Cortical control of arm movements: a dynamical systems perspective.

TL;DR: Evidence is provided that the language of dynamical systems can be used to parsimoniously link brain activity to sensorimotor computations through analysis of the geometry of neural states.

Abstract: Stroke disrupts movement control by damaging descending motor pathways, yet the cortical dynamics underlying recovery remain poorly defined. Using a non-human primate model of primary motor cortex injury with impaired reach-to-grasp control, we examined how dorsal premotor cortex (PMd) activity supports recovery. Specifically, we studied the interaction between beta activity (12-30 Hz), often linked to "idle" states, and execution-related ensemble co-firing quantified with dimensionality reduction. Stroke impaired the temporal separability between beta bursts and movement-related co-firing, leading to slower reaction times and reduced performance. Recovery was associated with increased separability, and during grasping, beta activity progressively declined with recovery. These results indicate that reliable transitions between high-beta idle and high co-firing execution states are important for movement control, whereas pathological beta intrusions during execution degrade performance. Importantly, low-frequency alternating current stimulation (ACS) via a ringtrode interface enhanced temporal separability and improved reach-to-grasp performance, highlighting a potential therapeutic strategy.

TL;DR: In this article , the authors identify the processes and brain areas involved in the ability to coordinate preparation of an upcoming action with ongoing execution and show that the bottleneck during simultaneous action control and preparation arises at the level of stimulus identification and action selection, whereas movement planning can unfold concurrently with execution without interference between the two processes.

TL;DR: The direction of movement was found to be uniquely predicted by the action of a population of motor cortical neurons that can be monitored during various tasks, and similar measures in other neuronal populations could be of heuristic value where there is a neural representation of variables with vectorial attributes.

TL;DR: This work shows that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions, and proposes an alternative theory based on stochastic optimal feedback control, which emerges naturally from this framework.

TL;DR: How noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise are highlighted, and noise's potential benefits are discussed.

TL;DR: The results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.