Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data

Abstract

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078–0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain’s functioning at resting state.

Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections

“…When estimating a more complex model, one could be confident that the previous connectivity pattern does not change much and all changes in the extended model are associated with the new source (region of interest). In our previous work (Sharaev et al, 2016), we found a stable coupling pattern between the four basic DMN nodes. Here, we aimed at developing the model to assess its parameter stability when adding or removing active regions.…”

“…Regarding family a, one may recall from Figure 2 it is the full connected base, which was the best model when analyzing four source models (Sharaev et al, 2016). This means that no matter how the LHIP/RHIP region is included, the best connection pattern between these four nodes remains the same.…”

“…(B) significant ( p < 0.05) coefficients of functional connectivity between the six ROIs of the DMN. (C) The model with four basic ROIs taken from (Sharaev et al, 2016). …”

“…An F-contrast was specified across all frequencies of DCT, producing an SPM that identified regions exhibiting BOLD fluctuations within the frequency band. The preprocessing and conventional SPM analysis are the same as in our previous work dedicated to assess effective connectivity between four key DMN regions (Sharaev et al, 2016). For DCM analysis, the principal eigenvariate of a (8 mm radius) sphere was computed (adjusted for confounds) for each region and centered on the peak voxel of the aforementioned F -contrast.…”

“…According to the purpose of the study, we took as a basic model set eight models with four sources from our previous work (Sharaev et al, 2016): a full connected model, three models where different regions predominantly affected the other ones (mPFC, PCC, and bilateral modulation), and the same models but without direct connections between bilateral LIPC and RIPC, comprising eight model families, see Figure 2A .…”