Abstract

BackgroundThis study aimed to describe a synchronized intracranial electroencephalogram (EEG) recording and motion capture system, which was designed to explore the neural dynamics during walking of Parkinson’s disease (PD) patients with freezing of gait (FOG). Preliminary analysis was performed to test the reliability of this system.MethodsA total of 8 patients were enrolled in the study. All patients underwent bilateral STN-DBS surgery and were implanted with a right subdural electrode covering premotor and motor area. Synchronized electrophysiological and gait data were collected using the Nihon Kohden EEG amplifier and Codamotion system when subjects performed the Timed Up and Go (TUG) test. To verify the reliability of the acquisition system and data quality, we calculated and compared the FOG index between freezing and non-freezing periods during walking. For electrophysiological data, we first manually reviewed the scaled (five levels) quality during waking. Spectra comprising broadband electrocorticography (ECoG) and local field potential (LFP) were also compared between the FOG and non-FOG states. Lastly, connectivity analysis using coherence between cortical and STN electrodes were conducted. In addition, we also use machine learning approaches to classified FOG and non-FOG.ResultsA total of 8 patients completed 41 walking tests, 30 of which had frozen episodes, and 21 of the 30 raw data were level 1 or 2 in quality (70%). The mean ± SD walking time for the TUG test was 85.94 ± 47.68 s (range: 38 to 190.14 s); the mean ± SD freezing duration was 12.25 ± 7.35 s (range: 1.71 to 27.50 s). The FOG index significantly increased during the manually labeled FOG period (P < 0.05). The beta power of STN LFP in the FOG period was significantly higher than that in the non-FOG period (P < 0.05), while the band power of ECoG did not exhibit a significant difference between walking states. The coherence between the ECoG and STN LFP was significantly greater in high beta and gamma bands during the FOG period compared with the shuffled surrogates (P < 0.05). Lastly, STN-LFP band power features showed above-chance performance (p < 0.01, permutation test) in identifying FOG epochs.ConclusionIn this study, we established and verified the synchronized ECoG/LFP and gait recording system in PD patients with FOG. Further neural substrates underlying FOG could be explored using the current system.

Highlights

  • Parkinson’s disease (PD) is a degenerative disease of the nervous system that occurs in older people

  • It is believed that the abnormal function of the basal ganglia–cortical loop may play an important role in the occurrence and development of Freezing of gait (FOG) (Bartels and Leenders, 2008; Snijders et al, 2016)

  • The results reported the characteristics of EEG dynamics and the coupling of different cortical locations of PD-FOG

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Summary

Introduction

Parkinson’s disease (PD) is a degenerative disease of the nervous system that occurs in older people. As PD progresses, the incidence of FOG gradually increases, and the incidence over 10 years is as high as 58% (Giladi et al, 2001). With the development of multi-modal imaging technology and electrophysiological technology, more attention has been paid to the neural mechanisms of FOG. Lv et al (2021) examined the association between basal ganglia perivascular spaces and FOG using high resolution 7Tmagnetic resonance imaging (MRI), and found that basal ganglia perivascular spaces were significantly greater during frozen episodes. Neuroimaging studies provide adequate coverage and spatial resolution, they do not reflect the dynamic response to FOG events. This study aimed to describe a synchronized intracranial electroencephalogram (EEG) recording and motion capture system, which was designed to explore the neural dynamics during walking of Parkinson’s disease (PD) patients with freezing of gait (FOG). Preliminary analysis was performed to test the reliability of this system

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