Beltz Adriene M, Weigard Alexander
Department of Psychology, University of Michigan, 2227 East Hall, 530 Church Street, Ann Arbor, MI 48109, USA.
Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI 48109, USA.
Curr Addict Rep. 2019 Dec;6(4):495-503. doi: 10.1007/s40429-019-00275-x. Epub 2019 Sep 9.
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.
重点介绍与青少年物质使用相关的神经影像数据统计分析方面的最新创新。超越区域定位小型研究中的同质性假设，重点关注整合大脑区域和分析层面的新方法，以便检测使用情况的个体差异及其前因后果。
考虑了三种分析方法。像构建网络框架这样的多模态方法结合神经、行为（包括认知）和自我报告指标，以创建青少年物质使用风险因素的综合表征。机器学习方法将青少年物质使用与使用以预测为重点的算法检测到的复杂大脑活动模式联系起来。个体特异性方法反映了与青少年物质使用相关的功能性脑连接的异质性。
当应用于特定数据集时，多模态、机器学习和个体特异性方法有很大潜力为青少年物质使用背后的神经过程提供独特见解。
