Skip to main content

ORIGINAL RESEARCH article

Front. Neuroinform.
Volume 18 - 2024 | doi: 10.3389/fninf.2024.1392271
This article is part of the Research Topic Time-Varying Neural Connectivity: New Concepts, Methods and Their Clinical Applications in Brain Disorders View all 4 articles

Investigating cortical complexity and connectivity in rats with schizophrenia

Provisionally accepted
Zongya Zhao Zongya Zhao Yifan Feng Yifan Feng Jiarong Wei Jiarong Wei Tao Tan Tao Tan Ruijiao Li Ruijiao Li Menghan Wang Menghan Wang Heshun Hu Heshun Hu Mengke Wang Mengke Wang Peiqi Chen Peiqi Chen Xudong Gao Xudong Gao Yinping Wei Yinping Wei Chang Wang Chang Wang Zhixian Gao Zhixian Gao Wenshuai Jiang Wenshuai Jiang Xuezhi Zhou Xuezhi Zhou Mingcai Li Mingcai Li Chong Wang Chong Wang Yi Yu Yi Yu *
  • Xinxiang Medical University, Xinxiang, Henan Province, China

The final, formatted version of the article will be published soon.

    The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. Methods: We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Results: Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. Conclusion: The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.

    Keywords: Schizophrenia, electrocorticogram, brain network, Complexity, connectivity 1.Introduction

    Received: 27 Feb 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Zhao, Feng, Wei, Tan, Li, Wang, Hu, Wang, Chen, Gao, Wei, Wang, Gao, Jiang, Zhou, Li, Wang and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yi Yu, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.