Imaging cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG beta rhythm

Abstract

AbstractAccurate mapping of cortical engagement during mental imagery or cognitive tasks remains a challenging brain-imaging problem with immediate relevance to the development of brain-computer interfaces (BCI). We analyzed data from fourteen individuals who performed cued motor imagery, mental arithmetic, or silent word generation tasks during MEG recordings. During the motor imagery task, participants imagined the movement of either both hands (HANDS) or both feet (FEET) after the appearance of a static visual cue. During the cognitive task, participants either mentally subtracted two numbers that were displayed on the screen (SUB) or generated words starting with a letter cue that was presented (WORD). The MEG recordings were denoised using a combination of spatiotemporal filtering, the elimination of noisy epochs, and ICA decomposition. Cortical source power in the beta-band (17–25 Hz) was estimated from the selected temporal windows using a frequency-resolved beamforming method applied to the sensor-level MEG signals. The task-related cortical engagement was inferred from beta power decrements within non-overlapping 400 ms temporal windows between 400 and 2800 ms after cue presentation relative to a baseline 400 ms temporal window before cue onset. We estimated the significance of these power changes within each parcel of the Desikan-Killiany atlas using a non-parametric permutation test at the group level. During the HANDS and FEET movement-imagery conditions, beta-power decreased in premotor and motor areas, consistent with a robust engagement of these cortical regions during motor imagery. During WORD and SUB tasks, beta-power decrements signaling cortical engagement were lateralized to left hemispheric brain areas that are expected to engage in language and arithmetic processing within the temporal (superior temporal gyrus), parietal (supramarginal gyrus), and (inferior) frontal regions. A leave-one-subject-out cross-validation using a support vector machine (SVM) applied to beta power decrements across brain parcels yielded accuracy rates of 74% and 68% respectively, for classifying motor-imagery (HANDS-vs-FEET) and cognitive (WORD-vs-SUB) tasks. From the motor-versus-nonmotor contrasts, accuracy rates of 85% and 80% respectively, were observed for HAND-vs-WORD and HAND-vs-SUB. A multivariate Gaussian process classifier (GPC) provided an accuracy rate of 60% for a four-way (HANDS-FEET-WORD-SUB) classification problem. The regions identified by both SVM and GPC classification weight maps were largely consistent with the source modeling findings. Within-subject correlations of beta-decrements during the two task sessions provided insights into the level of engagement by individual subjects and showed moderately high correlations for most subjects. Our results show that it is possible to map the dynamics of cortical engagement during mental processes in the absence of dynamic sensory stimuli or overt behavioral outputs using task-related beta-power decrements. The ability to do so with the high spatiotemporal resolution afforded by MEG could potentially help better characterize the physiological basis of motor or cognitive impairments in neurological disorders and guide strategies for neurorehabilitation.