Dynamic Network Analysis Demonstrates the Formation of Stable Functional Networks During Rule Learning

Functional reconfiguration of task-active frontoparietal control network facilitates abstract reasoning

While the brain's functional network architecture is largely conserved between resting and task states, small but significant changes in functional connectivity support complex cognition. In this study, we used a modified Raven's Progressive Matrices Task to examine symbolic and perceptual reasoning in human participants undergoing fMRI scanning. Previously, studies have focused predominantly on discrete symbolic versions of matrix reasoning, even though the first few trials of the Raven's Advanced Progressive Matrices task consist of continuous perceptual stimuli. Our analysis examined the activation patterns and functional reconfiguration of brain networks associated with resting state and both symbolic and perceptual reasoning. We found that frontoparietal networks, including the cognitive control and dorsal attention networks, were significantly activated during abstract reasoning. We determined that these same task-active regions exhibited flexibly-reconfigured functional connectivity when transitioning from resting state to the abstract reasoning task. Conversely, we showed that a stable network core of regions in default and somatomotor networks was maintained across both resting and task states. We propose that these regionally-specific changes in the functional connectivity of frontoparietal networks puts the brain in a "task-ready" state, facilitating efficient task-based activation.

The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia

There is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node's integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.

NDCN-Brain: An Extensible Dynamic Functional Brain Network Model

As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases.