MEG-based detection and localization of perilesional dysfunction in chronic stroke

Abstract

Post-stroke impairment is associated not only with structural lesions, but also with dysfunction in surviving perilesional tissue. Previous studies using equivalent current dipole source localization of MEG/EEG signals have demonstrated a preponderance of slow-wave activity localized to perilesional areas. Recent studies have also demonstrated the utility of nonlinear analyses such as multiscale entropy (MSE) for quantifying neuronal dysfunction in a wide range of pathologies. The current study utilized beamformer-based reconstruction of signals in source space to compare spectral and nonlinear measures of electrical activity in perilesional and healthy cortices. Data were collected from chronic stroke patients and healthy controls, both young and elderly. We assessed relative power in the delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz) and beta (15-30 Hz) frequency bands, and also measured the nonlinear complexity of electrical activity using MSE. Perilesional tissue exhibited a general slowing of the power spectrum (increased delta/theta, decreased beta) as well as a reduction in MSE. All measures tested were similarly sensitive to changes in the posterior perilesional regions, but anterior perilesional dysfunction was detected better by MSE and beta power. The findings also suggest that MSE is specifically sensitive to electrophysiological dysfunction in perilesional tissue, while spectral measures were additionally affected by an increase in rolandic beta power with advanced age. Furthermore, perilesional electrophysiological abnormalities in the left hemisphere were correlated with the degree of language task-induced activation in the right hemisphere. Finally, we demonstrate that single subject spectral and nonlinear analyses can identify dysfunctional perilesional regions within individual patients that may be ideal targets for interventions with noninvasive brain stimulation.

Keywords: Beamformer source reconstruction; Chronic stroke; Magnetoencephalography; Time–frequency analysis.

Figures

Fig. 1

Voxel-wise contrasts from baseline (upper) and sentence listening (lower) periods of the task. T-test maps of relative power and MSE overlaid on top of an artificially darkened render representing the lesion distribution across patients; darker regions correspond to areas with greater lesion overlap. The maps contrast patients minus young controls (A), patients minus age-matched controls (B), and age-matched controls minus young controls (C). Active voxels are color coded according to the magnitude of difference and thresholded at p 

Fig. 2

Task activation maps. Pseudo-T maps (left panel) presenting an 8–30 Hz event related desynchronization during the sentence listening period of the task for patients (A), young controls (B) and age-matched controls (C). These maps represent the average magnitude of activation across subjects (effect size), and are thresholded at an arbitrary level of 0.5, to avoid differences related to different numbers of subjects in the groups. T-test maps (right panel) contrast patients minus young controls (E), patients minus age-matched controls, and (F), age-matched minus young controls. Active voxels are color coded according to the magnitude of difference and thresholded at p 

Fig. 3

Power spectra averaged across all voxels from the atlas-based (top), perilesional and contralateral (middle), and rolandic (bottom) ROIs. Spectra from homologous ROIs are plotted together: left hemisphere in blue and right hemisphere in red. Spectra are plotted separately for patients (A), young controls (B), and age-matched controls (C). Spectra from the perilesional and contralateral ROIs, averaged across all subjects, are plotted together (D): left hemisphere in blue and right hemisphere in red. The displayed lesion and perilesional ROIs are from the single sample subject. (E) Averaged spectra from the three groups are plotted together for the left (top) and right (bottom) rolandic ROIs. Shaded regions represent the standard error of the mean power estimate.

Fig. 4

The magnitude of contralateral homologous task activation (8–30 Hz ERD) plotted as a function of perilesional relative delta, theta, alpha, beta, and MSE (B). Pearson's correlations are presented with each figure. *Significant at p 

Fig. 5

Single subject maps. Maps of baseline relative theta (right) and MSE (left) are presented for a patient with a left hemisphere perisylvian lesion (A) and a patient with an anterior subcortical lesion (B). T-score maps were computed by comparing the single subject's value at each voxel vs. the mean and standard deviation of the young (upper) and age-matched (middle) control groups. Z-score maps (lower) were computed by comparing each voxel vs. the mean and standard deviation of all voxels in the individual's brain. Active voxels are color coded according to the magnitude of difference and thresholded at p 

Fig. 6

Bar graph illustrating the proportion of participants with significant perilesional changes in relative power and MSE compared to the age-matched controls (significant single subject t-scores; p