Vlasenko, DV;
Zaikin, AA;
Zakharov, DG;
(2023)
Classification of brain activity using synolitic networks.
Izvestiya Vysshikh Uchebnykh Zavedeniy. Prikladnaya Nelineynaya Dinamika
, 31
(5)
pp. 661-669.
10.18500/0869-6632-003062.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Classification of brain activity using synolitic networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18500/0869-6632-003062 |
| Publisher version: | https://doi.org/10.18500/0869-6632-003062 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Cognitive processes, functional magnetic resonance imaging, synolitic networks, graphs, classification, machine learning. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Womens Cancer |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10180925 |
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