Disorders of Consciousness are divided into two major categories such as vegetative and minimally conscious states. Objective measures that allow correct identification of patients with vegetative and minimally conscious state are needed. EEG microstate analysis is a promising approach that we believe has the potential to be effective in examining the resting state activities of the brain in different stages of consciousness by allowing the proper identification of vegetative and minimally conscious patients. As a result, we try to identify clinical evaluation scales and microstate characteristics with resting state EEGs from individuals with disorders of consciousness. Our prospective observational study included 28 individuals with a disorder of consciousness. Control group included 18 healthy subjects with proper EEG data. We made clinical evaluations using patient behavior scales. We also analyzed the EEGs using microstate analysis. In our study, microstate D coverage differed substantially between vegetative and minimally conscious state patients. Also, there was a strong connection between microstate D characteristics and clinical scale scores. Consequently, we have demonstrated that the most accurate parameter for representing consciousness level is microstate D. Microstate analysis appears to be a strong option for future use in the diagnosis, follow-up, and treatment response of patients with Disorders of Consciousness.
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Brain Res Bull
January 2025
Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, The Second Affiliated Hospital of Air Force Medical University, No.569 Xinsi Road, Xi'an, 710038, Shaanxi, China. Electronic address:
Introduction: Cognitive fatigue is mainly caused by enduring mental stress or monotonous work, impairing cognitive and physical performance. Natural scene exposure is a promising intervention for relieving cognitive fatigue, but the efficacy of virtual reality (VR) simulated natural scene exposure is unclear. We aimed to investigate the effect of VR natural scene on cognitive fatigue and further explored its underlying neurophysiological alterations with electroencephalogram (EEG) microstates analysis.
View Article and Find Full Text PDFBrain Sci
December 2024
Department of Automation, Tsinghua University, Beijing 100084, China.
Background: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-state EEG. The task-related EEG was extensively analyzed in the field of brain-computer interfaces (BCIs); however, its primary objective is classification rather than segmentation.
View Article and Find Full Text PDFHeliyon
January 2025
Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain.
Resting state electroencephalography (EEG) has proved useful in studying electrophysiological changes in neurodegenerative diseases. In many neuropathologies, microstate analysis of the eyes-closed (EC) scalp EEG is a robust and highly reproducible technique for assessing topological changes with high temporal resolution. However, scalp EEG microstate maps tend to underestimate the non-occipital or non-alpha-band networks, which can also be used to detect neuropathological changes.
View Article and Find Full Text PDFBrain Topogr
January 2025
School of Psychology, Guizhou Normal University, Guiyang, Guizhou, China.
Relational integration is a key subcomponent of working memory and a strong predictor of fluid intelligence. Both relational integration and fluid intelligence share a common neural foundation, particularly involving the frontoparietal network. This study utilized a randomized controlled experiment to examine the effect of relational integration training on brain networks using electroencephalogram (EEG) and microstate analysis.
View Article and Find Full Text PDFInt J Psychophysiol
January 2025
Department of Applied Psychology, College of Public Administration, Guangdong University of Foreign Studies, Guangzhou, China. Electronic address:
Investigating the neurophysiological indicators of behavioral inhibition is crucial; however, despite numerous studies on the relationship between behavioral inhibition and resting-state electroencephalography (rs-EEG), the findings have yielded inconsistent results. Furthermore, these investigations primarily focused on reactive inhibition while neglecting intentional inhibition. Therefore, this study aimed to reassess the correlation between reactive inhibition and rs-EEG metrics while also exploring the association between intentional inhibition and rs-EEG.
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