Mapping the topological organisation of beta oscillations in motor cortex using MEG

Abstract

The spatial topology of the human motor cortex has been well studied, particularly using functional Magnetic Resonance Imaging (fMRI) which allows spatial separation of haemodynamic responses arising from stimulation of different body parts, individual digits and even spatially separate areas of the same digit. However, the spatial organisation of electrophysiological responses, particularly neural oscillations (rhythmic changes in electrical potential across cellular assemblies) has been less well studied. Mapping the spatial signature of neural oscillations is possible using magnetoencephalography (MEG), however spatial differentiation of responses induced by movement of separate digits is a challenge, because the brain regions involved are separated by only a few millimetres. In this paper we first show, in simulation, how to optimise experimental design and beamformer spatial filtering techniques to increase the spatial specificity of MEG derived functional images. Combining this result with experimental data, we then capture the organisation of the post-movement beta band (13-30 Hz) oscillatory response to movement of digits 2 and 5 of the dominant hand, in individual subjects. By comparing these MEG results to ultra-high field (7T) fMRI, we also show significant spatial agreement between beta modulation and the blood oxygenation level dependent (BOLD) response. Our results show that, when using an optimised inverse solution and controlling subject movement (using custom fitted foam padding) the spatial resolution of MEG can be of order 3-5 mm. The method described offers exciting potential to understand better the cortical organisation of oscillations, and to probe such organisation in patient populations where those oscillations are known to be abnormal.

Keywords: Beamformer; Magnetoencephalography; Motor cortex; Post movement beta rebound; Spatial resolution; fMRI.

Fig. 1

Schematic of the simulation. A) The generation of an ellipsoid, approximately 1.5 cm below the brain surface, upon which all sources were simulated. B) Two sources generated in close proximity, and a 1-dimensional beamformer image generated along a line joining the two sources. C) Source timecourses were simulated such that each source was sequentially active, to mimic the experimental set-up used later.

Fig. 2

Simulation results. A) Beamformer projected power (top) and pseudo-z-image (bottom), for the three beamformer implementations (Single weight vector; single image (yellow). Single weight vector; two images (blue). Two weight vectors; Two images (red) – dashed lines show analytical images computed in the infinite integration limit). The case of high, medium and low SNR are shown in the left, centre, and right plots respectively. Sensor noise is Gaussian. B) Equivalent to (A) but the interference is taken from an empty room. C) Schematic diagram showing how spatial separation of two sources was calculated: the amplitude of the local minima between the peaks (points marked A, B and C) had to be less than 80% of the maximum peak height. D) Spatial resolution for the 3 different beamformer implementations. The left hand plot shows the case for Gaussian noise and the right hand plot shows the case for empty room noise.

Fig. 3

Paradigm design for the digit tapping experiment used in MEG (left) and fMRI (right).

Fig. 4

Example pseudo-T-statistical images, from a single run in a single subject. The spatial distribution of the PMBR measured following D2 movement is shown in red. The PMBR following D5 movement is shown in blue. Note, qualitatively, a spatial shift in the two responses with the representation of D5 appearing superior, as expected from the known organisation of the sensorimotor cortex.

Fig. 5

MEG experimental results: The spatial distribution of the PMBR response, shown for Subjects 1–3 in panels A–C respectively. In all cases, the spatial distributions of PMBR responses for D2 tapping are shown in red, for D5 tapping in blue. In the left hand panel, peak locations for all 8 runs are shown in each subject, projected onto axial, coronal and sagittal slices of the MRI. In the right hand panel, the average locations (across all 8 runs) are shown.

Fig. 6

The spatial relationship between the PMBR and the BOLD response. A) The spatial location of the 8 MEG derived peak locations of the PMBR are shown by red (D2) and blue (D5) circles. The grey and green overlays show the peak BOLD signal change in response to D2 and D5 respectively. All differential contrasts were thresholded at a z-score of 1.96, before further thresholding using the activation maps. B) The bar charts show the corresponding digit distance (i.e. MEG-D2-to-fMRI-D2 and MEG-D5-to-fMRI-D5) and alternate digit distances (i.e. MEG-D2-to-fMRI-D5 and MEG-D5-to-fMRI-D2); note as expected that the corresponding digit distance is smaller than the alternate digit distance in all subjects (although this is only significant in Subjects 1 and 2).

Fig. A1

A) Left hand column shows the mean location of the peak PMBR response for D2 (red) and D5 (blue) in the optimised covariance case. The centre column shows the mean location of the peak PMBR response for D2 (red) and D5 (blue) using a generic weights vector. The right hand column shows Euclidean separation of D2 and D5 cortical representations in both cases. Separate rows show three different subjects. B) Euclidean separation between D2 and D5, averaged across subjects, for the two different weights calculations.