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Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms.
Dengxuan Bai1, Wenpo Yao2, Shuwang Wang3
1School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Entropy (Basel, Switzerland)
|March 25, 2022
Summary
Magnetoencephalogram (MEG) signals show significantly higher complexity in schizophrenia patients compared to healthy individuals, particularly in frontal and occipital brain regions. This complexity difference may lead to new biomarkers for schizophrenia.
Area of Science:
- Neuroscience
- Biomarkers
- Computational Psychiatry
Background:
- Schizophrenia is a neuropsychiatric disorder impacting brain activity dynamics.
- Understanding alterations in brain signal complexity is crucial for diagnosis.
Purpose of the Study:
- To investigate the complexity of magnetoencephalograms (MEG) in schizophrenia patients.
- To identify potential magnetoencephalographic biomarkers for schizophrenia.
Main Methods:
- Utilized a multiscale method combined with weighted permutation entropy.
- Analyzed MEG signals from 19 schizophrenia patients and 16 healthy controls.
- Examined signal complexity across different brain regions.
Main Results:
- Schizophrenia patients exhibited significantly higher MEG signal complexity at scales > 42 (p<0.004).
- Complexity differences were most pronounced in frontal and occipital areas (p<0.001).
- Healthy individuals showed a wider dynamic range of MEG complexity.
Conclusions:
- Multiscale weighted permutation entropy reliably quantifies MEG complexity in schizophrenia.
- Findings suggest potential for MEG-based biomarkers in schizophrenia diagnosis and monitoring.
