Department of Computer Science, ETH Zurich, Zurich, Switzerland.
PLoS Comput Biol. 2011 Jun;7(6):e1002079. doi: 10.1371/journal.pcbi.1002079. Epub 2011 Jun 23.
Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in 'hidden' physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups.
解码模型，如多元分类算法的基础模型，已经越来越多地被用于从功能磁共振成像 (fMRI) 获得的大脑活动测量中推断认知或临床大脑状态。然而，当前分类器的实用性受到两个主要挑战的限制。首先，由于数据维度高和样本量低，算法难以从信息和无信息特征中分离，导致泛化性能较差。其次，流行的判别方法，如支持向量机 (SVM)，很少具有机械可解释性。在本文中，我们通过提出一种新的生成式嵌入方法来解决这些问题，该方法将神经生物学可解释的生成模型纳入判别分类器中。我们的方法扩展了以前关于逐次分类的研究，将其应用于 fMRI 的个体分类，并提供了两个优于传统方法的关键优势：它可以通过利用隐藏的生理量（如突触连接强度）中的判别信息来提供更准确的预测；并且可以提供临床分类的机械可解释性。在这里，我们使用动态因果模型 (DCM) 和 SVM 的组合来引入 fMRI 的生成式嵌入。我们提出了一种基于 DCM 的生成式嵌入的个体分类的通用过程，提供了具体的实现，并提出了在 fMRI 背景下应用生成式嵌入的无偏应用的良好实践指南。我们通过一个临床示例来说明我们的方法的实用性，该示例使用语音处理期间丘脑-颞叶区域的 DCM 对中度失语症患者和健康对照组进行分类。生成式嵌入达到了近乎完美的平衡分类准确率 98%，显著优于传统的激活和相关方法。这个例子展示了如何以非常高的准确率检测疾病状态，同时根据连接异常从机械角度解释疾病状态。我们设想，生成式嵌入的未来应用可能会在将频谱障碍分解为生理上更明确的亚组方面提供关键进展。
