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

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.

Deep brain stimulation modulates directional frontolimbic connectivity in obsessive‐compulsive disorder

“…Further, a recent study shows that spectral DCM has good inter-subject and inter-session reliability when studying the default mode network 61 . Third, a relatively small number of nodes can be used in spectral DCM due to computational reasons, requiring a priori specification of regions of interest.…”

The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

“…Together they comprised 20 subjects (11 females, mean and standard deviation of age at onset study: 30.1 ± 5.2) and contained a total of 653 rsfMRI sessions (at the least 10 for each subject). For a further description of the longitudinal datasets, see Almgren et al (2018).…”

“…Concerning the longitudinal datasets, the same preprocessing and time series extraction steps were used as in Almgren et al (2018). In short, the initial five images for each resting state fMRI session were discarded, then rsfMRI data were corrected for differences in slice time (using the central slice as a reference), realigned to the first volume of each session, coregistered to an anatomical image (anatomical image prior to first functional scan session), normalized to MNI space and smoothed using a Gaussian kernel (FWHM = 6mm).…”

“…Time-series extraction. Extraction of regional time-series was the same for both types of datasets -and is described in detail in Almgren et al (2018). In short, voxels showing low frequency fluctuations were identified using a GLM with a discrete cosine basis set as regressor of interest (0.0078 -0.1Hz; where the number of components was a function of the number of scans and TR) and eight nuisance regressors (six head motion regressors, CSF signal from 5mm ROI in circulatory system, WM signal from 7mm ROI in brainstem).…”

“…To allow for sufficient generalizability, and ensure a comprehensive examination of how GSR affects connectivity, we performed analyses using two different designs; namely, longitudinal and cross-sectional designs. For the former, we used four longitudinal datasets (see Almgren et al, 2018), to which a hierarchical approach was applied (i.e., from sessions to subjects, and from subjects to group). The benefits of such a hierarchical analysis are that: (a) the total variance is captured by multiple variance components (e.g., between-session variance, between-subject variance), hence rendering parameter estimates potentially more precise, (b) within-subject effects (e.g., fluctuations in the amount of noise) are mitigated, and (c) conclusions are not limited to one acquisition protocol or dataset.…”