King's College London, Department of Neuroimaging, Institute of Psychiatry (PO89), De Crespigny Park, London SE5 8AF, UK.
Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London WC1N 3BG, UK.
Neuroimage. 2013 Nov 1;81:347-357. doi: 10.1016/j.neuroimage.2013.05.036. Epub 2013 May 17.
Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection.
神经影像学数据越来越多地被用于预测潜在的结果或分组，例如临床严重程度、药物剂量反应和过渡性疾病状态。在这些示例中，我们想要预测的变量（目标）本质上是有序的。传统的分类方案假设目标是名义的，因此忽略了它们的排序性质，而参数和/或非参数回归模型则强制在类之间具有度量距离的概念。在这里，我们提出了一种新颖的替代多变量方法来克服这些限制 - 使用高斯过程框架的全脑概率有序回归。我们将这项技术应用于来自健康志愿者的两个药理学神经影像学数据集。第一项研究旨在研究氯胺酮对大脑活动的影响及其随后与两种化合物 - 拉莫三嗪和利培酮的调制。第二项研究研究了东莨菪碱对脑血流及其使用多奈哌齐调制的影响。我们将有序回归与多类分类方案和度量回归进行了比较。考虑到氯胺酮与拉莫三嗪的调制，我们发现有序回归在准确性和平均绝对误差方面明显优于多类分类和度量回归。然而，对于利培酮，有序回归在准确性和平均绝对误差方面明显优于度量回归，但与多类分类相似。对于东莨菪碱数据集，考虑到前扣带皮层的局部脑血流，有序回归被发现优于多类和度量回归技术。因此，有序回归是所有情况下表现最好的唯一方法。我们的结果表明，有序回归方法在神经影像学数据中有潜力，同时提供了一个完全概率框架，具有用于模型选择的优雅方法。
