Multivoxel pattern analysis of functional brain imaging data for the prediction of the development and maintenance of alcohol use disorders

功能性脑成像数据的多体素模式分析，用于预测酒精使用障碍的发展和维持

• 批准号: 209301691
• 负责人: Dr. Katharina Schmack
• 金额: --
• 依托单位: The Francis Crick Institute
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2017-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/209301691?language=en
• 关键词: Multivoxel; pattern; analysis; functional; brain

Previous neuroimaging studies aimed at predicting clinical outcome in alcohol addiction have provided encouraging results. However, conventional univariate statistical approaches suffer from the problem that the predictive value of single surrogate markers, such as activity in a given brain region, is usually low despite the presence of a statistically significant relationship. Multivoxel pattern analysis (MVPA) can dramatically increase predictive power by accumulating information across multiple voxels, i.e., to use the information contained in a distributed pattern of brain activity rather than a single brain region. MVPA has been successfully applied both at the individual subject level and for diagnostic or prognostic classification between subjects. Functional and structural magnetic resonance imaging (MRI) data from Project 1 will be used to predict pathological drinking patterns in young adults. Treatment outcomes in alcohol dependent patients will be predicted on the basis of MRI data from Project 2. Crucially, MVPA will be used to identify those features, or patterns of features, in functional and structural MRI scans that reliably predict future clinical outcomes. This knowledge will inform pathogenetic models for neurobiological mechanisms underlying pathological drinking and help tailor therapeutic interventions in accord with an individual¿s neurobiological risk profile.

先前旨在预测酒精成瘾临床结果的神经影像学研究提供了令人鼓舞的结果。然而，传统的单变量统计方法存在这样的问题：尽管存在统计上显着的关系，但单个替代标记（例如给定大脑区域的活动）的预测值通常较低。多体素模式分析（MVPA）可以通过积累多个体素的信息来显着提高预测能力，即使用大脑活动的分布式模式而不是单个大脑区域中包含的信息。 MVPA 已成功应用于个体受试者水平以及受试者之间的诊断或预后分类。项目 1 的功能和结构磁共振成像 (MRI) 数据将用于预测年轻人的病理性饮酒模式。酒精依赖患者的治疗结果将根据项目 2 的 MRI 数据进行预测。至关重要的是，MVPA 将用于识别功能和结构 MRI 扫描中的那些特征或特征模式，从而可靠地预测未来的临床结果。这些知识将为病理性饮酒背后的神经生物学机制的发病模型提供信息，并有助于根据个人的神经生物学风险状况制定治疗干预措施。