Reiss Philip T, Huo Lan, Zhao Yihong, Kelly Clare, Ogden R Todd
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Ann Appl Stat. 2015 Jun;9(2):1076-1101. doi: 10.1214/15-AOAS829. Epub 2015 Jul 20.
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.
精神病学一个日益重要的目标是利用脑成像数据来开发预测模型。在此，我们为此目的对统计方法做出了两项贡献。首先，我们提出并比较了一组用于拟合具有标量响应和图像预测变量的广义线性模型的小波域程序：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变量相对于可用标量预测变量的贡献，特别是通过置换检验以及将混杂概念扩展到功能或图像预测变量的情况。使用所提出的方法，我们评估从功能磁共振成像得出的自发脑活动测量图是否能够有意义地预测注意缺陷多动障碍（ADHD）的存在与否。我们的结果揭示了混杂在近期ADHD - 200全球竞赛令人惊讶的结果中所起的作用，该竞赛要求研究人员开发基于图像的该疾病自动诊断算法。
