Variability and reliability of effective connectivity within the core default mode network: A longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most research applying spectral DCM has focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, behavioural, and physical states. Determining the sources of fluctuations in effective connectivity may yield greater understanding of brain processes and inform clinical applications about potential confounds. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We further investigated how standard procedures for data processing and signal extraction affect this consistency. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample consisted of 20 subjects with 653 resting state fMRI sessions in total. These data allowed to quantify the robustness of connectivity estimates for each subject, and to draw conclusions beyond specific data features. We found that subjects contributing to all datasets showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and reliability of connectivity for the majority of subjects. Bayesian model reduction increased reliability (within-subjects) and stability (between-subjects) of connectivity patterns.