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.

中文翻译：

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认知神经映射的本体论维度。

越来越多的文献报道了认知神经映射的结果，这要求对这些映射进行适当的组织本体或分类法的呼吁。这个企业是不平凡的，因为可能会促成这种本体的相关维度尚未达成共识。我们建议应评估任何候选尺寸的能力，以解释其在功能性神经影像激活模式中观察到的差异。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）结果样本，并使用数据驱动策略来识别这些维度。首先，使用数据驱动的降维方法和多元距离矩阵回归（MDMR），我们可以量化激活图之间的差异，这可用现有的本体维度来解释。我们发现，“任务范式”类别比其他维度（包括潜在的认知类别）解释了任务激活图之间的更多差异。令人惊讶的是，“研究ID”或报告每个激活图的研究解释了激活模式差异的近50％。使用允许重叠集群的聚类方法，我们得出了数据驱动的潜在激活状态，这些状态与规范的额顶，显着，感觉运动和默认模式网络激活模式的重复出现相关。重要的是，仅使用四个数据驱动的潜在维度，就可以解释激活图之间比所有常规本体维度的总和更大的方差。这些潜在的维度可能会为数据驱动的认知本体提供信息，