V.L. Ushakov - Ph.D. (Biol.), Head of Laboratory of Cognitive Brain Function Neurovisualisation of 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 of 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 of Kurchatov-s complex of NBICS-technologies of National Research Centre «Kurchatov institute» (Moscow). E-mail: ptica89@bk.ru S.I. Kartashov - Research-engineer, Laboratory of Cognitive Brain Function Neurovisualisation of 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 of Kurchatov-s Complex of NBICS-technologies, National Research Centre «Kurchatov institute» (Moscow). E-mail: tiuq@yandex.ru B.M. Velichkovsky - Dr.Sc. (Psychol.), Head of Department of Neurocognitive and Socio-Humanitarian sciences of Kurchatov-s complex of NBICS-technologies of National Research Centre «Kurchatov institute» (Moscow). E-mail: boris.velichkovsky@tu-dresden.de

The aim of this work is an extension and validation of advanced methods for analyzing large scale networks dynamics at resting state, construction of a structural, functional, and effective connectome of human brain. Experimental data were obtained by fMRI and DTI methods using 3.0 T scanner (Siemens Magnetom VERIO). 30 healthy right handed volunteers (20 males and 10 females at the age of 21-35 y.o.) participated in the study. Overall procedure was approved by the Ethics Committee at IHNA RAS. MRI anatomical data were obtained using standard 3D sequences with 1*1*1 mm voxel size (VS). Functional data were registered with echo-planar sequences T2*EPI (TR=2 s, TE=30 ms) at resolution of VS=3*3*3 mm and consisted of 30 slices (S). At each resting state experiment, we recorded 1000 sets of functional volumes, covering all brain. The DTI data were registered with the following parameters: number of directions 256, TR=4300 s, TE=100 ms, S=25, VS=1.8*1.8*4 mm. Visualization of active neuronal networks of human brain at resting state was implemented using Independent component analysis method (ICA, Infomax method) and statistical parametric mapping (SPM), using t-test (t > 3, р < 0.001, N = 21) with FDR correction. Filtration of networks representing artifacts of cerebrospinal fluid pulsations, motor movements or gray matter was performed using visual assessment of group t-maps. Number of components was determined using MDL-test (Minimum Description Length). Temporal change of BOLD-signal of ICA was estimated as a dynamics of selected network, which was compared with hemodynamic function model (HRF) using correlation coefficient at p < 0.01, df = 58, r = 0.32. The internetwork connectivity was estimated using graph theory: the construction of efficient and directed connectome. Using these methods described below, we were able to describe a relatively small number (up to 7) of dynamically changing networks states. We analyzed the statistics of their occurrence, probability of change and stability. Our results confirm the earlier results of other authors. At the same time, significant differences were found. They were connected with the dynamics of activation and with the localization of the centers of connective systems clusters: visual, auditory, cerebellar, that of cognitive control and default mode network. This work was supported by a grant from the Russian Science Foundation № 14-28-00234.

 

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