Santo-Angles Aniol, Temudo Ainsley, Babushkin Vahan, Sreenivasan Kartik K
Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Front Hum Neurosci. 2024 Mar 4;18:1339728. doi: 10.3389/fnhum.2024.1339728. eCollection 2024.
Visual working memory (WM) engages several nodes of a large-scale network that includes frontal, parietal, and visual regions; however, little is understood about how these regions interact to support WM behavior. In particular, it is unclear whether network dynamics during WM maintenance primarily represent feedforward or feedback connections. This question has important implications for current debates about the relative roles of frontoparietal and visual regions in WM maintenance. In the current study, we investigated the network activity supporting WM using MEG data acquired while healthy subjects performed a multi-item delayed estimation WM task. We used computational modeling of behavior to discriminate correct responses (high accuracy trials) from two different types of incorrect responses (low accuracy and swap trials), and dynamic causal modeling of MEG data to measure effective connectivity. We observed behaviorally dependent changes in effective connectivity in a brain network comprising frontoparietal and early visual areas. In comparison with high accuracy trials, frontoparietal and frontooccipital networks showed disrupted signals depending on type of behavioral error. Low accuracy trials showed disrupted feedback signals during early portions of WM maintenance and disrupted feedforward signals during later portions of maintenance delay, while swap errors showed disrupted feedback signals during the whole delay period. These results support a distributed model of WM that emphasizes the role of visual regions in WM storage and where changes in large scale network configurations can have important consequences for memory-guided behavior.
视觉工作记忆（WM）涉及一个大规模网络的多个节点，该网络包括额叶、顶叶和视觉区域；然而，对于这些区域如何相互作用以支持WM行为，我们了解甚少。特别是，尚不清楚WM维持期间的网络动态主要代表前馈连接还是反馈连接。这个问题对于当前关于额叶顶叶和视觉区域在WM维持中的相对作用的争论具有重要意义。在当前的研究中，我们使用健康受试者执行多项目延迟估计WM任务时获取的脑磁图（MEG）数据，研究了支持WM的网络活动。我们使用行为的计算模型来区分正确反应（高精度试验）与两种不同类型的错误反应（低精度和交换试验），并使用MEG数据的动态因果模型来测量有效连接性。我们在一个包含额叶顶叶和早期视觉区域的脑网络中观察到了有效连接性的行为依赖性变化。与高精度试验相比，额叶顶叶和额枕网络根据行为错误的类型显示出信号中断。低精度试验在WM维持的早期部分显示出反馈信号中断，在维持延迟的后期部分显示出前馈信号中断，而交换错误在整个延迟期显示出反馈信号中断。这些结果支持了一个WM的分布式模型，该模型强调视觉区域在WM存储中的作用，以及大规模网络配置的变化可能对记忆引导行为产生重要影响。
