Resting-state test-retest reliability over different preprocessing steps

Introduction: Resting-state (RS) functional connectivity (FC) analysis has become a widely used method for the investigation of human brain connectivity and pathology. While most of the current applications are based on data-driven analyses, the use of functionally specific, a priori defined networks provided by neuroimaging meta-analyses represent an important alternative to these, as they allow the standardized assessment of connectivity patterns. Neuronal activity as measured by functional MRI is influenced by various nuisance signals including system noise, thermal noise, and noise induced by physiological processes of the participant. The presence of these confounds in turn have an impact on the estimation of functional connectivity. Several methods exist to deal with this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we investigated the test-retest reliability of FC analyses in meta-analytically defined networks after removing confounding noise regressors.