A multiscale analysis of the temporal characteristics of resting-state fMRI data

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In this paper we conduct an investigation of the null hypothesis distribution for functional magnetic resonance imaging (fMRI) time series using multiscale analysis tools SiZer (significance of zero crossings of the derivative) and wavelets Most current approaches to the analysis of fMRI data assume simple models for temporal (short term or long term) dependence structure Such simplifications are to some extent necessary due to the complex high-dimensional nature of the data but to date there have been few systematic studies of the dependence structures under a range of possible null hypotheses using data sets gathered specifically for that purpose We aim to address some of these issues by analyzing the detrended data with a long enough time horizon to study possible long-range temporal dependence Our multiscale approach shows that even for resting-state data data i e null or ambient thought some voxel time series cannot be modeled by white noise and need long-range dependent type error structure This finding suggests the use of different time series models in different parts of the brain in fMRI studies (C) 2010 Elsevier B V All rights reserved
Publisher
ELSEVIER
Issue Date
2010-11
Language
English
Article Type
Article
Citation

JOURNAL OF NEUROSCIENCE METHODS, v.193, no.2, pp.334 - 342

ISSN
0165-0270
DOI
10.1016/j.jneumeth.2010.08.021
URI
http://hdl.handle.net/10203/285530
Appears in Collection
MA-Journal Papers(저널논문)IE-Journal Papers(저널논문)
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