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Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis

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Title
Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis
Author(s)
Oliver James; Hyunjin Park; Seong-Gi Kim
Publication Date
2019-08
Journal
HUMAN BRAIN MAPPING, v.40, no.11, pp.3321 - 3337
Publisher
WILEY-BLACKWELL
Abstract
Abstract A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross-covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity. © 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.
URI
https://pr.ibs.re.kr/handle/8788114/6277
ISSN
1065-9471
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > Journal Papers (저널논문)
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