Estimating intrinsic dimensionality of fMRI dataset incorporating an AR(1) noise model with cubic spline interpolation
文献类型：期刊论文
| 作者 | Xie, Xiaoping1; Cao, Zhitong1; Weng, Xuchu2 ; Jin, Dan1; Xiaoping Xie
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2009 |
| 卷号 | 72期号:4-6页码:1042-1055 |
| 关键词 | Dimensionality estimation Autoregressive noise model Cubic spline interpolation Functional magnetic resonance imaging Dimensionality reduction |
| ISSN号 | 0925-2312 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| 学科主题 | 认知神经科学 |
| 语种 | 英语 |
| WOS记录号 | WOS:000263372000038 |
| 公开日期 | 2011-08-22 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/5383]  |
| 专题 | 心理研究所_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. |
入库方式： OAI收割
来源：心理研究所
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