Biology subjects

Francis, J. T.

Publications and source records attributed to Francis, J. T..

8 recordsLinked to original sources

bioRxiv2021

Similarities Between Somatosensory Cortical Responses Induced via Natural Touch and Microstimulation in the Ventral Posterior Lateral Thalamus in Macaques

Lost sensations, such as touch, could be restored by microstimulation (MiSt) along the sensory neural substrate. Such neuroprosthetic sensory information can be used as feedback from an invasive brain-machine interface (BMI) to control a robotic arm/hand, such that tactile and proprioceptive feedback from the sensorized robotic arm/hand is directly given to the BMI user. Microstimulation in the human somatosensory thalamus (Vc) has been shown to produce somatosensory perceptions. However, until recently, systematic methods for using thalamic stimulation to evoke naturalistic touch perceptions were lacking. We have recently presented rigorous methods for determining a mapping between ventral posterior lateral thalamus (VPL) MiSt, and neural responses in the somatosensory cortex (S1), in a rodent model (Choi et al., 2016; Choi and Francis, 2018). Our technique minimizes the difference between S1 neural responses induced by natural sensory stimuli and those generated via VPL MiSt. Our goal is to develop systems that know what MiSt will produce a given neural response and possibly a more natural "sensation." To date, our optimization has been conducted in the rodent model and simulations. Here we present data from simple non-optimized thalamic MiSt during peri-operative experiments, where we MiSt in the VPL of macaques with a somatosensory system more like humans. We implanted arrays of microelectrodes across the hand area of the macaque S1 cortex as well as in the VPL thalamus. Multi and single-unit recordings were used to compare cortical responses to natural touch and thalamic MiSt in the anesthetized state. Post stimulus time histograms were highly correlated between the VPL MiSt and natural touch modalities, adding support to the use of VPL MiSt towards producing a somatosensory neuroprosthesis in humans.

neuroscience↗

bioRxiv2021

Mirror Neurons are Modulated by Reward Expectation and Grip Force in the Sensorimotor Cortices (S1, M1, PMd, PMv)

Mirror Neurons (MNs) respond similarly when primates make, or observe, grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1-2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units, however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1-2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.

neuroscience↗

bioRxiv2020

Noise Correlation is Modulated by Reward Expectation in the Primary Motor Cortex Bilaterally During Manual and Observational Tasks in Primates

Reward modulation is represented in the motor cortex (M1) and could be used to implement more accurate decoding models to improve brain computer interfaces (BCIs) (Zhao et al. 2018). Analyzing trial-to-trial noise-correlations between neural units in the presence of rewarding (R) and non-rewarding (NR) stimuli adds to our understanding of cortical network dynamics. We utilized Pearsons correlation coefficient to measure shared variability between simultaneously recorded units (32 - 112) and found significantly higher noise-correlation and positive correlation between the populations signal- and noise-correlation during NR trials as compared to R trials. This pattern is evident in data from two non-human primates (NHPs) during single-target center out reaching tasks, both manual and action observation versions. We conducted mean matched noise-correlation analysis in order to decouple known interactions between event triggered firing rate changes and neural correlations. Isolated reward discriminatory units demonstrated stronger correlational changes than units unresponsive to reward firing rate modulation, however the qualitative response was similar, indicating correlational changes within the network as a whole can serve as another information channel to be exploited by BCIs that track the underlying cortical state, such as reward expectation, or attentional modulation. Reward expectation and attention in return can be utilized with reinforcement learning towards autonomous BCI updating.

neuroscience↗

bioRxiv2019

Divisive Normalization by Valence and Motivational Intensity in the Primary Sensorimotor Cortices (PMd, M1 and S1)

