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。此类映射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存在个体差异，针对单一个体的分析对于深化我们对认知过程与大脑激活模式对应关系的理解至关重要。此类分析亦有助于推动fMRI的临床应用，因为临床场景中对单个病例进行诊断是必要的。 本研究采用心理意象任务，对单一个体的语言生成、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。分析方法基于相似性指标，包括训练数据与测试数据间的相关性，以及与神经合成（NeuroSynth）元分析映射结果之间的相关性。本研究的目标是针对单个30秒任务块的认知领域（例如语言）与具体内容（例如动物名称）作出精准预测。共有四个团队使用了该数据集，且所有团队均对测试数据的真实标签处于盲态。 结果显示，在预测任务块的认知领域时，相似性指标可达到最高的准确率水平。总体而言，25个测试块中有23个可被四个团队中的三个团队正确预测。排除非特异性静息态条件后，针对具体内容的解码中，最多可成功识别20个任务块中的10个。 本研究证明，仅通过单次fMRI扫描及其单个任务块所包含的信息，即可推断个体所参与的认知过程。诸如fMRI数据任务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。我们还在临床fMRI应用的背景下讨论了本研究结果的意义，重点探讨了解码技术如何助力功能定位。