ROIs (AUC = 0.569). Task data performed best with GCN (AUC = 0.619). Nodal metrics of left supplementary motor area and right cuneus showed significant group main effect across tasks. Conclusion: Neural responses to cognitive challenges relative to resting state better characterize binge drinking. The performance of different network models may depend on behavioral tasks and the number of ROIs. Evidence Level: 3 Technical Efficacy: Stage 2