V.L. Ushakov - Ph.D. (Biol.), Head of Laboratory of Cognitive Brain Function Neurovisualisation, Kurchatov-s complex of NBICS-technologies, National Research Centre «Kurchatov institute» (Moscow). E-mail: tiuq@yandex.ru V.V. Zavyalova - Research-engineer, Laboratory of Cognitive Brain Function Neurovisualisation, Kurchatov-s complex of NBICS-technologies, National Research Centre «Kurchatov institute» (Moscow). E-mail: z1315@mail.ru V.A. Orlov - Research-engineer, Laboratory of Cognitive Brain Function Neurovisualisation, Kurchatov-s complex of NBICS-technologies, National Research Centre «Kurchatov institute» (Moscow). E-mail: ptica89@bk.ru S.I. Kartashov - Research-engineer, Laboratory of Cognitive Brain Function Neurovisualisation, Kurchatov-s complex of NBICS-technologies, National Research Centre «Kurchatov institute» (Moscow). E-mail: sikartashov@gmail.com M.G. Sharaev - Research-engineer, Laboratory of Cognitive Brain Function Neurovisualisation, Kurchatov-s complex of NBICS-technologies, National Research Centre «Kurchatov institute» (Moscow). E-mail: sikartashov@gmail.com A.A. Poyda - Ph.D. (Phis.-Math.), Head of Laboratory, Kurchatov-s complex of NBICS-technologies, National Research Centre «Kurchatov institute» (Moscow). E-mail: tiuq@yandex.ru B.M. Velichkovsky - Dc.Sc. (Psychol.), Head of Department of Neurocognitive and Socio-humanitarian Sciences, Kurchatov-s complex of NBICS-technologies, National Research centre "Kurchatov institute" (Moscow). E-mail: boris.velichkovsky@tu-dresden.de

This paper presents data on the combination of structural and functional connectome human brain at resting state and during functional motor task. Experimental data were obtained by the EEG and fMRI methods. For functional dynamic connectome were used methods of calculation based on independent component analisys (ICA). 5 healthy partisipants took part in this experiment (2 woman, 3 man at the age from 21 to 24 years) The internetwork connection were estimated by graph theory: the construction of efficient and directed connectome. For functional data coherence and according to Granger causality matrices were found. For obtained coherence and causality matrices in some arbitrary time points were constructed corresponding to weighted graphs. Assessment of interaction networks has been analyzed using graph theory: calculation of effective and directed connectomes. For the obtained graphs were calculated at a number of options, including: centrality; modularity, calculated as the spectral algorithm of Newman, and using the Louvain method; power of links (inbound, outbound, and total power). For constructing of structural connectomes we used methods of tractography (DTI). To highlight the most active tract in the performance of the cognitive tasks we used method of functional tractography (fDTI). Comparing obtained parameters with those obtained previously in the works of foreign researchers, it was found that the sets of nodes allocated as the management network on the basis of the characteristics obtained with the graph theory, largely coincide with the DMN network (default network). But coincidence is not always complete. The identification of the causes of differences and the optimization developed in this project methods for the isolation of control circuits based on graph theory and independent component will be continued in the next phase of the work. As possible causes of seen deviations are: a number of independent components allocated in the data is too small, a short time interval of data receiving, inaccurate settings of graphs in their building on the basis of coherence and causality matrices, the imposition of additional activities, etc. Model checking of interaction networks among themselves proceeded on the basis of the method of dynamic causal modeling (DCM) in SPM12. The data obtained indicate the possibility of consideration of the default network DMN (default mode network) at rest (resting state) on the basis of the mutual influence of its individual components: Parahippocampal Gyrus, Intraparietal Sulcus, the Anterior Cingulate Cortex, Medial Prefrontal Cortex, Posterior Cingulate Cortex. This work was supported by a grant from the Russian Science Foundation № 14-28-00234

 

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