Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut Street, Philadelphia, 19104, PA, USA.
Department of Neurology, The University of Pennsylvania: Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, 19104, PA, USA.
Neuroimage. 2023 Dec 1;283:120386. doi: 10.1016/j.neuroimage.2023.120386. Epub 2023 Oct 10.
Cognitive control (CC) is essential for problem-solving in everyday life, and CC-related deficits occur alongside costly and debilitating disorders. The tri-partite model suggests that CC comprises multiple behaviors, including switching, inhibiting, and updating. Activity within the fronto-parietal control network B (FPCN-B), the dorsal attention network (DAN), the cingulo-opercular network (CON), and the lateral default-mode network (L-DMN) is related to switching and inhibiting behaviors. However, our understanding of how these brain regions interact to bring about cognitive switching and inhibiting in individuals is unclear. In the current study, subjects performed two in-scanner tasks that required switching and inhibiting. We used support vector regression (SVR) models containing individually-estimated functional connectivity between the FPCN-B, DAN, CON and L-DMN to predict switching and inhibiting behaviors. We observed that: inter-network connectivity can predict inhibiting and switching behaviors in individuals, and the L-DMN plays a role in switching and inhibiting behaviors. Therefore, individually estimated inter-network connections are markers of CC behaviors, and CC behaviors may arise due to interactions between a set of networks.
认知控制(CC)是日常生活中解决问题的关键,而与 CC 相关的缺陷伴随着代价高昂和使人衰弱的疾病。三分模型表明,CC 包括多种行为,包括转换、抑制和更新。额顶控制网络 B(FPCN-B)、背侧注意网络(DAN)、扣带回-顶叶网络(CON)和外侧默认模式网络(L-DMN)中的活动与转换和抑制行为有关。然而,我们对这些大脑区域如何相互作用以在个体中引起认知转换和抑制的理解还不清楚。在当前的研究中,受试者执行了两个需要转换和抑制的扫描内任务。我们使用支持向量回归(SVR)模型,该模型包含 FPCN-B、DAN、CON 和 L-DMN 之间的个体估计功能连接,以预测转换和抑制行为。我们观察到:网络间连接可以预测个体的抑制和转换行为,而 L-DMN 在转换和抑制行为中起作用。因此,个体估计的网络间连接是 CC 行为的标志物,CC 行为可能是由于一组网络的相互作用而产生的。