ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

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Vlasenko D. V., Zaikin A. A., Zakharov D. G. Classification of brain activity using synolitic networks. Izvestiya VUZ. Applied Nonlinear Dynamics, 2023, vol. 31, iss. 5, pp. 661-669. DOI: 10.18500/0869-6632-003062, EDN: RUEQPM

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Classification of brain activity using synolitic networks

Vlasenko Daniil Vladimirovich, Saint Petersburg State University
Zaikin Aleksei Anatolevich, University College London
Zakharov Denis Gennadevich, National Research University "Higher School of Economics"

Because the brain is an extremely complex hypernet of interacting macroscopic subnetworks, full-scale analysis of brain activity is a daunting task. Nevertheless, this task can be greatly simplified by analysing the correspondence between various patterns of macroscopic brain activity, for example, through functional magnetic resonance imaging (fMRI) scans, and the performance of particular cognitive tasks or pathological states.

The purpose of this work is to present and validate a methodology of representing fMRI data in the form of graphs that effectively convey valuable insights into the interconnectedness of brain region activity for subsequent classification purposes.

Methods. This paper explores the application of synolitic networks in the analysis of brain activity. We propose a method for constructing a graph, the vertices of which reflect fMRI voxels’ values, and the edges and edge weights reflect the relationships between fMRI voxels.

Results and Conclusion. Based on the classification of fMRI data by graph properties, the effectiveness of the method in conveying important information for classification in the construction of graphs was shown.

This article is an output of a research project implemented as part of the Basic Research Program at the National Research University HSE using the supercomputer complex of the National Research University HSE
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