Multiscale and multimodal network dynamics underpinning working memory

Abstract

Working memory (WM) allows information to be stored and manipulated over short time scales. Performance on WM tasks is thought to be supported by the frontoparietal system (FPS), the default mode system (DMS), and interactions between them. Yet little is known about how these systems and their interactions relate to individual differences in WM performance. We address this gap in knowledge using functional MRI data acquired during the performance of a 2-back WM task, as well as diffusion tensor imaging data collected in the same individuals. We show that the strength of functional interactions between the FPS and DMS during task engagement is inversely correlated with WM performance, and that this strength is modulated by the activation of FPS regions but not DMS regions. Next, we use a clustering algorithm to identify two distinct subnetworks of the FPS, and find that these subnetworks display distinguishable patterns of gene expression. Activity in one subnetwork is positively associated with the strength of FPS-DMS functional interactions, while activity in the second subnetwork is negatively associated. Further, the pattern of structural linkages of these subnetworks explains their differential capacity to influence the strength of FPS-DMS functional interactions. To determine whether these observations could provide a mechanistic account of large-scale neural underpinnings of WM, we build a computational model of the system composed of coupled oscillators. Modulating the amplitude of the subnetworks in the model causes the expected change in the strength of FPS-DMS functional interactions, thereby offering support for a mechanism in which subnetwork activity tunes functional interactions. Broadly, our study presents a holistic account of how regional activity, functional interactions, and structural linkages together support individual differences in WM in humans.

References in corpus (11)

• Modularity and community structure in networks
• Controllability of Brain Networks
• Comparing Community Structure to Characteristics in Online Collegiate Social Networks
• Optimally controlling the human connectome: the role of network topology
• Learning Latent Block Structure in Weighted Networks
• A mechanistic model of connector hubs, modularity, and cognition
• The Diverse Club: The Integrative Core of Complex Networks
• Control of Dynamics in Brain Networks
• Diversity of meso-scale architecture in human and non-human connectomes
• Explicitly Linking Regional Activation and Function Connectivity: Community Structure of Weighted Networks with Continuous Annotation
• Symmetries and synchronization in multilayer random networks

Cited by in corpus (3)

• Models of communication and control for brain networks: distinctions, convergence, and future outlook
• The information content of brain states is explained by structural constraints on state energetics
• Slow manifolds in recurrent networks encode working memory efficiently and robustly