| Title: | Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders |
| Journal: | Biological Psychiatry Cognitive Neuroscience and Neuroimaging |
| Published: | 9 Sep 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37696489/ |
| DOI: | https://doi.org/10.1016/j.bpsc.2023.08.010 |
Publication 9601
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Abstract
BACKGROUND: Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD.</p>
METHODS: Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses.</p>
RESULTS: Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets.</p>
CONCLUSIONS: Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature.</p>
6 Authors
- Tan Zhu
- Wuyi Wang
- Yu Chen
- Henry R Kranzler
- Chiang-Shan R Li
- Jinbo Bi
1 Application
| Application ID | Title |
|---|---|
| 51296 | Machine Learning Analysis of Brain Images, Behaviors and Genotypes to Understand Aging and Mental Disorders |
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