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  Estimating the effective degrees of freedom in univariate multiple regression analysis

Kruggel, F., Pelegrini-Issac, M., & Benali, H. (2002). Estimating the effective degrees of freedom in univariate multiple regression analysis. Medical Image Analysis, 6(1), 63-75. doi:10.1016/S1361-8415(01)00052-4.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-B278-3 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-444B-2
Genre: Journal Article

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 Creators:
Kruggel, F.1, 2, Author              
Pelegrini-Issac, M.2, Author              
Benali, H., Author
Affiliations:
1Department Cognitive Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634563              
2MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634574              

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Free keywords: Functional MRI; Effective degrees of freedom; Multiple linear regression; Temporal autocorrelations
 Abstract: The general linear model provides the most widely applied statistical framework for analyzing functional MRI (fMRI) data. With the increasing temporal resolution of recent scanning protocols, and more elaborate data preprocessing schemes, data independency is no longer a valid assumption. In this paper, we revise the statistical background of the general linear model in the presence of temporal autocorrelations. First, when detecting the activation signal, we explicitly account for the temporal autocorrelation structure, which yields a generalized F-test and the associated corrected (or effective) degrees of freedom (DOF). The proposed approach is data driven and thus independent of any specific preprocessing method. Then, for event-related protocols, we propose a new model for the temporal autocorrelations (“damped oscillator” model) and compare this model to another, previously used in the field (first-order autoregressive model, or AR(1) model). In the case of long fMRI time series, an efficient approximation for the number of effective DOF is provided for both models. Finally, the validity of our approach is assessed using simulated and real fMRI data and is compared with more conventional methods.

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Language(s): eng - English
 Dates: 2002
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: eDoc: 239701
ISI: 000174344500004
Other: P7078
DOI: 10.1016/S1361-8415(01)00052-4
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Title: Medical Image Analysis
  Other : Med. Image Anal.
Source Genre: Journal
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Publ. Info: London : Elsevier
Pages: - Volume / Issue: 6 (1) Sequence Number: - Start / End Page: 63 - 75 Identifier: ISSN: 1361-8415
CoNE: https://pure.mpg.de/cone/journals/resource/954927741859