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Neuroimage. 2014 Nov 15;102 Pt 2:294-308. doi: 10.1016/j.neuroimage.2014.07.045. Epub 2014 Jul 27.

Impact of autocorrelation on functional connectivity.

Author information

1
The Mind Research Network, Albuquerque, NM, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA. Electronic address: marbabshirani@mrn.org.
2
The Mind Research Network, Albuquerque, NM, USA.
3
Department of CSEE, University of Maryland, Baltimore County, MD, USA.
4
The Mind Research Network, Albuquerque, NM, USA; K. G. Jebsen Cener for Research on Neuropsychiatric Disorders, University of Bergen, Norway; Department of Biological and Medical Psychology, University of Bergen, Norway.
5
Department of Psychiatry, University of California, San Francisco, CA, USA; San Francisco VA Medical Center, San Francisco, CA, USA.
6
Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA.
7
Department of Psychiatry, University of Iowa, IA, USA.
8
The Mind Research Network, Albuquerque, NM, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA.

Abstract

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in "spurious" correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies.

KEYWORDS:

Autocorrelation; Autoregressive process; Functional connectivity; Independent component analysis; Resting-state fMRI

PMID:
25072392
PMCID:
PMC4253536
DOI:
10.1016/j.neuroimage.2014.07.045
[Indexed for MEDLINE]
Free PMC Article
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