Abnormal Phase Coupling in Parkinson's Disease and Normalization Effects of Subthreshold Vestibular Stimulation

Abstract

The human brain is a highly dynamic structure requiring dynamic coordination between different neural systems to perform numerous cognitive and behavioral tasks. Emerging perspectives on basal ganglia (BG) and thalamic functions have highlighted their role in facilitating and mediating information transmission among cortical regions. Thus, changes in BG and thalamic structures can induce aberrant modulation of cortico-cortical interactions. Recent work in deep brain stimulation (DBS) has demonstrated that externally applied electrical current to BG structures can have multiple downstream effects in large-scale brain networks. In this work, we identified EEG-based altered resting-state cortical functional connectivity in Parkinson's disease (PD) and examined effects of dopaminergic medication and electrical vestibular stimulation (EVS), a non-invasive brain stimulation (NIBS) technique capable of stimulating the BG and thalamus through vestibular pathways. Resting EEG was collected from 16 PD subjects and 18 age-matched, healthy controls (HC) in four conditions: sham (no stimulation), EVS1 (4-8 Hz multisine), EVS2 (50-100 Hz multisine) and EVS3 (100-150 Hz multisine). The mean, variability, and entropy were extracted from time-varying phase locking value (PLV), a non-linear measure of pairwise functional connectivity, to probe abnormal cortical couplings in the PD subjects. We found the mean PLV of Cz and C3 electrodes were important for discrimination between PD and HC subjects. In addition, the PD subjects exhibited lower variability and entropy of PLV (mostly in theta and alpha bands) compared to the controls, which were correlated with their clinical characteristics. While levodopa medication was effective in normalizing the mean PLV only, all EVS stimuli normalized the mean, variability and entropy of PLV in the PD subject, with the exact extent and duration of improvement a function of stimulus type. These findings provide evidence demonstrating both low- and high-frequency EVS exert widespread influences on cortico-cortical connectivity, likely via subcortical activation. The improvement observed in PD in a stimulus-dependent manner suggests that EVS with optimized parameters may provide a new non-invasive means for neuromodulation of functional brain networks.

Keywords: EEG; Parkinson’s disease; cortical oscillations; electrical vestibular stimulation; phase locking value; sample entropy; sparse discriminant analysis.

FIGURE 1

(A) Time and frequency plots of the three types of multisine stimulus given at 90% individual threshold level (EVS1: 4–8 Hz; EVS2: 50–100 Hz; EVS3: 100–150 Hz). (B) Placement of 27 EEG electrodes and PLV calculation. Hilbert transform is applied to the two signals to extract the instantaneous phases. The phase differences calculated at each time point are represented into unit vectors in the complex plane and PLV is computed to evaluate the spread of the distribution (Lachaux et al., 1999; Mormann et al., 2000). (C) The procedure to extract phase locking value (PLV) time series. For each subject, preprocessing steps were first applied to the raw EEG data in order to remove high-voltage stimulation artifacts as well as cardinal artifacts caused by eye movements [electrooculography (EOG)] or muscle movement. The cleaned data were bandpass filtered into four different frequency bands (theta: 4–8 Hz; alpha: 8–13 Hz; beta: 13–30 Hz; gamma: 30–45 Hz) and segmented into epochs. PLV between a pair of electrodes in each epoch was computed to generate the time series, and its mean, variability, and sample entropy were calculated. Each subject has a 1 × p vector for the mean, variability and sample entropy (p = 1,404 = 351 pairs × 4 frequency bands).

FIGURE 2

Non-zero features selected by sparse discriminant analysis (SDA). SDA was applied to the mean, variability and entropy data sets, respectively, to discriminate the PDMOFF and HC groups. The non-zero weights in the sparse discriminant vectors are presented in the scalp maps. (A) Weights for the 17 selected features from the mean PLV data set. (B) Weights for the 12 selected features from the PLV variability data set. (C) Weights for the 17 selected features from the PLV entropy data set.

FIGURE 3

(A) Group comparison of the discriminant component obtained from the SDA. The discriminant components were obtained by multiplying the discriminant vectors to the data sets from the sham condition. Bars and error bars indicate group means and s.e. Significant P-values from one-way ANOVA with post hoc Tukey’s HSD test are indicated (^∗∗P < 0.01; ^∗∗∗P < 0.001). (B) Pearson correlations with clinical scores. The PLV variability and entropy of the PDMOFF subjects are significantly correlated with UPDRS2 and disease duration, respectively.

FIGURE 4

Effects of EVS on the PLV mean. The PLV mean values in the sham condition are identical to those in Figure 3A. The PLV mean values in the stimulation (60 s) and post-stimulation period (20 s) were obtained in the same manner by multiplying the discriminant vector to the corresponding data sets. In each row, from the left, the results for the PDMOFF (blue), PDMON (green), and HC (gray) groups are presented in each panel. Significant P-values from repeated measures ANOVA with post hoc Tukey’s HSD test are indicated (^∗P < 0.05; ^∗∗P < 0.01; ^∗∗∗P < 0.001). (A) EVS1 effects. (B) EVS2 effects. (C) EVS3 effects.

FIGURE 5

Effects of EVS on the PLV variability. The PLV variability values in the sham condition are identical to those in Figure 3A. Descriptions for the arrangement of the plots and statistical significance are same as in the Figure 4. (A) EVS1 effects. (B) EVS2 effects. (C) EVS3 effects.

FIGURE 6

Effects of EVS on the PLV entropy. The PLV entropy values in the sham condition are identical to those in Figure 3A. Descriptions for the arrangement of the plots and statistical significance are same as in the Figure 4. (A) EVS1 effects. (B) EVS2 effects. (C) EVS3 effects.