Evidence map›Paper›PMID 39211912›Full record

ArticleFrontiers in neuroinformatics2024

Investigating cortical complexity and connectivity in rats with schizophrenia.

Zongya Zhao, Yifan Feng, Menghan Wang, Jiarong Wei, Tao Tan, Ruijiao Li, Heshun Hu, Mengke Wang, Peiqi Chen, Xudong Gao and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Zongya ZhaoSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Yifan FengSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Menghan WangSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Jiarong WeiSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Tao TanSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Ruijiao LiSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Heshun HuSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Mengke WangSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Peiqi ChenSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Xudong GaoSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Yinping WeiSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Chang WangSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Zhixian GaoSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Wenshuai JiangSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Xuezhi ZhouSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Mingcai LiSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Chong WangSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Ting PangEngineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China.
Yi YuSchool of Medical Engineering, Xinxiang Medical University, Xinxiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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.

Indexed as

brain networkcomplexityconnectivityelectrocorticogramschizophrenia

Identifiers

PMID39211912
PMCPMC11358091

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