Feature Extraction and Analysis of Multilayer Higher-Order Brain Functional Networks in Patients with Schizophrenia

Jianghao Zhao; Guimei Yin

doi:10.54097/4f7fje07

Authors

Jianghao Zhao
Guimei Yin

DOI:

https://doi.org/10.54097/4f7fje07

Keywords:

Multilayer network, Persistent homology, EEG, Schizophrenia

Abstract

Objectives: A schizophrenia patient model based on Persistent homology and multilayer brain networks combines full-frequency band information to mine and categorize key brain regions, providing a decision-making basis for patient treatment. Methods: Using the information of 198 subjects from Huilongguan Hospital in Beijing who met the requirements of this study, the network features based on the constructed multilayer brain network were mined by Persistent homophony and traditional methods of mining multi-frequency band information of subjects, and finally analyzed by using a deep learning model. Results: After the lesion, the brain area changes were mainly in the frontal central cortex and left parietal lobe, leading to the alteration of the patient's perceptual and cognitive abilities, and the classification training using a deep learning model resulted in a classification accuracy of 92.3%. Conclusion: By constructing a multilayer brain network model and using a persistent homology approach, cross-frequency band information can be effectively utilized, and higher-order information can be exploited to improve disease diagnosis.

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References

[1] Buzsáki, G Logothetis, N Singer, W. Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron, 2013, 80(3): 751-764.

[2] Xing, X-X. Globally aging cortical spontaneous activity revealed by multiple metrics and frequency bands using resting-state functional MRI. Frontiers in Aging Neuroscience, 2021, 13803436.

[3] Lynall, M-E Bassett, D S Kerwin, R, et al. Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience, 2010, 30(28): 9477-9487.

[4] Fan, F Tan, S Huang, J, et al. Functional disconnection between subsystems of the default mode network in schizophrenia. Psychological medicine, 2022, 52(12): 2270-2280.

[5] Battiston, F Nicosia, V Latora, V. Structural measures for multiplex networks. Phys. Rev. E, 2014, 89(3): 032804.

[6] D'Andrea, A Chella, F Marshall, T R, et al. Alpha and alpha-beta phase synchronization mediate the recruitment of the visuospatial attention network through the Superior Longitudinal Fasciculus. NeuroImage, 2019, 188722-732.

[7] Yin, G Chang, Y Zhao, Y, et al. Automatic recognition of schizophrenia from brain-network features using graph convolutional neural network. Asian Journal of Psychiatry, 2023, 87103687.

[8] Tandon, R Nasrallah, H Akbarian, S, et al. The schizophrenia syndrome, circa 2024: What we know and how that informs its nature. Schizophrenia research, 2024, 2641-28.

[9] Xu, F Zhang, J Jin, M, et al. Chimera states and synchronization behavior in multilayer memristive neural networks. Nonlinear dynamics, 2018, 94(2): 775-783.

[10] Ke, M Wang, C Liu, G. Multilayer brain network modeling and dynamic analysis of juvenile myoclonic epilepsy. Frontiers in Behavioral Neuroscience, 2023, 171123534.

[11] Kim, J Lee, D A Lee, H J, et al. Multilayer network changes in patients with migraine. Brain and Behavior, 2023, 13(12): e3316.

[12] Micheloyannis, S Pachou, E Stam, C J, et al. Small-world networks and disturbed functional connectivity in schizophrenia. Schizophrenia research, 2006, 87(1-3): 60-66.

[13] Kindelan, R Frías, J Cerda, M, et al. A topological data analysis based classifier. Advances in Data Analysis and Classification, 2023, 1-46.

[14] Taiwo, F M Islambekov, U Akcora, C G. Explaining the Power of Topological Data Analysis in Graph Machine Learning. arXiv preprint arXiv:2401.04250, 2024.

[15] Wang, Y Ombao, H Chung, M K. Statistical persistent homology of brain signals. In Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019; pp. 1125-1129.

[16] Yip, J H Biagetti, M Cole, A, et al. Cosmology with persistent homology: a Fisher forecast. Journal of Cosmology and Astroparticle Physics, 2024, 2024(09): 034.

[17] Peng, Y Wang, H Sonka, M, et al. PHG-Net: Persistent homology guided medical image classification. In Proceedings of the Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024; pp. 7583-7592.

[18] Yu, B Xie, H Xu, Z. PN-GCN: Positive-negative graph convolution neural network in information system to classification. Information Sciences, 2023, 632411-423.

[19] Kong, H Lou, X Li, Z. Emotional EEG recognition and classification based on GCN combined with LSTM. In Proceedings of the 2024 IEEE International Conference on Medical Artificial Intelligence (MedAI), 2024; pp. 294-301.

[20] Zhang, Y Zhang, Y Yan, D, et al. NIE-GCN: Neighbor Item Embedding-Aware Graph Convolutional Network for Recommendation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024.

[21] Liang, J Wang, Z Han, J, et al. EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network. Frontiers in Neuroscience, 2024, 181421010.

[22] Cao, H Wei, X Zhang, W, et al. Cerebellar functional dysconnectivity in drug-naïve patients with first-episode schizophrenia. Schizophrenia bulletin, 2023, 49(2): 417-427.

[23] Yu, X-M Qiu, L-L Huang, H-X, et al. Comparison of resting-state spontaneous brain activity between treatment-naive schizophrenia and obsessive-compulsive disorder. BMC psychiatry, 2021, 211-14.

[24] Howes, O D Cummings, C Chapman, G E, et al. Neuroimaging in schizophrenia: an overview of findings and their implications for synaptic changes. Neuropsychopharmacology, 2023, 48(1): 151-167.

[25] Chatterjee, I Hilal, B. Investigating the association between symptoms and functional activity in brain regions in schizophrenia: A cross-sectional fmri-based neuroimaging study. Psychiatry Research: Neuroimaging, 2024, 344111870.