Resting state dynamic functional connectivity: Network topology analysis

Abstract Biologically inspired cognitive architectures are based on the data obtained by studying the mechanisms of the brain’s functional networks operations, the causality of their integration and differentiation into the neurophysiological structures of the cognitive processes of consciousness. One of the critical network involved in maintaining the core level of consciousness at the resting state is the default mode network (DMN). The DMN reduces its activity with an external stimulation and behavioral task and increases with perceived mental states as imagination, internal dialogue, and others. A complete loss of consciousness is characterized by the synchronization of DMN with the anticorrelated with DMN network. When the level of consciousness changes in the processes of cognitive activity, there is a complex picture of the combination of positive and negative connections between different networks and regions of the brain. At the same time, the changes in the intrinsic brain organization during the cognitive process and the resting state still an open question. The primary purpose of this work is studying the dynamics of the different brain networks interactions at the resting state, such as executive and attention networks, cerebellum, DMN, visual and auditory network, brainstem, the somatosensory and motor networks and subcortical network. We used three algorithms for clustering states in neural network connectivity dynamics: direct clustering of the functional network with the k-means algorithm, modularity-based and topological based clustering. We obtained that in the dynamics of functional connectomes there are three expressed states, determined by different types of interactions between DMN networks, attention and other neural networks.