PDF(Pubmed)
Abstract：
Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy analysis to resting-state Magnetoencephalography 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 multi-scale entropy 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 multi-scale entropy and spectral features was similar, with a slight advantage for multi-scale entropy features. The results of the n-back test were slightly below those obtained from multi-scale entropy and spectral features.
摘要：
精神分裂症是一种严重的认知和情感障碍，影响人类的基本功能。在这项研究中,我们将多尺度熵分析应用于54例精神分裂症患者和98例健康对照的静息状态脑磁图数据.该方法使用样本熵的概念来量化信号在不同时间尺度上的时间复杂度。结果显示精神分裂症患者的样本熵明显更高，主要在中央,顶叶,和枕叶，在相当于15和24Hz之间的频率的时间尺度上达到峰值。为了解开振幅和相位分量的贡献，我们将相同的分析应用于相位混洗的替代信号。分析表明，大多数差异源于δ中的振幅分量，α,和β功率带。虽然相位分量的幅度较小，更仔细的检查显示出清晰的空间模式和特定大脑区域的显著差异。我们通过将其分类性能与常规频谱分析和认知任务（n-back范式）进行比较，评估了多尺度熵作为精神分裂症生物标志物的潜力。多尺度熵和谱特征的判别能力相似，对于多尺度熵特征略有优势。n-back测试的结果略低于从多尺度熵和光谱特征获得的结果。
