Gallup, Data Science Division, Washington, DC, USA.
Department of Psychology, University of Miami, P.O. Box 248185, Coral Gables, FL, 33124, USA.
Neuroinformatics. 2020 Jun;18(3):451-463. doi: 10.1007/s12021-020-09454-y.
The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that 'task paradigm' categories explain more variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, 'study ID', or the study from which each activation map was reported, explained close to 50% of the variance in activation patterns. Using a clustering approach that allows for overlapping clusters, we derived data-driven latent activation states, associated with re-occurring configurations of the canonical frontoparietal, salience, sensory-motor, and default mode network activation patterns. Importantly, with only four data-driven latent dimensions, one can explain greater variance among activation maps than all conventional ontological dimensions combined. These latent dimensions may inform a data-driven cognitive ontology, and suggest that current descriptions of cognitive processes and the tasks used to elicit them do not accurately reflect activation patterns commonly observed in the human brain.
越来越多的报告认知神经映射结果的文献呼吁建立一个适当的认知神经映射组织本体论或分类学。这项工作并非微不足道，因为尚未就可能有助于这种本体论的相关维度达成一致。我们提出，任何候选维度都应根据其解释功能神经影像学激活模式中观察到的差异的能力来进行评估。在这项研究中，我们使用了大量基于任务的功能磁共振成像（task-fMRI）结果和数据驱动策略来识别这些维度。首先，我们使用数据驱动的降维方法和多元距离矩阵回归（MDMR），量化了现有本体论维度解释激活图之间差异的方差。我们发现，“任务范式”类别比其他维度（包括潜在认知类别）更能解释任务激活图之间的方差。令人惊讶的是，“研究 ID”，即报告每个激活图的研究，解释了激活模式方差的近 50%。使用允许重叠聚类的聚类方法，我们推导出了数据驱动的潜在激活状态，这些状态与经典额顶叶、突显、感觉运动和默认模式网络激活模式的重复配置相关联。重要的是，仅使用四个数据驱动的潜在维度，就可以解释比所有传统本体论维度组合更多的激活图之间的方差。这些潜在维度可能为数据驱动的认知本体论提供信息，并表明当前对认知过程及其诱发任务的描述不能准确反映人类大脑中常见的激活模式。
