HighlightsWe have found that PD can be characterized by unique spatial microstate different from healthy controls, which may be related to the brain dysfunction in PD.The drug-free patients with PD show abnormal brain dynamics revealed by the regular changes of temporal microstate features in early PD and such temporal dynamics in microstates are correlated with motor function and cognition of the subjects.The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD.

Acupuncture, as an external stimulation, can produce clinical effects via the central nervous system. In order to investigate the modulatory efficacy of acupuncture on brain activity, multichannel EEG signals evoked by acupuncture at "Zusanli" acupoint were recorded from healthy humans in three states: pre-acupuncture, acupuncture, and post-acupuncture. Power spectral density is first used to analyze the EEG power change during acupuncture process. It is found that EEG power significantly increased in the delta and alpha bands under acupuncture and high power level remained in alpha band after acupuncture. Then, we calculated phase lag index to quantify the phase synchronization of pair-wise channels. In acupuncture state, delta and alpha bands exhibit significantly higher synchronization degree than pre-acupuncture state. Additionally, post-effect of acupuncture can be observed in alpha band as high synchronization degree remains in post-acupuncture state. Moreover, functional brain networks converted from synchronization matrix in each band are reconstructed. Acupuncture increases long-range connections between left and right hemispheres and changes the position of main nodes. Graph theory metrics are extracted to explore the change of functional connectivity in different states. The result shows the functional networks in delta and alpha bands are small world networks (SWN) and acupuncture improves the SWN efficiency of functional network.

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The complexity change of brain activity in Alzheimer's disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.