AUTHOR=Adebisi Abdulyekeen T. , Veluvolu Kalyana C. 

TITLE=Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 15 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2023.1039496

DOI=10.3389/fnagi.2023.1039496

ISSN=1663-4365

ABSTRACT=Dementia related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed in similar clinical symptoms. These disorders are usually irreversible at their late onsets, hence their lack of validated and approved cure. Since their early onsets usually lurk long before the expression of noticeable symptom, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiological signals have played a significant role in the diagnosis of various dementia disorders through early onsets identification. With the various applications of electrophysiological signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. The study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues. In the review, ProQuest, PubMed, IEEE Xplore, Springer Link and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia related disorders between January 2016 to December 2021. The quality of assessment of the studied articles was done using Cochrane guidelines for systematic review of diagnostic test accuracy. Out of a total of 3958 articles found to have been published on the review scope between January 2016 to December 2021, a total of 41 peer review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:9. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), characteristic path length (CPL) and global efficiency (GE) appearing more frequent compared to other metrics. This review provides a general insight on how to employ connectivity measures for the analysis of electrophysiology signals of dementia related disorders in order to better understand their underlying mechanism as well as their differential diagnosis.