{"title":"利用 fMRI 数据提取大脑功能网络方法的调查。","authors":"Yuhui Du, Songke Fang, Xingyu He, Vince D Calhoun","doi":"10.1016/j.tins.2024.05.011","DOIUrl":null,"url":null,"abstract":"<p><p>Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.</p>","PeriodicalId":23325,"journal":{"name":"Trends in Neurosciences","volume":" ","pages":"608-621"},"PeriodicalIF":14.6000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A survey of brain functional network extraction methods using fMRI data.\",\"authors\":\"Yuhui Du, Songke Fang, Xingyu He, Vince D Calhoun\",\"doi\":\"10.1016/j.tins.2024.05.011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.</p>\",\"PeriodicalId\":23325,\"journal\":{\"name\":\"Trends in Neurosciences\",\"volume\":\" \",\"pages\":\"608-621\"},\"PeriodicalIF\":14.6000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Neurosciences\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.tins.2024.05.011\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Neurosciences","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.tins.2024.05.011","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
A survey of brain functional network extraction methods using fMRI data.
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
期刊介绍:
For over four decades, Trends in Neurosciences (TINS) has been a prominent source of inspiring reviews and commentaries across all disciplines of neuroscience. TINS is a monthly, peer-reviewed journal, and its articles are curated by the Editor and authored by leading researchers in their respective fields. The journal communicates exciting advances in brain research, serves as a voice for the global neuroscience community, and highlights the contribution of neuroscientific research to medicine and society.