Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 3; p. 314
• Main Authors: Bai, Dengxuan, Yao, Wenpo, Wang, Shuwang, Wang, Jun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.02.2022; MDPI
• Subjects: Algorithms; Biomarkers; Brain; Complexity; Electroencephalography; Entropy; Investigations; Magnetic resonance imaging; magnetoencephalogram; Medical imaging; multiscale; Multiscale analysis; Nonlinear dynamics; Permutations; Schizophrenia; Time series; Tomography; weighted permutation entropy; magnetoencephalogram; schizophrenia; weighted permutation entropy; multiscale
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24030314

Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls (p<0.004). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas (p<0.001), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.