Department of Biomedical Engineering, Washington University in St. Louis, USA; Medical Scientist Training Program, Washington University in St. Louis, USA.
CAS, Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Beijing, China; Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Medical Image Analysis Lab, The Mind Research Network, USA.
Neuroimage. 2017 Dec;163:41-54. doi: 10.1016/j.neuroimage.2017.08.081. Epub 2017 Sep 1.
Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.
认知控制是一个概念，它指的是通过任务状态、目标和规则的表示来进行决策和任务执行的一系列功能。人类的认知控制的神经相关性已经通过多种神经影像学模式进行了研究，包括结构磁共振成像、静息态功能磁共振成像和任务态功能磁共振成像。这些模式各自的结果都表明，许多脑区参与了认知控制，包括背侧前额叶皮层，以及额顶叶和扣带前回脑网络。然而，目前还不清楚单一模式的结果与其他模式的结果有何关联。多模态图像分析方法的最新进展为回答这些问题提供了途径，并可能产生认知控制神经相关性的更综合模型。在这项研究中，我们使用多集典型相关分析与联合独立成分分析（mCCA + jICA）来识别与认知控制相关的多模态变化模式。我们使用来自人类连接组计划的两个独立参与者队列，每个队列都有来自四种成像模式的数据。我们使用独立和预测分析在第二个队列中复制了第一个队列的结果。独立分析在每个队列中识别出一个与其他队列非常相似且与认知控制表现显著相关的成分。通过预测分析的复制，在第一个队列中识别出两个与认知控制表现显著相关且在第二个队列中显著预测表现的独立成分。这些成分在与任务控制的动态和稳定方面相关的神经区域中识别出了模态之间的正相关关系，包括额顶叶和扣带前回网络中的区域，以及被认为受认知控制信号调节的区域，如视觉皮层。总之，这些结果表明多模态分析在识别不同脑结构和功能指标的认知控制神经相关性方面具有潜在的应用价值。
