Repetitive transcranial magnetic stimulation (rTMS) has been increasingly used to treat many neurological and neuropsychiatric disorders. However, the clinical response is heterogeneous mainly due to our inability to predict the effect of rTMS on the human brain. Our previous investigation based on functional magnetic resonance imaging (fMRI) suggested that neuroimaging-guided navigation for rTMS could be informed by understanding connectivity patterns that correlate with treatment response. In this study, 20 individuals with a balance disorder called Mal de Debarquement Syndrome completed high-density resting-state electroencephalogram (EEG) and fMRI recordings before and after 5 days of rTMS stimulation over both dorsolateral prefrontal cortices. Based on temporal independent component analysis of source-level EEG data, large-scale electrophysiological resting-state networks were reconstructed and connectivity values in each individual were quantified both before and after treatment. Our results show that high-density, resting-state EEG can reveal connectivity changes in brain networks after rTMS that correlate with symptom changes. The connectivity changes measured by EEG were primarily superficial cortical areas that correlate with previously shown default mode network changes revealed by fMRI. Further, higher baseline EEG connectivity values in the primary visual cortex were predictive of symptom reduction after rTMS. Our findings suggest that multimodal EEG and fMRI measures of brain networks can be biomarkers that correlate with the treatment effect of rTMS. Since EEG is compatible with rTMS, real-time navigation based on an EEG neuroimaging marker may augment rTMS optimization.
Keywords: EEG; Mal de Debarquement Syndrome; brain stimulation; fMRI; functional connectivity; resting-state networks.