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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
Source PublicationNEUROCOMPUTING
ISSN0925-2312
SubtypeArticle
Volume72Issue:4-6Pages:1042-1055
AbstractEstimating 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.
KeywordDimensionality estimation Autoregressive noise model Cubic spline interpolation Functional magnetic resonance imaging Dimensionality reduction
Subject Area认知神经科学
Indexed BySCI
Language英语
WOS IDWOS:000263372000038
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/5383
Collection中国科学院心理研究所回溯数据库(1956-2010)
Corresponding AuthorXiaoping Xie
Affiliation1.Zhejiang Univ, Dept Phys, Hangzhou 310027, Peoples R China
2.Chinese Acad Sci, Inst Psychol, Lab Higher Brain Funct, Beijing 100101, Peoples R China
Recommended Citation
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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