Impact of autocorrelation on functional connectivity

Mohammad R. Arbabshirani, Eswar Damaraju, Ronald Phlypo, Sergey Plis, Elena Allen, Sai Ma, Daniel Mathalon, Adrian Preda, Jatin G. Vaidya, Tülay Adali, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)294-308
Number of pages15
Issue numberP2
StatePublished - Nov 15 2014


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

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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