Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

Liu, Mingrui; Liu, Baohu; Ye, Zelin; Wu, Daming

doi:10.3389/fnins.2023.1128851

Cited by 4 publications

(2 citation statements)

References 67 publications

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“…Although findings in this area are still inconclusive (C. Wang et al, 2022), an association between slow-wave connectivity and cognitive performance has been established (Laptinskaya et al, 2020). Interestingly, while it is common to come across studies that compare cognitively impaired patients with healthy controls, demonstrating differences in delta-band power and other bands (Torres-Simón et al, 2022), the focus has understandably shifted towards examining neural networks and connectivity measures (Liu et al, 2023) as brain connectivity is perceived as more interpretable in a context of the brain as a complex neural network. In our study, we did not find evidence for power differences in any frequency band between socially vulnerable individuals and controls.…”

Section: Discussionmentioning

confidence: 99%

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Lanfranco,

Santos Sousa,

Wessel

et al. 2023

Preprint

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Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.

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Section: Discussionmentioning

confidence: 99%

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Lanfranco,

Santos Sousa,

Wessel

et al. 2023

Preprint

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“…As WoSCC provides the most extensive information for bibliometric analysis compared to other multidisciplinary electronic databases such as Google Scholar and Scopus@, WoSCC was chosen as the only database in this study ( 36 ). Moreover, WoSCC has been successfully used by researchers as a data source to conduct bibliometric analysis in the field of Tai Chi for Health ( 37 ), electroencephalogram research in MCI ( 38 ), and other fields related to BME for MCI.…”

Section: Methodsmentioning

confidence: 99%

Knowledge structure and future research trends of body–mind exercise for mild cognitive impairment: a bibliometric analysis

Zhang,

Yang,

Fan

2024

Front. Neurol.

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BackgroundMild cognitive impairment (MCI) is a common neurodegenerative disorder that poses a risk of progression to dementia. There is growing research interest in body–mind exercise (BME) for patients with MCI. While we have observed a rapid growth in interest in BME for MCI over the past 10 years, no bibliometric analysis has investigated the knowledge structure and research trends in this field. Consequently, the objective of this research is to conduct a bibliometric analysis of global publications of BME for MCI from 2013 to 2022.MethodsA total of 242 publications in the field of BME for MCI were retrieved from the Web of Science Core Collection. Bibliometric analysis, including performance analysis, science mapping, and visualization, was performed using CiteSpace, VOSviewer, and Microsoft Excel.ResultsPublications and citations in the field of BME for MCI have shown a rapidly increasing trend over the last decade. Geriatrics & Gerontology, and Neurosciences were the most frequently involved research categories. China (78 documents) and the USA (75 documents) contributed to the largest number of publications and had the strongest international collaborative networks. Fujian University of Traditional Chinese Medicine contributed to the largest number of publications (12 documents), and Chen, L of this institution was the most prolific author (12 documents). Frontiers in Aging Neuroscience (16 documents), and JOURNAL OF ALZHEIMER’S DISEASE (12 documents) were the most prolific journals. Tai Chi and Baduanjin, as specific types of BME, were the hotspots of research in this field, while evidence synthesis and guidelines might be future research trends.ConclusionIn the last decade, there has been a rapid growth in scientific activities in the field of BME for MCI. The results of this study provide researchers and other stakeholders with knowledge structure, hotspots, and future research trends in this field.

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Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning – A Bibliometric Analysis

Chaki,

Deshpande

2024

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Background and Objective: Brain disorders are one of the major global mortality issues, and their early detection is crucial for healing. Machine learning, specifically deep learning, is a technology that is increasingly being used to detect and diagnose brain disorders. Our objective is to provide a quantitative bibliometric analysis of the field to inform researchers about trends that can inform their Research directions in the future. Methods: We carried out a bibliometric analysis to create an overview of brain disorder detection and diagnosis using machine learning and deep learning. Our bibliometric analysis includes 1550 articles gathered from the Scopus database on automated brain disorder detection and diagnosis using machine learning and deep learning published from 2015 to May 2023. A thorough bibliometric análisis is carried out with the help of Biblioshiny and the VOSviewer platform. Citation analysis and various measures of collaboration are analyzed in the study. Results: According to a study, maximum research is reported in 2022, with a consistent rise from preceding years. The majority of the authors referenced have concentrated on multiclass classification and innovative convolutional neural network models that are effective in this field. A keyword analysis revealed that among the several brain disorder types, Alzheimer's, autism, and Parkinson's disease had received the greatest attention. In terms of both authors and institutes, the USA, China, and India are among the most collaborating countries. We built a future research agenda based on our findings to help progress research on machine learning and deep learning for brain disorder detection and diagnosis. Conclusion: In summary, our quantitative bibliometric analysis provides useful insights about trends in the field and points them to potential directions in applying machine learning and deep learning for brain disorder detection and diagnosis.

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Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022

Cited by 4 publications

References 67 publications

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals

Knowledge structure and future research trends of body–mind exercise for mild cognitive impairment: a bibliometric analysis

Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning – A Bibliometric Analysis

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