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

Hannes Almgren, Frederik Van de Steen, Simone Kühn, Adeel Razi, Karl Friston, Daniele Marinazzo

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

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 applications of spectral DCM have 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, and physiological states. Quantifying these variations may help understand functional brain architectures – and inform clinical applications. 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 also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects 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 the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.

Original languageEnglish
Pages (from-to)757-768
Number of pages12
JournalNeuroImage
Volume183
DOIs
Publication statusPublished - 1 Dec 2018

Keywords

  • Dynamic causal modelling
  • Effective connectivity
  • fMRI
  • Longitudinal designs
  • Reliability
  • Resting state
  • Variability

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