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跨研究预测成瘾:使用数据驱动方法时的概念和方法考虑因素述评。

Prediction of craving across studies: A commentary on conceptual and methodological considerations when using data-driven methods.

机构信息

1General Psychology: Cognition, Faculty of Computer Science, University of Duisburg-Essen, Duisburg, Germany.

2Center for Behavioral Addiction Research (CeBAR), Center for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Germany.

出版信息

J Behav Addict. 2024 Oct 1;13(3):695-701. doi: 10.1556/2006.2024.00050. Print 2024 Oct 4.

Abstract

Craving is a central feature of substance use disorders and disorders due to addictive behaviors. Considerable research has investigated neural mechanisms involved in the development and processing of craving. Recently, connectome-based predictive modeling, a data-driven method, has been used in four studies aiming to predict craving related to substance use, addictive behaviors, and food. Studies differed in methods, samples, and conceptualizations of craving. Within the commentary we aim to compare, contrast and consolidate findings across studies by considering conceptual and methodological features of the studies. We derive a theoretical model on the functional connectivity-craving relationships across studies.

摘要

craving 是物质使用障碍和成瘾行为障碍的一个核心特征。大量研究已经调查了涉及 craving 发展和处理的神经机制。最近,基于连接组的预测建模,一种数据驱动的方法,已经被用于四项旨在预测与物质使用、成瘾行为和食物相关的 craving 的研究。这些研究在方法、样本和 craving 的概念化方面存在差异。在评论中,我们旨在通过考虑研究的概念和方法特征来比较、对比和整合研究结果。我们得出了一个关于 across 研究中功能连接与 craving 关系的理论模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bef/11457034/f92ea4b4bcb0/jba-13-695-g001.jpg

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