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

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...