Adebisi Abdulyekeen T, Veluvolu Kalyana C
School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea.
School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea.
Front Aging Neurosci. 2023 Mar 3;15:1039496. doi: 10.3389/fnagi.2023.1039496. eCollection 2023.
Dementia-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.
With 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.
In 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.
Out 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.
This 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.
与痴呆症相关的疾病长期以来一直是研究和医疗界面临的挑战，因为它们的各种形式都表现出相似的临床症状。这些疾病在晚期通常是不可逆的，因此缺乏经过验证和批准的治疗方法。由于它们的前驱期通常在明显的临床症状出现之前潜伏很长时间，因此有人建议将与治疗早期发病相关的二级预防作为可能的解决方案。电生理信号的连通性分析通过早期发病识别在各种痴呆症疾病的诊断中发挥了重要作用。
鉴于电生理信号的各种应用，本研究的目的是系统回顾痴呆症疾病连通性分析框架的逐步程序。本研究旨在识别此类框架中涉及的方法学问题，并提出解决这些问题的方法。
在本研究中，使用ProQuest、PubMed、IEEE Xplore、Springer Link和ScienceDirect数据库来探索2016年1月至2022年12月期间与痴呆症相关疾病的电生理信号连通性分析的演变和进展。使用Cochrane诊断测试准确性系统评价指南对所研究文章进行评估质量。
在2016年1月至2022年12月期间发现的总共4638篇关于综述范围的文章中，共确定了51篇同行评审文章完全符合综述标准。在所考虑的时间范围内，该领域的研究呈现出上升趋势。在所审查的文章中发现的脑磁图（MEG）和脑电图（EEG）使用比例为1:8。大多数审查文章采用图论指标进行分析，与其他指标相比，聚类系数（CC）、全局效率（GE）和特征路径长度（CPL）出现得更频繁。
本研究提供了关于如何使用连通性测量来分析与痴呆症相关疾病的电生理信号的一般见解，以便更好地理解其潜在机制和鉴别诊断。
