Department of Psychology, Stanford University, Stanford, CA, USA.
Department of Psychology, University of California Berkeley, Berkeley, CA, USA.
Neuroimage. 2022 Nov;263:119610. doi: 10.1016/j.neuroimage.2022.119610. Epub 2022 Sep 3.
A deep understanding of the neural architecture of mental function should enable the accurate prediction of a specific pattern of brain activity for any psychological task, based only on the cognitive functions known to be engaged by that task. Encoding models (EMs), which predict neural responses from known features (e.g., stimulus properties), have succeeded in circumscribed domains (e.g., visual neuroscience), but implementing domain-general EMs that predict brain-wide activity for arbitrary tasks has been limited mainly by availability of datasets that 1) sufficiently span a large space of psychological functions, and 2) are sufficiently annotated with such functions to allow robust EM specification. We examine the use of EMs based on a formal specification of psychological function, to predict cortical activation patterns across a broad range of tasks. We utilized the Multi-Domain Task Battery, a dataset in which 24 subjects completed 32 ten-minute fMRI scans, switching tasks every 35 s and engaging in 44 total conditions of diverse psychological manipulations. Conditions were annotated by a group of experts using the Cognitive Atlas ontology to identify putatively engaged functions, and region-wise cognitive EMs (CEMs) were fit, for individual subjects, on neocortical responses. We found that CEMs predicted cortical activation maps of held-out tasks with high accuracy, outperforming a permutation-based null model while approaching the noise ceiling of the data, without being driven solely by either cognitive or perceptual-motor features. Hierarchical clustering on the similarity structure of CEM generalization errors revealed relationships amongst psychological functions. Spatial distributions of feature importances systematically overlapped with large-scale resting-state functional networks (RSNs), supporting the hypothesis of functional specialization within RSNs while grounding their function in an interpretable data-driven manner. Our implementation and validation of CEMs provides a proof of principle for the utility of formal ontologies in cognitive neuroscience and motivates the use of CEMs in the further testing of cognitive theories.
对心理功能的神经结构有深入的了解，应该能够仅根据该任务已知的认知功能，准确预测任何心理任务的特定脑活动模式。基于已知特征（例如，刺激属性）来预测神经反应的编码模型（EM）在特定领域（例如，视觉神经科学）取得了成功，但实现可预测任意任务的大脑广泛活动的领域通用 EM 主要受到以下两个方面的限制：1）数据集足够广泛地涵盖了大量的心理功能，2）数据集足够详细地标注了这些功能，以便可以进行稳健的 EM 规范。我们检查了基于心理功能的正式规范的 EM 的使用，以预测广泛任务的皮质激活模式。我们利用了多领域任务电池，这是一个数据集，其中 24 名受试者完成了 32 次十分钟的 fMRI 扫描，每隔 35 秒切换一次任务，并进行了 44 种不同心理操作的总条件。通过使用认知图谱本体对条件进行标注，以确定潜在的参与功能，然后针对个体受试者的皮质反应来拟合局部认知 EM（CEM）。我们发现，CEM 可以非常准确地预测保留任务的皮质激活图，优于基于随机排列的零模型，同时接近数据的噪声上限，而不是仅受认知或感知运动特征驱动。基于 CEM 泛化误差相似性结构的层次聚类揭示了心理功能之间的关系。特征重要性的空间分布与大规模静息态功能网络（RSN）系统地重叠，支持 RSN 内功能专业化的假设，同时以可解释的数据驱动方式为其功能提供依据。我们对 CEM 的实现和验证为认知神经科学中正式本体的实用性提供了一个原理证明，并促使在进一步测试认知理论时使用 CEM。
