Brain network effects by continuous theta burst stimulation in mal de débarquement syndrome: simultaneous EEG and fMRI study

Abstract

Objective. Heterogeneous clinical responses to treatment with non-invasive brain stimulation are commonly observed, making it necessary to determine personally optimized stimulation parameters. We investigated neuroimaging markers of effective brain targets of treatment with continuous theta burst stimulation (cTBS) in mal de débarquement syndrome (MdDS), a balance disorder of persistent oscillating vertigo previously shown to exhibit abnormal intrinsic functional connectivity.Approach.Twenty-four right-handed, cTBS-naive individuals with MdDS received single administrations of cTBS over one of three stimulation targets in randomized order. The optimal target was determined based on the assessment of acute changes after the administration of cTBS over each target. Repetitive cTBS sessions were delivered on three consecutive days with the optimal target chosen by the participant. Electroencephalography (EEG) was recorded at single-administration test sessions of cTBS. Simultaneous EEG and functional MRI data were acquired at baseline and after completion of 10-12 sessions. Network connectivity changes after single and repetitive stimulations of cTBS were analyzed.Main results.Using electrophysiological source imaging and a data-driven method, we identified network-level connectivity changes in EEG that correlated with symptom responses after completion of multiple sessions of cTBS. We further determined that connectivity changes demonstrated by EEG during test sessions of single administrations of cTBS were signatures that could predict optimal targets.Significance.Our findings demonstrate the effect of cTBS on resting state brain networks and suggest an imaging-based, closed-loop stimulation paradigm that can identify optimal targets during short-term test sessions of stimulation.ClinicalTrials.gov Identifier:NCT02470377.

Keywords: EEG; continuous theta burst stimulation; fMRI; functional connectivity; mal de débarquement syndrome; resting state networks.

Conflict of interest statement

Declaration of Competing Interest

The Authors declare no competing interests.

© 2021 IOP Publishing Ltd.

Figures

Figure 1:

The experimental study protocol and analysis schemes.

Figure 2:. Symptom changes from Day 1 to Day 5.

The VAS changes of 16 responders are in blue and 7 non-responders are in red.

Figure 3:. EEG-derived visual network.

The network was identified by a spatial match approach with the visual network template. In the ROI (circled region in the insert), symptom changes after cTBS protocol were correlated with connectivity changes in the right inferior occipital gyrus (pcorreced = 0.04). Post-vs-pre changes of the connectivity in the right inferior occipital gyrus did not differ between the responders and non-responders.

Figure 4:. EEG networks associated with the default mode network (DMN).

The network A was identified by a spatial match approach with the DMN template. The networks B and C were identified for connectivity at the left inferior parietal lobule and right inferior parietal lobule. In the ROI (circled regions in inserts), symptom changes after cTBS protocol were correlated with connectivity changes in the right IPL (B2: r = 0.60, pcorrected = 0.007). Post-vs-pre changes of the connectivity in the left medial frontal gyrus is different between the responders and non-responders, but only marginally different in the right IPL. * indicates significance at p < 0.05. Δ Indicates marginal significance at p < 0.1.

Figure 5:. Transient changes in EEG network connectivity at test sessions.

Connectivity changes immediately after single administration of cTBS were derived from the ROIs at the right inferior occipital (A), gyrus left medial frontal gyrus (B), left inferior parietal lobule (C), and right inferior parietal lobule (D). The activities are grouped by whether the targets were selected as optimal targets, non-optimal targets, or undecided targets. * Indicates significance at p

Figure 6:. Transient changes in EEG networks can predict optimal vs. non-optimal targets.

True positive (TP), false negative (FN), true negative (TN) and false negative (FN) are displayed for the classification results of a linear discriminate classifier with leave-one-out cross validation. Based on the connectivity features, all undecided targets were automatically classified as non-optimal targets.