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

For citation:

Vlasenko D. V., Ushakov V. G., Zaikin A. A., Zakharov D. G. Comparison of ensemble and correlation graphs in the task of classifying brain states based on fMRI data. Izvestiya VUZ. Applied Nonlinear Dynamics, 2025, vol. 33, iss. 4, pp. 557-566. DOI: 10.18500/0869-6632-003164, EDN: PPZDBV

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Language: 
Russian
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-003164
EDN: 
PPZDBV

Comparison of ensemble and correlation graphs in the task of classifying brain states based on fMRI data

Autors: 
Vlasenko Daniil Vladimirovich, National Research University "Higher School of Economics"
Ushakov Vadim Gennadevich, Lomonosov Moscow State University
Zaikin Aleksei Anatolevich, University College London
Zakharov Denis Gennadevich, National Research University "Higher School of Economics"
Abstract: 

The study of functional brain networks that support cognitive processes is one of the central goals of modern neuroscience. Functional magnetic resonance imaging (fMRI) is widely used to obtain data on brain activity. However, the high dimensionality and dynamic nature of fMRI data makes their processing challenging. Network-based methods of data representation offer a promising approach to describe the brain as a network, where nodes correspond to brain regions and edges correspond to functional connections between them. This
allows us to further explore the topology of brain networks and their role in cognitive states. The purpose of this paper is to compare ensemble and correlation graphs in a brain state classification task based on functional magnetic resonance imaging (fMRI) data.

Methods. This paper presents a novel method for representing fMRI data in graph form based on ensemble learning. To demonstrate the effectiveness of the data representation method, we compared it with correlated graphs by applying a graph neural network to classify brain states.

Results and Conclusion. Our results showed that ensemble graphs lead to significantly more accurate and stable classification. The better classification performance suggests that using this method we are more efficient in identifying functional connections between brain regions during cognitive tasks.
 

Key words: 
когнитивные процессы
функциональная магнитно-резонансная томография
ансамблевые графы
классификация
машинное обучение
Acknowledgments: 
This research was supported by RSF (project No. 24-68-00030) and in part through computational resources of HPC facilities at HSE University.
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Received: 
12.10.2024
Accepted: 
25.01.2025
Available online: 
03.02.2025
Published: 
31.07.2025
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