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Toward Addiction Prediction: An Overview of Cross-Validated Predictive Modeling Findings and Considerations for Future Neuroimaging Research.走向成瘾预测:交叉验证预测模型研究结果概述及对未来神经影像学研究的思考。
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超重和肥胖的脑指纹识别

Identification of a brain fingerprint for overweight and obesity.

作者信息

Farruggia Michael C, van Kooten Maria J, Perszyk Emily E, Burke Mary V, Scheinost Dustin, Constable R Todd, Small Dana M

机构信息

Interdepartmental Neuroscience Program, Yale University, 333 Cedar Street, New Haven, CT, U.S.; Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA.

Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, USA; University of Groningen, Faculty of Medical Sciences, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.

出版信息

Physiol Behav. 2020 Aug 1;222:112940. doi: 10.1016/j.physbeh.2020.112940. Epub 2020 May 14.

DOI:10.1016/j.physbeh.2020.112940
PMID:32417645
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7321926/
Abstract

The brain plays a central role in the pathophysiology of overweight and obesity. Connectome-based Predictive Modeling (CPM) is a newly developed, data-driven approach that exploits whole-brain functional connectivity to predict a behavior or trait that varies across individuals. We used CPM to determine whether brain "fingerprints" evoked during milkshake consumption could be isolated for common measures of adiposity in 67 adults with overweight and obesity. We found that CPM captures more variance in waist circumference than either percent body fat or BMI, the most frequently used measures to assess brain correlates of obesity. In a post-hoc analysis, we were also able to derive a largely separable functional connectivity network predicting fasting blood insulin. These findings suggest that, in individuals with overweight and obesity, brain network patterns may be more tightly coupled to waist circumference than BMI or percent body fat and that adiposity and glucose tolerance are associated with distinct maps, pointing to dissociable central pathophysiological phenotypes for obesity and diabetes.

摘要

大脑在超重和肥胖的病理生理学中起着核心作用。基于连接组的预测模型(CPM)是一种新开发的数据驱动方法,它利用全脑功能连接来预测个体间不同的行为或特征。我们使用CPM来确定在饮用奶昔期间诱发的大脑“指纹”是否可以用于67名超重和肥胖成年人的常见肥胖指标。我们发现,与评估肥胖的大脑相关性最常用的指标——体脂百分比或体重指数相比,CPM能捕捉到更多腰围变化。在事后分析中,我们还能够得出一个基本可分离的功能连接网络来预测空腹血胰岛素。这些发现表明,在超重和肥胖个体中,大脑网络模式与腰围的耦合可能比体重指数或体脂百分比更紧密,而且肥胖和葡萄糖耐量与不同的图谱相关,这表明肥胖和糖尿病存在可分离的中枢病理生理表型。