WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING.
WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING.
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DOI：
10.1214/15-aoas829
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Ogden RT
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文献类型：
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作者：
Reiss PT;Huo L;Zhao Y;Kelly C;Ogden RT
精神病学的一个日益重要的目标是利用大脑成像数据来开发预测模型。在这里，我们为此目的提出了对统计方法的两项贡献。首先，我们提出并比较了一组小波域程序，用于拟合具有标量响应和图像预测变量的广义线性模型：主成分回归和偏最小二乘的稀疏变体以及弹性网络。其次，我们考虑评估图像预测器相对于可用标量预测器的贡献，特别是通过排列测试以及将混杂思想扩展到函数或图像预测器的情况。使用所提出的方法，我们评估了源自功能性磁共振成像的自发大脑活动测量图是否可以有意义地预测注意力缺陷/多动障碍（ADHD）的存在或不存在。我们的结果揭示了混杂因素在最近 ADHD-200 全球竞赛令人惊讶的结果中的作用，该竞赛要求研究人员开发基于图像的疾病自动诊断算法。
An increasingly important goal of psychiatry is the use of brain imaging data to develop predictive models. Here we present two contributions to statistical methodology for this purpose. First, we propose and compare a set of wavelet-domain procedures for fitting generalized linear models with scalar responses and image predictors: sparse variants of principal component regression and of partial least squares, and the elastic net. Second, we consider assessing the contribution of image predictors over and above available scalar predictors, in particular via permutation tests and an extension of the idea of confounding to the case of functional or image predictors. Using the proposed methods, we assess whether maps of a spontaneous brain activity measure, derived from functional magnetic resonance imaging, can meaningfully predict presence or absence of attention deficit/hyperactivity disorder (ADHD). Our results shed light on the role of confounding in the surprising outcome of the recent ADHD-200 Global Competition, which challenged researchers to develop algorithms for automated image-based diagnosis of the disorder.