{"title":"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","authors":"Sayedeh-Zahra Kazemi-Harikandei , Parnian Shobeiri , Mohammad-Reza Salmani Jelodar , Seyed Mohammad Tavangar","doi":"10.1016/j.neuri.2022.100104","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods and Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>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.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100104"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000668/pdfft?md5=4b2fd13b687332fa0a4be378f10b8576&pid=1-s2.0-S2772528622000668-main.pdf","citationCount":"2","resultStr":"{\"title\":\"Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review\",\"authors\":\"Sayedeh-Zahra Kazemi-Harikandei , Parnian Shobeiri , Mohammad-Reza Salmani Jelodar , Seyed Mohammad Tavangar\",\"doi\":\"10.1016/j.neuri.2022.100104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods and Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>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.</p></div>\",\"PeriodicalId\":74295,\"journal\":{\"name\":\"Neuroscience informatics\",\"volume\":\"2 4\",\"pages\":\"Article 100104\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772528622000668/pdfft?md5=4b2fd13b687332fa0a4be378f10b8576&pid=1-s2.0-S2772528622000668-main.pdf\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuroscience informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772528622000668\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroscience informatics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772528622000668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review
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.
Neuroscience informaticsSurgery, 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