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A modified temporal self-correlation method for analysis of fMRI time series

A modified temporal self-correlation method for analysis of fMRI time series Temporal self-correlation has recently been proposed as a measure for fMRI-activation detection. In this paper, a modified temporal self-correlation method is introduced. The modified temporal self-correlation is based on the expectation value and standard deviation of the correlation coefficients between all pairs of epochs, while the original temporal self-correlation method is only based on the expectation value. Performance of the proposed method is evaluated on both simulated and in vivo fMRI data. Compared with the original temporal self-correlation method, the proposed method shows a significant improvement. In addition, a technique for quantitative comparison of different fMRI data analysis methods is proposed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

A modified temporal self-correlation method for analysis of fMRI time series

Neuroinformatics , Volume 1 (3) – Jun 6, 2007

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Publisher
Springer Journals
Copyright
Copyright © 2003 by Humana Press Inc
Subject
Biomedicine; Neurosciences; Computer Appl. in Life Sciences; Neurology; Biotechnology; Computational Biology/Bioinformatics
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1385/NI:1:3:259
pmid
15046247
Publisher site
See Article on Publisher Site

Abstract

Temporal self-correlation has recently been proposed as a measure for fMRI-activation detection. In this paper, a modified temporal self-correlation method is introduced. The modified temporal self-correlation is based on the expectation value and standard deviation of the correlation coefficients between all pairs of epochs, while the original temporal self-correlation method is only based on the expectation value. Performance of the proposed method is evaluated on both simulated and in vivo fMRI data. Compared with the original temporal self-correlation method, the proposed method shows a significant improvement. In addition, a technique for quantitative comparison of different fMRI data analysis methods is proposed.

Journal

NeuroinformaticsSpringer Journals

Published: Jun 6, 2007

References