{"title":"功能性MRI数据的联合时空统计分析","authors":"Z. Fu, Y. Hui, Zhi-Pei Liang","doi":"10.1109/ICIP.1998.723595","DOIUrl":null,"url":null,"abstract":"This paper presents a novel method for the detection of neuronal activity-dependent signal changes in functional magnetic resonance imaging (fMRI) image sequences. In this method, wavelet analysis and statistical testing are applied jointly, enabling fMRI image sequences to be effectively analyzed in a unique spatio temporal framework. Experimental results show that this method performs significantly better than several existing methods for fMRI data with low SNR.","PeriodicalId":220168,"journal":{"name":"Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Joint spatiotemporal statistical analysis of functional MRI data\",\"authors\":\"Z. Fu, Y. Hui, Zhi-Pei Liang\",\"doi\":\"10.1109/ICIP.1998.723595\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel method for the detection of neuronal activity-dependent signal changes in functional magnetic resonance imaging (fMRI) image sequences. In this method, wavelet analysis and statistical testing are applied jointly, enabling fMRI image sequences to be effectively analyzed in a unique spatio temporal framework. Experimental results show that this method performs significantly better than several existing methods for fMRI data with low SNR.\",\"PeriodicalId\":220168,\"journal\":{\"name\":\"Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP.1998.723595\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.1998.723595","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Joint spatiotemporal statistical analysis of functional MRI data
This paper presents a novel method for the detection of neuronal activity-dependent signal changes in functional magnetic resonance imaging (fMRI) image sequences. In this method, wavelet analysis and statistical testing are applied jointly, enabling fMRI image sequences to be effectively analyzed in a unique spatio temporal framework. Experimental results show that this method performs significantly better than several existing methods for fMRI data with low SNR.