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

Towards population coding principles in the primate premotor and parietal grasping network

As humans, the only way for us to interact with the world around us is by utilizing our highly trained motor system. Therefore, understanding how the brain generates movement is essential to understanding all aspects of human behavior. Despite the importance the motor system, the manner in which the brain prepares and executes movements, especially grasping movements, is still unclear. In this thesis I undertake a number of electrophysiological and computational experiments on macaque monkeys, primates showing similar grasping behavior to humans, to shed light on how grasping movements are planned and executed across distributed brain regions in both parietal and premotor cortices. Through these experiments, I reveal how the use of large-scale electrophysiological recording of hundreds of neurons simultaneously in primates allows the investigation of network computational principles essential for grasping, and I develop a series of analytical techniques for dissecting the large data sets collected from these experiments. In chapter 2.1 I show how large-scale parallel recordings can be leveraged to make behavioral predictions on single trials. The methods used to extract single-trial predictions varied in their performance, but population-based methods provided the most consistent and meaningful interpretation of the data. In addition, the success of these behavioral predictions could be used to make inferences about how areas differ in their contribution to preparation of grasping movements. It was found that while reaction time could be predicted from the population activity of either area, performance was significantly higher using the data from premotor cortex, suggesting that population activity in premotor cortex may have a more direct effect on behavior. In chapter 2.2 I show how preparation and movement intermingle and interact with one another on the continuum between immediate and withheld movement. Our population-based and dimensionality reduction techniques enable interpretation of the data, even when single neuron tuning properties are highly temporally and functionally complex. Activity in parietal cortex stabilizes during the memory period, while it continues to evolve in premotor cortex, revealing a decodable signature of time. Furthermore, activity during movement initiation clusters into two groups, movements initiated as fast as possible and movements from memory, showing how a state shift likely occurs on the border between these two types of actions. In chapter 2.3 I show that the question of how motor cortex controls movement is an ongoing issue in the field. I address crucial details about recent methodology used to extract rotational dynamics in motor cortex. I show how a simple neural network simulation and novel statistical test reveal properties of motor cortex not examined before, showing how models of movement generation can be essential tools in adding perspective to empirical results. Finally, in chapter 2.4 I show how the specificity of hand use can be used as a tool to dissociate levels of abstraction in the visual to motor transformation in parietal and premotor cortex. While preparatory activity is mostly hand-invariant in parietal cortex, activity in premotor cortex dissociates the intended hand use well before movement. Importantly, we show how appropriate dimensionality reduction techniques can disentangle the effects of multiple task parameters and find latent dimensions consistent between areas and animals. Together, the results of my experiments reinforce the importance of seeing the motor system not as a collection of individually tuned neurons, but as a dynamic network of neurons continuously acting together to produce the complex and flexible behavior we observe in all primates

Mirror Activity in the Macaque Motor System

Mirror neurons (MirNs) within ventral premotor cortex (PMv) and primary motor cortex (M1), including pyramidal tract neurons (PTNs) projecting to the spinal cord, modulate their activity during both the execution and observation of motor acts. However, movement is not produced in the latter condition, and mirror responses cannot be explained by lowlevel muscle activity. Relatively reduced activity in M1 during observation may help to suppress movement. Here, we examined the extent to which activity at different stages of action observation reflects grasp representation and suppression of movement across multiple levels of the mirror system in monkeys and humans. We recorded MirNs in M1 and F5 (rostral PMv), including identified PTNs, in two macaque monkeys as they performed, observed, and withheld reach-to-grasp actions. Time-varying population activity was more distinct between execution and observation in M1 than in F5, and M1 activity in the lead-up to the observation of movement onset shared parallels with movement withholding activity. In separate experiments, modulation of short-latency responses evoked in hand muscles by pyramidal tract stimulation revealed modest grasp-specific facilitation at the spinal level during grasp observation. This contrasted with a relative suppression of excitability prior to observed movement onset or when monkeys simply withheld movement. Additional cortical recording experiments examined how contextual factors, such as observing to imitate, observing while engaged in action, or observation with reduced visual information, modulated mirror activity in M1 and F5. Finally, single-pulse transcranial magnetic stimulation (TMS) in healthy human volunteers was used to examine changes in corticospinal excitability (CSE) during action observation and withholding. Overall, the results reveal distinctions in the profile of mirror activity across premotor and motor areas. While F5 maintains a more abstract representation of grasp independent of the acting agent, a balance of excitation and inhibition in motor cortex and spinal circuitry during action observation may support a flexible dissociation between initiation of grasping actions and representation of observed grasp

Deep Learning for real-time neural decoding of grasp

Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this context, this work starts from a specific pre-existing dataset of neural recordings from monkey motor cortex and presents a Deep Learning-based approach to the decoding of neural signals for grasp type classification. Specifically, we propose here an approach that exploits LSTM networks to classify time series containing neural data (i.e., spike trains) into classes representing the object being grasped. The main goal of the presented approach is to improve over state-of-the-art decoding accuracy without relying on any prior neuroscience knowledge, and leveraging only the capability of deep learning models to extract correlations from data. The paper presents the results achieved for the considered dataset and compares them with previous works on the same dataset, showing a significant improvement in classification accuracy, even if considering simulated real-time decoding