多空间尺度多变量模式分析方法的验证

J. Bulthé, J. V. D. Hurk, Nicky Daniels, B. Smedt, H. O. D. Beeck
{"title":"多空间尺度多变量模式分析方法的验证","authors":"J. Bulthé, J. V. D. Hurk, Nicky Daniels, B. Smedt, H. O. D. Beeck","doi":"10.1109/PRNI.2014.6858513","DOIUrl":null,"url":null,"abstract":"Most fMRI studies using Multi-Voxel Pattern Analysis (MVPA) restrict these analyses to merely one spatial scale. However, recently [1] used a multi-spatial scale method combining three levels of MVPA analysis on fMRI data from 16 subjects who performed a number comparison task: whole-brain MVPA, Regions Of Interest (ROI) based MVPA, and a small radius searchlight. The results of [1] clearly demonstrated the necessity of incorporating different spatial scales in MVPA analysis to draw conclusions on how the neural representations of the effects are distributed across the brain. We tested the validity of the method used in this empirical study by using three simulated fMRI datasets. Both simulated data and the real data [1] confirmed the relevance of analyzing data with MVPA on different spatial scales.","PeriodicalId":133286,"journal":{"name":"2014 International Workshop on Pattern Recognition in Neuroimaging","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A validation of a multi-spatialscale method for multivariate pattern analysis\",\"authors\":\"J. Bulthé, J. V. D. Hurk, Nicky Daniels, B. Smedt, H. O. D. Beeck\",\"doi\":\"10.1109/PRNI.2014.6858513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most fMRI studies using Multi-Voxel Pattern Analysis (MVPA) restrict these analyses to merely one spatial scale. However, recently [1] used a multi-spatial scale method combining three levels of MVPA analysis on fMRI data from 16 subjects who performed a number comparison task: whole-brain MVPA, Regions Of Interest (ROI) based MVPA, and a small radius searchlight. The results of [1] clearly demonstrated the necessity of incorporating different spatial scales in MVPA analysis to draw conclusions on how the neural representations of the effects are distributed across the brain. We tested the validity of the method used in this empirical study by using three simulated fMRI datasets. Both simulated data and the real data [1] confirmed the relevance of analyzing data with MVPA on different spatial scales.\",\"PeriodicalId\":133286,\"journal\":{\"name\":\"2014 International Workshop on Pattern Recognition in Neuroimaging\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Workshop on Pattern Recognition in Neuroimaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PRNI.2014.6858513\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Workshop on Pattern Recognition in Neuroimaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRNI.2014.6858513","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

大多数使用多体素模式分析(MVPA)的fMRI研究将这些分析限制在一个空间尺度上。然而,最近[1]使用了一种多空间尺度方法,将三个层次的MVPA分析结合了16名受试者的fMRI数据,这些受试者执行了数字比较任务:全脑MVPA,基于感兴趣区域(ROI)的MVPA和小半径探照灯。[1]的结果清楚地证明了在MVPA分析中纳入不同空间尺度的必要性,以得出关于效应的神经表征如何在大脑中分布的结论。我们通过使用三个模拟fMRI数据集来测试本实证研究中使用的方法的有效性。模拟数据和实际数据[1]都证实了分析数据在不同空间尺度上与MVPA的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A validation of a multi-spatialscale method for multivariate pattern analysis
Most fMRI studies using Multi-Voxel Pattern Analysis (MVPA) restrict these analyses to merely one spatial scale. However, recently [1] used a multi-spatial scale method combining three levels of MVPA analysis on fMRI data from 16 subjects who performed a number comparison task: whole-brain MVPA, Regions Of Interest (ROI) based MVPA, and a small radius searchlight. The results of [1] clearly demonstrated the necessity of incorporating different spatial scales in MVPA analysis to draw conclusions on how the neural representations of the effects are distributed across the brain. We tested the validity of the method used in this empirical study by using three simulated fMRI datasets. Both simulated data and the real data [1] confirmed the relevance of analyzing data with MVPA on different spatial scales.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信