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

• Published in: NeuroImage (Orlando, Fla.) Vol. 199; pp. 609 - 625
• Main Authors: Afyouni, Soroosh, Smith, Stephen M., Nichols, Thomas E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2019; Elsevier Limited; Academic Press
• Subjects: Autocorrelation; Bias; Brain; Brain - diagnostic imaging; Brain mapping; Connectome - methods; Connectome - standards; Cross correlation; Data Interpretation, Statistical; fMRI; Functional connectivity; Functional magnetic resonance imaging; Functional Neuroimaging - methods; Functional Neuroimaging - standards; Genetic transformation; Graph theory; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Medical imaging; Methods; Neural networks; Pearson correlation coefficient; Quadratic covariance; Resting state; Serial correlation; Time series; Toeplitz matrix; Variance; Functional connectivity; fMRI; Resting state; Cross correlation; Serial correlation; Time-series; Graph theory; Autocorrelation; Toeplitz matrix; Variance; Quadratic covariance; Pearson correlation coefficient
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.05.011

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 interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical “xDF” method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction. •Autocorrelation is a problem for sample correlation, breaking the variance-stabilising property of Fisher's transformation.•We show that fMRI autocorrelation varies systematically with region of interest size, and is heterogeneous over subjects.•Existing adjustment methods are themselves biased when true correlation is non-zero due to a confounding effect.•Our “xDF” method provides accurate Z-scores based on either of Pearson's or Fisher's transformed correlations.•Resting state fMRI autocorrelation considerably alters the graph theoretical description of human connectome.