Aurélie Leborgne, F. Ber, Laetitia Degiorgis, L. Harsan, Stella Marc-Zwecker, V. Noblet
{"title":"基于频繁模式挖掘的脑功能连通性分析。在小鼠模型表征中的应用","authors":"Aurélie Leborgne, F. Ber, Laetitia Degiorgis, L. Harsan, Stella Marc-Zwecker, V. Noblet","doi":"10.1109/ISBI48211.2021.9434117","DOIUrl":null,"url":null,"abstract":"Functional Magnetic Resonance Imaging (fMRI) is an imaging technique that allows to explore brain function in vivo. Many methods dedicated to analyzing these data are based on graph modeling, each node corresponding to a brain region and the edges representing their functional link. The objective of this work is to investigate the interest of methods for extracting frequent pattern in graphs to compare these data between two populations. Results are presented in the context of the characterization of a mouse model of Alzheimer’s disease in comparison with a group of control mice.","PeriodicalId":372939,"journal":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Analysis Of Brain Functional Connectivity By Frequent Pattern Mining In Graphs. Application To The Characterization Of Murine Models\",\"authors\":\"Aurélie Leborgne, F. Ber, Laetitia Degiorgis, L. Harsan, Stella Marc-Zwecker, V. Noblet\",\"doi\":\"10.1109/ISBI48211.2021.9434117\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Functional Magnetic Resonance Imaging (fMRI) is an imaging technique that allows to explore brain function in vivo. Many methods dedicated to analyzing these data are based on graph modeling, each node corresponding to a brain region and the edges representing their functional link. The objective of this work is to investigate the interest of methods for extracting frequent pattern in graphs to compare these data between two populations. Results are presented in the context of the characterization of a mouse model of Alzheimer’s disease in comparison with a group of control mice.\",\"PeriodicalId\":372939,\"journal\":{\"name\":\"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBI48211.2021.9434117\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI48211.2021.9434117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis Of Brain Functional Connectivity By Frequent Pattern Mining In Graphs. Application To The Characterization Of Murine Models
Functional Magnetic Resonance Imaging (fMRI) is an imaging technique that allows to explore brain function in vivo. Many methods dedicated to analyzing these data are based on graph modeling, each node corresponding to a brain region and the edges representing their functional link. The objective of this work is to investigate the interest of methods for extracting frequent pattern in graphs to compare these data between two populations. Results are presented in the context of the characterization of a mouse model of Alzheimer’s disease in comparison with a group of control mice.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
