TY - JOUR

T1 - Resting state dynamic functional connectivity

T2 - Network topology analysis

AU - Knyazeva, Irina

AU - Poyda, Alexey

AU - Orlov, Vyacheslav

AU - Verkhlyutov, Vitaliy

AU - Makarenko, Nikolai

AU - Kozlov, Stanislav

AU - Velichkovsky, Boris

AU - Ushakov, Vadim

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Algebraic topology

KW - Complex networks

KW - Dynamics

KW - fMRI

KW - Functional connectivity

KW - Resting state

UR - http://www.scopus.com/inward/record.url?scp=85033607976&partnerID=8YFLogxK

U2 - 10.1016/j.bica.2017.10.001

DO - 10.1016/j.bica.2017.10.001

M3 - Article

AN - SCOPUS:85033607976

VL - 23

SP - 43

EP - 53

JO - Biologically Inspired Cognitive Architectures

JF - Biologically Inspired Cognitive Architectures

SN - 2212-683X

ER -