Central Executive and Default Mode Network Intranet work Functional Connectivity Patterns in Chronic Migraine

Abstract

Background: The neural mechanisms of chronic migraine remain largely unknown but linked to the decreased connectivity to intrinsic brain networks.

Objective: To characterize the intranetwork functional connectivity within the Central Executive Network (CEN) and Default Mode Network (DMN) in chronic migraine (CM), with and without medication overuse headache (MOH).

Methods: Using functional magnetic resonance imaging, we performed post-hoc analysis of a total of 136 pairs of nodes to node functional connectivity (NTNC) within the CEN and 6 pairs of NTNC within the DMN in CM (n=13) and CM^MOH (n=16) as compared to controls, and between these two subgroups.

Results: Connectivity between right ventrolateral prefrontal cortex (PFC) to contralateral anterior thalamus and connectivity between left dorsal PFC/frontal eye field (FEF) to dorsomedial PFC were decreased within the CEN in both CM and CM^MOH subgroups. In the CEN, there was more widespread disruption in the CM^MOH (n=16) versus CM (n=13), when compared to healthy controls. Within the subgroups, connectivity between right inferior frontal gyrus to left dorsolateral PFC was decreased in CM^MOH compared to CM. In the DMN, only one NTNC (left lateral parietal to precuneus/PCC) was disrupted in the CM^MOH group when compared to controls.

Conclusion: There are similar patterns of NTNC dysfunction within CEN in CM regardless of MOH status. We observed more extensive intranetwork disruption in CM^MOH than CM. The decreased coherence between the right inferior frontal gyrus and the left dorsolateral PFC in CM^MOH is likely associated with a significant disruption in the inhibitory control and a maladaptive response in risk aversion and reward; whereas the decreased coherence between right dorsolateral and ventrolateral PFC to contralateral dorsal PFC/FEF may be related to lack of cognitive control and top-down regulation of pain in both CM and CM^MOH.

Keywords: Central executive network; Chronic migraine; Default mode network; Functional MRI; Medication overuse headache.

Figure 1:

Axial and sagittal view of the Central Executive Network (CEN). (1) Left dIPFC, (2) Right dIPFC, (3) Left dPFC/FEF, (4) Right dPFC/FEF, (5) dm PFC, (6) Left vIPFC, (7) Right vIPFC, (8) Left OFI, (9) Right IFG, (10) Right Inferior Temporal, (11) Left Lateral Parietal, (12) Right Lateral Parietal, (13) Left dCaudate, (14) Right dCaudate, (15) Right vmCaudate, (16) Left Anterior Thalamus, (17) Right anterior thalamus. Images were made with “Surf Ice” (https://www.nitrc.org/projects/surfice/) using the exact MNI coordinate locations. Node depth is illustrated by transparency.

Figure 2:

Axial and sagittal view of the Default Mode Network (DMN). (1) Medial prefrontal, (2) Precuneus/PCC, (3) Left lateral parietal, (4) Right lateral parietal. Images were made with “Surf Ice” (https://www.nitrc.org/projects/surfice/) using the exact MNI coordinate locations and sphere sizes. Node depth is illustrated by transparency.

Figure 3:

Intra-network connectivity differences in CM^MOH as compared to controls (left) and CM as compared to controls (right). Colored lines represents a significant intra-network connectivity difference between the CM groups as compared to matched controls. Green represents decreased connectivity as compared to controls, and blue represents shared decreased difference in both subgroups. Images were generated using the Matlab application “Circro” (https://github.com/bonilhamusclab/circro).

Figure 4:

Colored lines represent intra-network connectivity differences between the CM^MOH and CM. Green represents the intra-network connection that was lower in CM patients, blue represents the intra-network connection that was lower in CM^MOH patients. Images were generated using the Matlab application “Circro” (https://github.com/bonilhamusclab/circro).

Figure 5:

A schematic diagram of fMRI data processing workflow. After fMRI time series activity level for each individual node is estimated for each participant, then BOLD signals from each node are correlated to other nodes within a priori network, and the R value (correlation coefficient) is calculated for each pair (edge). A table is then generated for each pair of nodes, containing all edges for each network per participant. Average node to node connectivity is calculated by averaging the R value of the same edge within the network in all participants.