Altered Brain Functional Connectome in Migraine with and without Restless Legs Syndrome: A Resting-State Functional MRI Study

Abstract

Background: Migraine is frequently comorbid with restless legs syndrome (RLS), both displaying functional connectivity (FC) alterations in multiple brain networks, although the neurological basis of this association is unknown.

Methods: We performed resting-state functional magnetic resonance imaging and network-wise analysis of FC in migraine patients with and without RLS and healthy controls (CRL). Network-based statistics (NBS) and composite FC matrix analyses were performed to identify the patterns of FC changes. Correlation analyses were performed to identify associations between alterations in FC and clinical profiles.

Results: NBS results revealed that both migraine patients with and without RLS exhibited lower FC than CRL in the dorsal attention, salience, default mode, cingulo-opercular, visual, frontoparietal, auditory, and sensory/somatomotor networks. Further composite FC matrix analyses revealed differences in FC of the salience, default mode to subcortical and frontoparietal, auditory to salience, and memory retrieval networks between migraine patients with and without RLS. There was a trend toward a negative association between RLS severity and cross-network abnormalities in the default mode to subcortical network.

Discussion: Migraine patients with and without RLS exhibit disruptions of brain FC. Such findings suggest that these disorders are associated with differential neuropathological mechanisms and may aid in the future development of neuroimaging-driven biomarkers for these conditions.

Keywords: MRI; connectome; functional connectivity; migraine; restless legs syndrome.

Figure 1

Overview of the analytical steps in this study. (A) ROIs defined by Power’s template were used to form ROI-wise FC matrix for all participants. Each color represents a different large-scale functional subsystem: 1, dorsal attention; 2, ventral attention; 3, subcortical; 4, salience; 5, frontoparietal task control; 6, visual; 7, default mode; 8, auditory; 9, cingulo-opercular task control; 10, sensory/somatomotor; 11, memory; and 12, cerebellar network (B) FC between an ROI pair was computed using Pearson correlation coefficient and then transformed to z-value. (C) An example of a connectivity matrix from a single subject. (D) Composite z-score approach was used to integrate the high-resolution connectivity matrices into 12 functional subsystems. (E,F) The corresponding statistical approaches for analyzing (C,D), respectively. Abbreviations: FC, functional connectivity; ROIs, regions of interest.

Figure 2

NBS identify the set of abnormal functional connections between study groups. Illustration of the network with reduced FC in two patient groups (compared to CRL group). Node colors correspond with the different functional subsystems. (A) Compared to CRL, MIG w/o RLS showed reduced FC in the network with eight nodes and seven edges, and (B) MIG w RLS showed reduced FC in the network with nine nodes and eight edges. Abbreviations: CRL, healthy controls; FC, functional connectivity; MIG w/o RLS, migraine without restless legs syndrome; MIG w RLS, migraine with restless legs syndrome; NBS, network-based statistics; FWE, family-wise error.

Figure 3

Statistical analysis and effect size estimation of composite z-score FC matrices among three study groups (A)–(C). Circles with different colors represent 12 functional subsystems. Inside the matrices, the squares with different colors indicate seven statistical levels of matrixwise comparisons between study groups (uncorrected p-values); the squares with different sizes indicate the corresponding effect size, which was calculated by Cohen’s d. The asterisk indicates that the p-value is less than the FDR corrected p-value 0.05. Abbreviations: CRL, healthy controls; FC, functional connectivity; FDR, false discovery rate; MIG w/o RLS, migraine without restless legs syndrome; MIG w/RLS, migraine with restless legs syndrome.