Zhao Wanyun, Su Kaiqiang, Zhu Hengcheng, Kaiser Marcus, Fan Mingxia, Zou Yong, Li Ting, Yin Dazhi
Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.
Division of Biostatistics, University of Minnesota, Minneapolis 55455, MN, USA.
Neuroimage. 2024 Aug 15;297:120761. doi: 10.1016/j.neuroimage.2024.120761. Epub 2024 Jul 27.
Flexible cognitive functions, such as working memory (WM), usually require a balance between localized and distributed information processing. However, it is challenging to uncover how local and distributed processing specifically contributes to task-induced activity in a region. Although the recently proposed activity flow mapping approach revealed the relative contribution of distributed processing, few studies have explored the adaptive and plastic changes that underlie cognitive manipulation. In this study, we recruited 51 healthy volunteers (31 females) and investigated how the activity flow and brain activation of the frontoparietal systems was modulated by WM load and training. While the activation of both executive control network (ECN) and dorsal attention network (DAN) increased linearly with memory load at baseline, the relative contribution of distributed processing showed a linear response only in the DAN, which was prominently attributed to within-network activity flow. Importantly, adaptive training selectively induced an increase in the relative contribution of distributed processing in the ECN and also a linear response to memory load, which were predominantly due to between-network activity flow. Furthermore, we demonstrated a causal effect of activity flow prediction through training manipulation on connectivity and activity. In contrast with classic brain activation estimation, our findings suggest that the relative contribution of distributed processing revealed by activity flow prediction provides unique insights into neural processing of frontoparietal systems under the manipulation of cognitive load and training. This study offers a new methodological framework for exploring information integration versus segregation underlying cognitive processing.
灵活的认知功能，如工作记忆（WM），通常需要局部和分布式信息处理之间的平衡。然而，要揭示局部和分布式处理如何具体促进一个区域内任务诱发的活动是具有挑战性的。尽管最近提出的活动流映射方法揭示了分布式处理的相对贡献，但很少有研究探讨认知操纵背后的适应性和可塑性变化。在本研究中，我们招募了51名健康志愿者（31名女性），并研究了额顶叶系统的活动流和大脑激活如何受到工作记忆负荷和训练的调节。在基线时，执行控制网络（ECN）和背侧注意网络（DAN）的激活均随记忆负荷线性增加，但分布式处理的相对贡献仅在DAN中呈现线性反应，这主要归因于网络内的活动流。重要的是，适应性训练选择性地诱导了ECN中分布式处理相对贡献的增加以及对记忆负荷的线性反应，这主要归因于网络间的活动流。此外，我们通过训练操纵证明了活动流预测对连接性和活动的因果效应。与经典的大脑激活估计不同，我们的研究结果表明，活动流预测揭示的分布式处理的相对贡献为认知负荷和训练操纵下额顶叶系统的神经处理提供了独特的见解。本研究为探索认知处理背后的信息整合与分离提供了一个新的方法框架。
