Abstract
Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM). We used parametric empirical Bayes (PEB) to model fluctuations in directed coupling over consecutive windows of resting state fMRI time series. Hierarchical PEB can model random effects on connectivity parameters at the second (between-window) level given connectivity estimates from the first (within-window) level. In this work, we used a discrete cosine transform basis set or eigenvariates (i.e., expression of principal components) to model fluctuations in effective connectivity over windows. We evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within default mode network (DMN), using the resting state fMRI from Human Connectome Project (HCP). To model group-level baseline and dynamic effective connectivity for DMN, we extended the PEB approach by conducting a multilevel PEB analysis of between-session and between-subject group effects. Model comparison clearly spoke to dynamic fluctuations in effective connectivity – and the dynamic functional connectivity these changes explain. Furthermore, baseline effective connectivity was consistent across independent sessions – and notably more consistent than estimates based upon conventional models. This work illustrates the advantage of hierarchical modelling with spDCM, in characterizing the dynamics of effective connectivity.
Highlights
The human brain exhibits coherent endogenous fluctuations across distributed brain regions in resting-state functional magnetic resonance imaging data, which are thought to reflect the underlying network architecture (Biswal et al, 1995; Greicius et al, 2003; Lowe et al, 1998; McGuire et al, 1996; Raichle and Snyder, 2007)
For the analysis of large-scale intrinsic brain networks, we extracted the resting-state functional magnetic resonance imaging data (rsfMRI) time series from 8 regions of interest (ROIs), defined in a previously constructed automated labeling map (Desikan et al, 2006), corresponding to the default mode network (DMN) (Yeo et al, 2011). Those regions include the inferior parietal lobe (IPL), isthmus cingulate or posterior cingulate cortex (PCC), rostral anterior cingulate cortex (ACC), superior frontal gyrus (SFG), hippocampus (HIP), parahippocampal gyrus (PHP), middle temporal gyrus (MTG) and pars orbitalis of the inferior frontal lobe (IFG)
We modeled baseline and dynamic effective connectivity using parametric empirical Bayes (PEB) with four different types of basis sets in the second level design matrix (X1): (i) a vector of ones for a simple average across windows, (ii) a discrete cosine transformation (DCT) basis set, (iii) a basis set based on principal component analysis (PCA) of first level A matrices and (iii) a basis set based on functional principal component analysis of first level A matrices: we included fPCA as a dynamic model to incorporate temporal smoothness
Summary
The human brain exhibits coherent endogenous fluctuations across distributed brain regions in resting-state functional magnetic resonance imaging data (rsfMRI), which are thought to reflect the underlying network architecture (Biswal et al, 1995; Greicius et al, 2003; Lowe et al, 1998; McGuire et al, 1996; Raichle and Snyder, 2007). A growing number of studies have looked at correlations over shorter time scales to reveal fluctuations in functional connectivity (Allen et al, 2014; Calhoun et al, 2014; Chang and Glover, 2010; Cribben et al, 2012; Handwerker et al, 2012; Hutchison et al., 2013; Monti et al, 2014; Wang et al, 2016) These studies have led to the notion of dynamic functional connectivity that is conventionally estimated using the cross-correlation of rsfMRI. These limitations call for a characterization in terms of effective connectivity; namely the causal influence that one neural system exerts over another, either at a synaptic or a population level (Friston, 2011)
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