Estimating intrinsic dimensionality of fMRI dataset incorporating an AR(1) noise model with cubic spline interpolation
Xie, Xiaoping1; Cao, Zhitong1; Weng, Xuchu2; Jin, Dan1; Xiaoping Xie
2009
发表期刊NEUROCOMPUTING
ISSN0925-2312
文章类型Article
卷号72期号:4-6页码:1042-1055
摘要Estimating the true dimensionality of the data to determine what is essential in the data is an important but a difficult problem in fMRI dataset. In this paper, cubic spline interpolation is introduced to detect the number of essential components in fMRI dataset. By constructing proper interpolation variable, more reasonable estimation of the coefficient of an autoregressive noise model of order I can be made. Simulation data and real fMRI dataset of resting-state in human brains are used to compare the performance of the new method incorporating an autoregressive noise model of order 1 with cubic spline interpolation (AR1CSI) with that of the method based only on an autoregressive noise model of order 1 (AR1). The results show the AR1CSI method leads to more accurate estimate of the model order at many circumstances, as illustrated in simulated datasets and real fMRI datasets of resting-state human brain.; Estimating the true dimensionality of the data to determine what is essential in the data is an important but a difficult problem in fMRI dataset. In this paper, cubic spline interpolation is introduced to detect the number of essential components in fMRI dataset. By constructing proper interpolation variable, more reasonable estimation of the coefficient of an autoregressive noise model of order I can be made. Simulation data and real fMRI dataset of resting-state in human brains are used to compare the performance of the new method incorporating an autoregressive noise model of order 1 with cubic spline interpolation (AR1CSI) with that of the method based only on an autoregressive noise model of order 1 (AR1). The results show the AR1CSI method leads to more accurate estimate of the model order at many circumstances, as illustrated in simulated datasets and real fMRI datasets of resting-state human brain. (C) 2008 Elsevier B.V. All rights reserved.
关键词Dimensionality estimation Autoregressive noise model Cubic spline interpolation Functional magnetic resonance imaging Dimensionality reduction
学科领域认知神经科学
收录类别SCI
语种英语
WOS记录号WOS:000263372000038
引用统计
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/5383
专题中国科学院心理研究所回溯数据库(1956-2010)
通讯作者Xiaoping Xie
作者单位1.Zhejiang Univ, Dept Phys, Hangzhou 310027, Peoples R China
2.Chinese Acad Sci, Inst Psychol, Lab Higher Brain Funct, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714
Xie, Xiaoping,Cao, Zhitong,Weng, Xuchu,et al. Estimating intrinsic dimensionality of fMRI dataset incorporating an AR(1) noise model with cubic spline interpolation[J]. NEUROCOMPUTING,2009,72(4-6):1042-1055.
APA Xie, Xiaoping,Cao, Zhitong,Weng, Xuchu,Jin, Dan,&Xiaoping Xie.(2009).Estimating intrinsic dimensionality of fMRI dataset incorporating an AR(1) noise model with cubic spline interpolation.NEUROCOMPUTING,72(4-6),1042-1055.
MLA Xie, Xiaoping,et al."Estimating intrinsic dimensionality of fMRI dataset incorporating an AR(1) noise model with cubic spline interpolation".NEUROCOMPUTING 72.4-6(2009):1042-1055.
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