Geodesic
Classification of brain activity using synolitic networks
Izvestiya VUZ. Applied Nonlinear Dynamics, Tome 31 (2023) no. 5, pp. 661-669.

Voir la notice de l'article provenant de la source Math-Net.Ru

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.
Keywords: cognitive processes, functional magnetic resonance imaging, synolitic networks, graphs, classification, machine learning. 
@article{IVP_2023_31_5_a11,
     author = {D. V. Vlasenko and A. A. Zaikin and D. G. Zakharov},
     title = {Classification of brain activity using synolitic networks},
     journal = {Izvestiya VUZ. Applied Nonlinear Dynamics},
     pages = {661--669},
     publisher = {mathdoc},
     volume = {31},
     number = {5},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a11/}
}
TY  - JOUR
AU  - D. V. Vlasenko
AU  - A. A. Zaikin
AU  - D. G. Zakharov
TI  - Classification of brain activity using synolitic networks
JO  - Izvestiya VUZ. Applied Nonlinear Dynamics
PY  - 2023
SP  - 661
EP  - 669
VL  - 31
IS  - 5
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a11/
LA  - ru
ID  - IVP_2023_31_5_a11
ER  - 
%0 Journal Article
%A D. V. Vlasenko
%A A. A. Zaikin
%A D. G. Zakharov
%T Classification of brain activity using synolitic networks
%J Izvestiya VUZ. Applied Nonlinear Dynamics
%D 2023
%P 661-669
%V 31
%N 5
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a11/
%G ru
%F IVP_2023_31_5_a11
D. V. Vlasenko; A. A. Zaikin; D. G. Zakharov. Classification of brain activity using synolitic networks. Izvestiya VUZ. Applied Nonlinear Dynamics, Tome 31 (2023) no. 5, pp. 661-669. http://geodesic.mathdoc.fr/item/IVP_2023_31_5_a11/

[1] Ogawa S., Lee T. M., Kay A. R., Tank D. W., “Brain magnetic resonance imaging with contrast dependent on blood oxygenation”, Proceedings of the National Academy of Sciences of the United States of America, 87:24 (1990), 9868–9872 | DOI

[2] Singleton M. J., “Functional magnetic resonance imaging”, Yale J. Biol. Med, 82:4 (2009), 233

[3] Gao J. S., Huth A. G., Lescroart M. D., Gallant J. L., “Pycortex: an interactive surface visualizer for fMRI”, Frontiers in Neuroinformatics, 9 (2015), 23 | DOI

[4] Li X., Dvornek N. C., Zhou Y., Zhuang J., Ventola P., Duncan J. S., “Graph neural network for interpreting task-fMRI biomarkers”, Medical Image Computing and Computer Assisted Intervention MICCAI 2019, Lecture Notes in Computer Science, 11678, eds. Shen D., Liu T., Peters T. M., Staib L. H., Essert C., Zhou S., Yap P.-T., Khan A., Springer, Cham, 2019, 485–493 | DOI

[5] Saueressig C., Berkley A., Munbodh R., Singh R., “A joint graph and image convolution network for automatic brain tumor segmentation”, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Computer Science, 12962, eds. Crimi A., Bakas S., Springer, Cham, 2022. P. 356–365. | DOI

[6] Anderson A., Cohen M. S., “Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial”, Frontiers in Human Neuroscience, 7 (2013), 520 | DOI

[7] Kim B.-H., Ye J. C., “Understanding graph isomorphism network for rs-fMRI functional connectivity analysis”, Frontiers in Neuroscience, 14 (2020), 630 | DOI

[8] Nazarenko T., Whitwell H. J., Blyuss O., Zaikin A., “Parenclitic and synolytic networks revisited”, Frontiers in Genetics, 12 (2021), 733783 | DOI

[9] Horikawa T., Kamitani Y., Generic Object Decoding (fMRI on ImageNet) | DOI

[10] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay É., “Scikit-learn: Machine learning in Python”, Journal of Machine Learning Research, 12:85 (2011), 2825–2830