Neuron-level dynamics of oscillatory network structure and markerless tracking of kinematics during grasping

Oscillatory synchrony is proposed to play an important role in flexible sensory-motor transformations. Thereby, it is assumed that changes in the oscillatory network structure at the level of single neurons lead to flexible information processing. Yet, how the oscillatory network structure at the neuron-level changes with different behavior remains elusive. To address this gap, we examined changes in the fronto-parietal oscillatory network structure at the neuron-level, while monkeys performed a flexible sensory-motor grasping task. We found that neurons formed separate subnetworks in the low frequency and beta bands. The beta subnetwork was active during steady states and the low frequency network during active states of the task, suggesting that both frequencies are mutually exclusive at the neuron-level. Furthermore, both frequency subnetworks reconfigured at the neuron-level for different grip and context conditions, which was mostly lost at any scale larger than neurons in the network. Our results, therefore, suggest that the oscillatory network structure at the neuron-level meets the necessary requirements for the coordination of flexible sensory-motor transformations. Supplementarily, tracking hand kinematics is a crucial experimental requirement to analyze neuronal control of grasp movements. To this end, a 3D markerless, gloveless hand tracking system was developed using computer vision and deep learning techniques. 2021-11-3

Classification of phases of hand grasp task by the extraction of miniature compound nerve action potentials (mCNAPs)

Interfacing with the nervous system to restore functional motor activity is a promising therapy to augment the classical surgical approaches to treating peripheral nerve injuries. Despite the advances in electrode microelectronics engineering, the challenge of extracting information from injured nerves to help restore motor function remains unsolved. Here we used waveform feature extraction and clustering techniques to identify a discrete set of events in intraneural recordings of the median nerve in a non-human primate (NHP) during grasping tasks. This analysis allowed the classification of the different phases of hand grasping. The waveform features were found to be significantly different for each phase of grasping. Since these waveforms can be seen as the minimal signal components that result from the activation of a group of nerve fibers, we denominated them miniature compound nerve action potentials (mCNAPs). The correlation between mCNAPs and the different stages of movement can be utilized in the near future to design high-performance neuroprosthetic therapies