Beyond Spectral Measures: Multi-Scale Entropy Assessment of MEG Signals in Schizophrenia

Abstract

Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy (MSE) analysis to resting-state MEG data from 54 schizophrenia patients and 98 healthy controls. This method quantifies the temporal complexity of the signal across different time scales using the concept of sample entropy. Results show significantly higher sample entropy in schizophrenia patients, primarily in central, parietal, and occipital lobes, peaking at time scales equivalent to frequencies between 15 and 24 Hz. To disentangle the contributions of the amplitude and phase components, we applied the same analysis to a phase-shuffled surrogate signal. The analysis revealed that most differences originate from the amplitude component in the δ, α, and β power bands. While the phase component had a smaller magnitude, closer examination reveals clear spatial patterns and significant differences across specific brain regions. We assessed the potential of MSE as a schizophrenia biomarker by comparing its classification performance to conventional spectral analysis and a cognitive task (the n-back paradigm). The discriminative power of MSE and spectral features was similar, with a slight advantage for MSE features. The results of the n-back test were slightly below those obtained from MSE and spectral features.