Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed

Abstract

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

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

“…Plastic changes taking place at this time should relate to various components of motor activity, such as integration of somato-sensory and visuospatial information into motor plans, and acquisition of new motor skills through procedural learning. This suggests that brain regions beyond the premotor and motor cortices take part in the process 89 . Notable among the cortical components of the re-organizing network is the insula, in which the existence of damage to almost half of its voxels was found to affect patients’ FMA-UL scores.…”

Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes

“…Above chance gesture decoding has been demonstrated in several studies [10], [11]. Breault et al [12] decoded movement speed with a correlation of 0.38 ± 0.03, and 70% ± 3% when decoding three speed levels.…”

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

“…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). Unlike these studies, we chose to classify using a baseline without movement to quantify the extent to which the brain areas recorded by sEEG were related to movement in general and focused on individual electrode contacts instead of patient-level data.…”