Thought experiment: Decoding cognitive processes from the fMRI data of one individual

Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.

诸如语言生成等认知过程，可通过功能磁共振成像（fMRI）映射至大脑。此类脑映射图可进一步用于从大脑激活模式中解码对应认知过程。鉴于大脑解剖结构与组织方式存在个体差异，针对个体水平的分析对于深化我们对认知过程与大脑活动模式对应关系的理解至关重要。此外，这类分析还可推动功能磁共振成像的临床应用，因为临床场景中针对单个病例开展诊断不可或缺。本研究采用心理意象任务，对单个受试者的语言产生、运动功能、视觉空间记忆、面孔加工以及静息态活动进行了探究。分析方法基于相似度指标，包括训练集与测试集数据间的相关性，以及与NeuroSynth元分析所得映射图的相关性。本研究的目标是针对单个30秒扫描块的认知领域（如语言）与具体内容（如动物名称）作出精准预测。共有四支研究团队使用本数据集，所有团队均对测试数据的真实标签处于盲态。结果显示，在预测扫描块所属认知领域时，相似度指标可实现最高的预测精度。总体而言，25个测试扫描块中有23个可被四支团队中的三支团队正确预测。排除无特异性的静息态条件后，20个扫描块中最多有10个可被成功解码其具体内容。本研究证实，单次功能磁共振成像扫描及其每个扫描块所包含的信息，可用于推断个体所参与的认知过程。诸如fMRI数据扫描块间相关性这类简单方法，可作为认知解码的高可靠性手段。我们将结合功能磁共振成像的临床应用场景讨论本研究结果的启示，重点探讨解码技术如何助力功能定位。