Brain decoding that classifies cognitive states using the functional fluctuations of the brain can provide insightful information for understanding the brain mechanisms of cognitive functions. Among the common procedures of decoding the brain cognitive states with functional magnetic resonance imaging (fMRI), extracting the time series of each brain region after brain parcellation traditionally averages across the voxels within a brain region. This neglects the spatial information among the voxels and the requirement of extracting information for the downstream tasks. In this study, we propose to use a fully connected neural network that is jointly trained with the brain decoder to perform an adaptively weighted average across the voxels within each brain region. We perform extensive evaluations by cognitive state decoding, manifold learning, and interpretability analysis on the Human Connectome Project (HCP) dataset. The performance comparison of the cognitive state decoding presents an accuracy increase of up to 5\% and stable accuracy improvement under different time window sizes, resampling sizes, and training data sizes. The results of manifold learning show that our method presents a considerable separability among cognitive states and basically excludes subject-specific information. The interpretability analysis shows that our method can identify reasonable brain regions corresponding to each cognitive state. Our study would aid the improvement of the basic pipeline of fMRI processing.

翻译：利用大脑功能波动对认知状态进行分类的脑解码，能为理解认知功能的脑机制提供深刻见解。在利用功能磁共振成像（fMRI）解码大脑认知状态的常见流程中，大脑分区后提取各脑区时间序列的传统方法是对脑区内所有体素进行平均。这种做法忽略了体素间的空间信息以及下游任务对信息提取的需求。在本研究中，我们提出使用一个与脑解码器联合训练的全连接神经网络，对每个脑区内的体素执行自适应加权平均。我们在人类连接组计划（HCP）数据集上，通过认知状态解码、流形学习和可解释性分析进行了广泛评估。认知状态解码的性能对比显示，准确率最高提升5%，且在不同时间窗大小、重采样大小和训练数据规模下均呈现稳定的准确率提升。流形学习结果表明，我们的方法在认知状态间呈现出显著的可分离性，并基本排除了被试特异性信息。可解释性分析显示，我们的方法能识别出与各认知状态相对应的合理脑区。本研究将有助于改进fMRI处理的基本流程。