Purpose of Review
Recent innovations in the statistical analysis of neuroimaging data related to adolescent substance use are highlighted. Going beyond assumptions of homogeneity in small studies of regional localization, the focus is on novel approaches that integrate across regions of the brain and levels of analysis in order to detect individual differences in use along with antecedents and consequences.
Three analysis approaches are considered. Multimodal approaches like the construct-network framework combine neural, behavioral (including cognitive), and self-report indicators to create comprehensive representations of risk factors for adolescent substance use. Machine learning approaches link adolescent substance use to complex patterns of brain activity detected using prediction-focused algorithms. Person-specific approaches reflect heterogeneity in functional brain connectivity associated with adolescent substance use.
When applied to specialized datasets, multimodal, machine learning, and person-specific approaches have significant potential to provide unique insights into the neural processes underlying adolescent substance use.
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Alexander Weigard was supported by NIAAA T32 AA007477 (to Dr. Frederick Blow).
This article does not contain any studies with human or animal subjects performed by any of the authors.
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Adriene Beltz and Alexander Weigard declare that they have no conflict of interest.
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This article is part of the Topical Collection on Adolescent / Young Adult Addiction
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Beltz, A.M., Weigard, A. Methodological Advances in Leveraging Neuroimaging Datasets in Adolescent Substance Use Research. Curr Addict Rep 6, 495–503 (2019). https://doi.org/10.1007/s40429-019-00275-x
- Alcohol use
- Brain structure and function
- Machine learning
- Magnetic resonance imaging
- Multimodal approaches
- Person-specific analyses