Sensorimotor integration

Research presented in this dissertation has the central aim of applying a novel method of source localization called beamforming to neuromagnetic recordings for characterizing dynamic spatiotemporal activity of sensorimotor brain processes in subjects during rhythmic auditory stimulation, self-paced movement, and two sensorimotor coordination (synchronization and syncopation) tasks known to differentiate on the basis of behavioral stability. Each experimental condition was performed at different rates resulting in 26 experimental runs per subject. Event-related neural responses were recorded with a whole-head MEG system and characterized in terms of their phase-locked (evoked) and non-phase-locked (induced) activity within the brain using both whole-brain analysis and region of interest (ROI) analysis. The analysis of the auditory conditions revealed that neural activity within extraauditory areas throughout the brain, including sensorimotor cortex, is modulated by rhythmic auditory stimulation. Additionally, the temporal profile of this activity was markedly different between sensorimotor and auditory cortex, possibly revealing different physiological processes, entrained within a common network for representing isochronic auditory events. During self-paced movements cycle-by-cycle dynamics of induced neural activity was measured and consistent neuro-modulation in the form of event-related desynchronization (ERD) and synchronization (ERS) was observed at all rates investigated (0.25 - 1.75Hz). ERD and ERS modulations exhibited dynamic scaling properties on a cycle-by-cycle basis that depended on the period of movement. Activity in the beta- and mu-bands also exhibited patterns of phase locking between sensorimotor locations. Phase locking patterns exhibited abrupt decreases with increases in movement rate.

The neurophysiological signals that are recorded in EEG (electroencephalography) and MEG (magnetoencephalography) originate from current flow perpendicular to the cortical surface due to the columnar organization of pyramidal cells in the cortical gray matter. These locations and directions have been used as anatomical constraints for dipolar sources in estimations of neural activity from MEG recordings. Here we extend anatomically constrained beamforming to EEG, which requires a more sophisticated forward model than MEG due to the blurring of the electric potential at tissue boundaries, but in contrast to MEG, EEG can account for both tangential and radial sources. Using computed tomography (CT) scans we create a realistic three-layer head model consisting of tessellated surfaces representing the tissue boundaries cerebrospinal fluid-skull, skull-scalp and scalp-air. The cortical gray matter surface, the anatomical constraint for the source dipoles, is extracted from magnetic resonance imaging (MRI) scans. EEG beamforming is implemented in a set of simulated data and compared for three different head models: single sphere, multi-shell sphere and realistic geometry multi-shell model that employs a boundary element method. Beamformer performance is also analyzed and evaluated for multiple dipoles and extended sources (patches). We show that using anatomical constraints with the beamforming algorithm greatly reduces computation time while increasing the spatial accuracy of the reconstructed sources of neural activity. Using the spatial Laplacian instead of the electric potential in combination with beamforming further improves the spatial resolution and allows for the detection of highly correlated sources.