School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
Brain Struct Funct. 2021 Jun;226(5):1437-1452. doi: 10.1007/s00429-021-02249-0. Epub 2021 Mar 20.
It is thought that brain structure is the primary determinant of functions of brain regions. For example, cortical areas with functional differences also have different structural connectivity (SC) patterns. We used SCs derived from diffusion tensor imaging (DTI) data in 100 healthy adults included in the Human Connectome Project (HCP) to successfully predict cortical activation responses across distinct cognitive tasks and found that predictive performance varied among tasks. We also observed that predictive performance could be used to characterize task load in both relational reasoning and N-back working memory tasks and was significantly positively associated with behavioral performance. Moreover, we found that the default mode network (DMN) played a more dominant role in both activation prediction and behavioral performance than was found for other functional networks. These results support our hypothesis that individuals who performed tasks better might exhibit a more accurate predicted activation pattern as task-evoked activities are more inclined to flow over inherent structural networks than over more flexible paths. In the high difficulty condition, the decreased correlation between predicted and empirical activation may be associated with the more random brain activity in these conditions/participants due to the lack of engagement. Together, our findings highlight the feasibility of using SCs to estimate various cognitive task activations and thus further facilitate the exploration of the relationship between the brain and behavior by providing strong evidence for the relevance of structure to function in the human brain.
人们认为大脑结构是大脑区域功能的主要决定因素。例如，具有不同功能的皮质区域也具有不同的结构连接（SC）模式。我们使用来自人类连接组计划（HCP）中 100 名健康成年人的扩散张量成像（DTI）数据得出的 SC，成功地预测了不同认知任务中的皮质激活反应，并且发现预测性能因任务而异。我们还观察到，预测性能可用于表征关系推理和 N-back 工作记忆任务中的任务负荷，并且与行为表现呈显著正相关。此外，我们发现与其他功能网络相比，默认模式网络（DMN）在激活预测和行为表现中均起着更为重要的作用。这些结果支持我们的假设，即表现更好的个体可能表现出更准确的预测激活模式，因为任务诱发的活动更倾向于在固有结构网络上流动，而不是在更灵活的路径上流动。在高难度条件下，预测和经验激活之间的相关性降低可能与这些条件/参与者中大脑活动更加随机有关，因为缺乏参与度。总之，我们的研究结果突出了使用 SC 来估计各种认知任务激活的可行性，从而通过为大脑结构与功能的相关性提供有力证据，进一步促进了对大脑与行为之间关系的探索。
