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.

Objective. Parkinson's disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagnosis of early PD has always been a difficult clinical problem due to the lack of reliable biomarkers. Electroencephalogram (EEG) is the most common clinical detection method, and studies have attempted to discover the EEG spectrum characteristics of early PD, but the reported conclusions are not uniform due to the heterogeneity of early PD patients. There is an urgent need for a more advanced algorithm to extract spectrum characteristics from EEG to satisfy the personalized requirements. Approach. The structured power spectral density with spatial distribution was used as the input of convolutional neural network (CNN). A visualization technique called gradient-weighted class activation mapping (Grad-CAM) was used to extract the optimal frequency bands for identifying early PD. Based on the model visualization, we proposed a novel quantitative index of spectral characteristics, spatial-mapping relative power (SRP), to detect personalized abnormalities in the spatial spectral characteristics of EEG in early PD. Main results. We demonstrated the feasibility of applying CNN to identify the patients with early PD with an accuracy of 99.87% ± 0.03%. The models indicated the characteristic frequency bands (high-delta (3.5-4.5 Hz) and low-alpha (7.5-11 Hz) frequency bands) that are used to identify the early PD. The SRP of these two characteristic bands in early PD patients was significantly higher than that in the control group, and the abnormalities were consistent at the group and individual levels. Significance. This study provides a novel personalized detection algorithm based on deep learning to reveal the optimal frequency bands for identifying early PD and obtain the spatial frequency characteristics of early PD. The findings of this study will provide an effective reference for the auxiliary diagnosis of early PD in clinical practice.

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Aberrant dynamic switches between internal brain states are believed to underlie motor dysfunction in Parkinson’s disease. Deep brain stimulation of the subthalamic nucleus is a well-established treatment for the motor symptoms of Parkinson’s disease, yet it remains poorly understood how subthalamic stimulation modulates the whole-brain intrinsic motor network state dynamics. To investigate this, we acquired resting-state functional magnetic resonance imaging time-series data from 27 medication-free patients with Parkinson’s disease (mean age: 64.8 years, standard deviation: 7.6) who had deep brain stimulation electrodes implanted in the subthalamic nucleus, in both on and off stimulation states. Sixteen matched healthy individuals were included as a control group. We adopted a powerful data-driven modeling approach, known as a hidden Markov model, to disclose the emergence of recurring activation patterns of interacting motor regions (whole-brain intrinsic motor network states) via the blood oxygen-level dependent signal detected in the resting-state functional magnetic resonance imaging time-series data from all participants. The estimated hidden Markov model disclosed the dynamics of distinct whole-brain motor network states, including frequency of occurrence, state duration, fractional coverage, and their transition probabilities. Notably, the data-driven decoding of whole-brain intrinsic motor network states revealed that subthalamic stimulation reshaped functional network expression and stabilized state transitions. Moreover, subthalamic stimulation improved motor symptoms by modulating key trajectories of state transition within whole-brain intrinsic motor network states. This modulation mechanism of subthalamic stimulation was manifested in three significant effects: recovery, relieving, and remodeling effects. Significantly, recovery effects correlated with improvements in tremor and posture symptoms induced by subthalamic stimulation (P < 0.05). Furthermore, subthalamic stimulation was found to restore a relatively low level of fluctuation of functional connectivity in all motor regions to a level closer to that of healthy participants. Also, changes in the fluctuation of functional connectivity between motor regions were associated with improvements in tremor and gait symptoms (P < 0.05). These findings fill a gap in our knowledge of the role of subthalamic stimulation at the level of neural activity, revealing the regulatory effects of subthalamic stimulation on whole-brain inherent motor network states in Parkinson’s disease. Our results provide mechanistic insight and explanation for how subthalamic stimulation modulates motor symptoms in Parkinson's disease.