Estimating intrinsic dimensionality of fMRI dataset incorporating an AR(1) noise model with cubic spline interpolation

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	Estimating intrinsic dimensionality of fMRI dataset incorporating an AR(1) noise model with cubic spline interpolation
	Xie, Xiaoping 1; Cao, Zhitong 1; Weng, Xuchu2 ; Jin, Dan 1; Xiaoping Xie
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
	2009
语种	英语
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	72 期号:4-6 页码:1042-1055
期刊论文类型	Article
收录类别	SCI
WOS记录号	WOS:000263372000038
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
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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