Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation.

ABSTRACT: The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of

SUBMITTER: Afyouni S 

PROVIDER: S-EPMC6693558 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature