Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review

Sayedeh-Zahra Kazemi-Harikandei , Parnian Shobeiri , Mohammad-Reza Salmani Jelodar , Seyed Mohammad Tavangar
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引用次数: 2

Abstract

Background

Alzheimer's disease (AD) is the most common cause of dementia. Effective connectivity (EC) methods signify the direction of brain interactions. The identified inter-system mappings can be helpful in characterizing the pathophysiology of the disease.

Methods and Results

We conducted a systematic review of the alterations in EC findings in individuals with AD or Mild Cognitive Impairment (MCI) from PubMed, Scopus, and Google Scholar from fMRI studies. We extracted EC alterations and altered network findings related to specific cognitive impairments. Additionally, we brought a narrative synthesis on the clinical-pathologic relevance of the utilized computational methods. Thirty-nine studies retrieved from the full-text screening. A general pattern of disconnection in several hub centers and changes in inter-network interactions was identified.

Conclusion

In summary, this study demonstrated the beneficial role of EC analyses and network measures in understanding the pathophysiology of AD. Future studies are needed to bring out methodologically consistent data for more structured meta-analytic views.

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阿尔茨海默病和轻度认知障碍患者的有效连通性:一项系统综述
阿尔茨海默病(AD)是痴呆症最常见的病因。有效连接(Effective connectivity, EC)方法表明了大脑相互作用的方向。确定的系统间映射可以帮助表征疾病的病理生理学。方法和结果我们从PubMed、Scopus和Google Scholar的fMRI研究中对AD或轻度认知障碍(MCI)患者EC发现的变化进行了系统回顾。我们提取了与特定认知障碍相关的EC改变和改变的网络发现。此外,我们带来了一个叙事综合的临床病理相关的利用计算方法。从全文筛选中检索到39项研究。确定了几个枢纽中心的一般断开模式和网络间相互作用的变化。综上所述,本研究证明了EC分析和网络测量在了解AD病理生理方面的有益作用。未来的研究需要为更结构化的元分析观点提供方法上一致的数据。
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来源期刊
Neuroscience informatics
Neuroscience informatics Surgery, Radiology and Imaging, Information Systems, Neurology, Artificial Intelligence, Computer Science Applications, Signal Processing, Critical Care and Intensive Care Medicine, Health Informatics, Clinical Neurology, Pathology and Medical Technology
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审稿时长
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