Understanding dementia disorders can be challenging due to their similar symptoms. In order to detect early signs of these disorders, researchers have turned to connectivity analysis of electrophysiology signals. This systematic review explores the step-by-step procedures and methodological issues involved in connectivity analysis frameworks for dementia disorders. By analyzing articles published between January 2016 and December 2022, the study identifies an increasing trend in research in this field. The review also reveals that EEG is more commonly used than MEG, and that graph theory metrics, such as clustering coefficient, global efficiency, and characteristic path length, are frequently employed for analysis. This study provides valuable insight into employing connectivity measures to better understand the underlying mechanisms and differential diagnosis of dementia-related disorders.

BackgroundDementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification.ObjectiveWith the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues.MethodsIn this study, 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 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy.ResultsOut of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 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 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics.SignificanceThis study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.

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