Matsui Teppei, Taki Masato, Pham Trung Quang, Chikazoe Junichi, Jimura Koji
Department of Biology, Okayama University, Okayama, Japan.
JST-PRESTO, Japan Science and Technology Agency, Tokyo, Japan.
Front Neuroinform. 2022 Mar 16;15:802938. doi: 10.3389/fninf.2021.802938. eCollection 2021.
Deep neural networks (DNNs) can accurately decode task-related information from brain activations. However, because of the non-linearity of DNNs, it is generally difficult to explain how and why they assign certain behavioral tasks to given brain activations, either correctly or incorrectly. One of the promising approaches for explaining such a black-box system is counterfactual explanation. In this framework, the behavior of a black-box system is explained by comparing real data and realistic synthetic data that are specifically generated such that the black-box system outputs an unreal outcome. The explanation of the system's decision can be explained by directly comparing the real and synthetic data. Recently, by taking advantage of advances in DNN-based image-to-image translation, several studies successfully applied counterfactual explanation to image domains. In principle, the same approach could be used in functional magnetic resonance imaging (fMRI) data. Because fMRI datasets often contain multiple classes (e.g., multiple behavioral tasks), the image-to-image transformation applicable to counterfactual explanation needs to learn mapping among multiple classes simultaneously. Recently, a new generative neural network (StarGAN) that enables image-to-image transformation among multiple classes has been developed. By adapting StarGAN with some modifications, here, we introduce a novel generative DNN (counterfactual activation generator, CAG) that can provide counterfactual explanations for DNN-based classifiers of brain activations. Importantly, CAG can simultaneously handle image transformation among all the seven classes in a publicly available fMRI dataset. Thus, CAG could provide a counterfactual explanation of DNN-based multiclass classifiers of brain activations. Furthermore, iterative applications of CAG were able to enhance and extract subtle spatial brain activity patterns that affected the classifier's decisions. Together, these results demonstrate that the counterfactual explanation based on image-to-image transformation would be a promising approach to understand and extend the current application of DNNs in fMRI analyses.
深度神经网络（DNN）能够从大脑激活中准确解码与任务相关的信息。然而，由于DNN的非线性，通常很难解释它们如何以及为何将特定的行为任务分配给给定的大脑激活，无论正确与否。解释这种黑箱系统的一种有前景的方法是反事实解释。在这个框架中，通过比较真实数据和专门生成的逼真合成数据来解释黑箱系统的行为，使得黑箱系统输出一个不真实的结果。系统决策的解释可以通过直接比较真实数据和合成数据来进行。最近，利用基于DNN的图像到图像转换的进展，一些研究成功地将反事实解释应用于图像领域。原则上，相同的方法可用于功能磁共振成像（fMRI）数据。由于fMRI数据集通常包含多个类别（例如，多个行为任务），适用于反事实解释的图像到图像转换需要同时学习多个类别之间的映射。最近，一种能够在多个类别之间进行图像到图像转换的新型生成神经网络（StarGAN）已经被开发出来。通过对StarGAN进行一些修改，在这里，我们引入了一种新型的生成DNN（反事实激活生成器，CAG），它可以为基于DNN的大脑激活分类器提供反事实解释。重要的是，CAG能够同时处理公开可用的fMRI数据集中所有七个类别的图像转换。因此，CAG可以为基于DNN的大脑激活多类别分类器提供反事实解释。此外，CAG的迭代应用能够增强并提取影响分类器决策的细微空间大脑活动模式。总之，这些结果表明基于图像到图像转换的反事实解释将是一种有前景的方法，用于理解和扩展DNN在fMRI分析中的当前应用。
