Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson's disease?

Costa, Thaísa Dias de Carvalho; Machado, Camila Beatriz da Silva; Segundo, Robson Prazeres Lemos; Silva, Joyce Poláine dos Santos; Silva, Ana Catarine Tavares; Andrade, Rafael de Souza; Rosa, Marine Raquel Diniz da; Smaili, Suhaila Mahmoud; Morya, Edgard; Clementino, Adriana Carla Costa Ribeiro; Lindquist, Ana Raquel Rodrigues; Andrade, Suellen Marinho; Machado, Daniel Gomes da Silva

doi:10.1016/j.neucli.2022.102839

Cited by 2 publications

(1 citation statement)

References 60 publications

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“…These tools are designed to inform the development of personalized treatment strategies. The growing interest in “walking,” “balance,” and “nonmotor symptoms ( Conti et al, 2023 )” reflects a broader approach to understanding PD, emphasizing the need to address the multifaceted nature of the disease that affects both motor and cognitive functions ( Bosch et al, 2021 ; Costa et al, 2023 ; Guo et al, 2024 ). As researchers continue to investigate these topics, integrating advanced technologies and holistic approaches promises to drive innovations in the management of PD, potentially leading to earlier detection, more effective treatments, and better patient outcomes.…”

Section: Discussionmentioning

confidence: 99%

Bibliometric analysis of electroencephalogram research in Parkinson’s disease from 2004 to 2023

Liao,

Gao,

Qian

et al. 2024

Front. Neurosci.

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BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder affecting millions globally. It encompasses both motor and non-motor symptoms, with a notable impact on patients’ quality of life. Electroencephalogram (EEG) is a non-invasive tool that is increasingly utilized to investigate neural mechanisms in PD, identify early diagnostic markers, and assess therapeutic responses.MethodsThe data were sourced from the Science Citation Index Expanded within the Web of Science Core Collection database, focusing on publications related to EEG research in PD from 2004 to 2023. A comprehensive bibliometric analysis was conducted using CiteSpace and VOSviewer software. The analysis began with an evaluation of the selected publications, identifying leading countries, institutions, authors, and journals, as well as co-cited references, to summarize the current state of EEG research in PD. Keywords are employed to identify research topics that are currently of interest in this field through the analysis of high-frequency keyword co-occurrence and cluster analysis. Finally, burst keywords were identified to uncover emerging trends and research frontiers in the field, highlighting shifts in interest and identifying future research directions.ResultsA total of 1,559 publications on EEG research in PD were identified. The United States, Germany, and England have made notable contributions to the field. The University of London is the leading institution in terms of publication output, with the University of California closely following. The most prolific authors are Brown P, Fuhr P, and Stam C In terms of total citations and per-article citations, Stam C has the highest number of citations, while Brown P has the highest H-index. In terms of the total number of publications, Clinical Neurophysiology is the leading journal, while Brain is the most highly cited. The most frequently cited articles pertain to software toolboxes for EEG analysis, neural oscillations, and PD pathophysiology. Through analyzing the keywords, four research hotspots were identified: research on the neural oscillations and connectivity, research on the innovations in EEG Analysis, impact of therapies on EEG, and research on cognitive and emotional assessments.ConclusionThis bibliometric analysis demonstrates a growing global interest in EEG research in PD. The investigation of neural oscillations and connectivity remains a primary focus of research. The application of machine learning, deep learning, and task analysis techniques offers promising avenues for future research in EEG and PD, suggesting the potential for advancements in this field. This study offers valuable insights into the major research trends, influential contributors, and evolving themes in this field, providing a roadmap for future exploration.

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Section: Discussionmentioning

confidence: 99%

Bibliometric analysis of electroencephalogram research in Parkinson’s disease from 2004 to 2023

Liao,

Gao,

Qian

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

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An interpretable model based on graph learning for diagnosis of Parkinson’s disease with voice-related EEG

Zhao,

Dai,

et al. 2024

npj Digit. Med.

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Parkinson’s disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis, but their practicality is constrained by the interpretable features and the stochastic nature of resting-state EEG. The present study proposes a novel and interpretable deep learning model, graph signal processing-graph convolutional networks (GSP-GCNs), using event-related EEG data obtained from a specific task involving vocal pitch regulation for PD diagnosis. By incorporating both local and global information from single-hop and multi-hop networks, our proposed GSP-GCNs models achieved an averaged classification accuracy of 90.2%, exhibiting a significant improvement of 9.5% over other deep learning models. Moreover, the interpretability analysis revealed discriminative distributions of large-scale EEG networks and topographic map of microstate MS5 learned by our models, primarily located in the left ventral premotor cortex, superior temporal gyrus, and Broca’s area that are implicated in PD-related speech disorders, reflecting our GSP-GCN models’ ability to provide interpretable insights identifying distinctive EEG biomarkers from large-scale networks. These findings demonstrate the potential of interpretable deep learning models coupled with voice-related EEG signals for distinguishing PD patients from healthy controls with accuracy and elucidating the underlying neurobiological mechanisms.

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Are the EEG microstates correlated with motor and non-motor parameters in patients with Parkinson's disease?

Cited by 2 publications

References 60 publications

Bibliometric analysis of electroencephalogram research in Parkinson’s disease from 2004 to 2023

Bibliometric analysis of electroencephalogram research in Parkinson’s disease from 2004 to 2023

An interpretable model based on graph learning for diagnosis of Parkinson’s disease with voice-related EEG

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