The role of nonmotor brain regions during human motor control

Abstract

Neural prostheses have generally relied on signals from cortical motor regions to control reaching movements of a robotic arm. However, little work has been done in exploring the involvement of nonmotor cortical and associative regions during motor tasks. In this study, we identify regions which may encode direction during planning and movement of a center-out motor task. Local field potentials were collected using stereoelectroencephalography (SEEG) from nine epilepsy patients implanted with multiple depth electrodes for clinical purposes. Spectral analysis of the recorded data was performed using nonparametric statistical techniques to identify regions that may encode direction of movements during the motor task. The analysis revealed several nonmotor regions; including the right insular cortex, right temporal pole, right superior parietal lobule, and the right lingual gyrus, that encode directionality before and after movement onset. We observed that each of these regions encode direction in different frequency bands. This preliminary study suggests that nonmotor regions may be useful in assisting in neural prosthetic control.

Shared and distinct voxel-based lesion-symptom mappings for spasticity and impaired movement in the hemiparetic upper limb

“…The insula is known to be involved in the execution of arm movements in the gravitational field 92 . The insular cortex and temporal pole were shown to encode movement direction in a center-out motor task 93 , and the middle temporal gyrus was shown to be involved in encoding path-related information during a center-out motor task 94 . The involvement of nonmotor regions such as insula, temporal pole and the middle and superior temporal gyri point to their role in adaptive remapping processes supporting recovery 95 .…”

Classification of Movement-Related Oscillations in sEEG Recordings with Machine Learning

“…Previous studies that have attempted to decode patterns in sEEG data related to movement without machine learning have had mixed results. Attempts to decode movement direction (Johnson et al, 2017) and path information (Breault et al, 2017) have found few modulated areas and correlations that reached statistical significance but were on the order of r =0.2. A study decoding movement speed that used a more complex classification method, least absolute shrinkage and selection operator (LASSO) linear regression, had a higher correlation of r= 0.4 on average but with a large range of correlations across subjects and a mean squared error greater than one (Breault et al, 2019).…”

Stereo-EEG recordings extend known distributions of canonical movement-related oscillations

“…These stereoelectroencephalography (sEEG) recordings provide an opportunity to determine the spatial extent of movement-related oscillations in humans because they sample a broad range of brain areas, including deeper regions. Previous sEEG studies found that movement direction (Johnson et al 2017) and movement path direction, deviation, and speed (Breault et al 2017) had statistically-significant correlations with beta power in a few brain areas, which were on the order of r = 0.2, reflecting modest predictive power at a single-trial resolution. Another study decoding movement speed that used a more complex classification method, least absolute shrinkage and selection operator linear regression, had a higher correlation of r = 0.4 on average using the entire sEEG electrode implantation montage for a patient, but had a large range of correlations across patients and a mean squared error greater than one (Breault et al 2019).…”