Our brains ability to represent vast amounts of information, such as continuous ranges of reward spanning orders of magnitude, with limited dynamic range neurons, may be possible due to normalization. Recently our group and others have shown that the sensorimotor cortices are sensitive to reward value. Here we ask if psychological affect causes normalization of the sensorimotor cortices by modulating valence and motivational intensity. We had two non-human primate (NHP) subjects (one male bonnet macaque and one female rhesus macaque) make visually cued grip-force movements while simultaneously cueing the level of possible reward if successful, or timeout punishment, if unsuccessful. We recorded simultaneously from 96 electrodes in each the following: caudal somatosensory, rostral motor, and dorsal premotor cortices (cS1, rM1, PMd). We utilized several normalization models for valence and motivational intensity in all three regions. We found three types of divisive normalized relationships between neural activity and the representation of valence and motivation, linear, sigmodal, and hyperbolic. The hyperbolic relationships resemble receptive fields in psychological affect space, where a unit is susceptible to a small range of the valence/motivational space. We found that these cortical regions have both strong valence and motivational intensity representations.

neuroscience↗

bioRxiv2018

Reward Modulates Local Field Potentials, Spiking Activity and Spike-Field Coherence in the Primary Motor Cortex

Reward modulation of the primary motor cortex (M1) could be exploited in developing an autonomously updating brain-machine interface (BMI) based on a reinforcement learning architecture. In order to understand the multifaceted effects of reward on M1 activity, we investigated how neural spiking, oscillatory activities and their functional interactions are modulated by conditioned stimuli related reward expectation. To do so, local field potentials (LFPs) and singleunit/multi-unit activities were recorded simultaneously and bilaterally from M1 cortices while five non-human primates performed cued center-out reaching or grip force tasks either manually using their right arm/hand or observed passively. We found that reward expectation influenced the strength of alpha (8-14 Hz) power, alpha-gamma comodulation, alpha spike-field coherence, and firing rates in general in M1. Furthermore, we found that an increase in alpha-band power was correlated with a decrease in neural spiking activity, that firing rates were highest at the trough of the alpha-band cycle and lowest at the peak of its cycle. These findings imply that alpha oscillations modulated by reward expectation have an influence on spike firing rate and spike timing during both reaching and grasping tasks in M1. These LFP, spike, and spike-field interactions could be used to follow the M1 neural state in order to enhance BMI decoding (An et al., 2018; Zhao et al., 2018).\n\nSignificance StatementKnowing the subjective value of performed or observed actions is valuable feedback that can be used to improve the performance of an autonomously updating brain-machine interface (BMI). Reward-related information in the primary motor cortex (M1) may be crucial for more stable and robust BMI decoding (Zhao et al., 2018). Here, we present how expectation of reward during motor tasks, or simple observation, is represented by increased spike firing rates in conjunction with decreased alpha (8-14 Hz) oscillatory power, alpha-gamma comodulation, and alpha spike-field coherence, as compared to non-rewarding trials. Moreover, a phasic relation between alpha oscillations and firing rates was observed where firing rates were found to be lowest and highest at the peak and trough of alpha oscillations, respectively.

neuroscience↗

bioRxiv2018

Motor Cortex Encodes A Value Function Consistent With Reinforcement Learning

Temporal difference reinforcement learning (TDRL) accurately models associative learning observed in animals, where they learn to associate outcome predicting environmental states, termed conditioned stimuli (CS), with the value of outcomes, such as rewards, termed unconditioned stimuli (US). A component of TDRL is the value function, which captures the expected cumulative future reward from a given state. The value function can be modified by changes in the animals knowledge, such as by the predictability of its environment. Here we show that primary motor cortical (M1) neurodynamics reflect a TD learning process, encoding a state value function and reward prediction error in line with TDRL. M1 responds to the delivery of reward, and shifts its value related response earlier in a trial, becoming predictive of an expected reward, when reward is predictable due to a CS. This is observed in tasks performed manually or observed passively, as well as in tasks without an explicit CS predicting reward, but simply with a predictable temporal structure, that is a predictable environment. M1 also encodes the expected reward value associated with a set of CS in a multiple reward level CS-US task. Here we extend the Microstimulus TDRL model, reported to accurately capture RL related dopaminergic activity, to account for M1 reward related neural activity in a multitude of tasks.\n\nSignificance statementThere is a great deal of agreement between aspects of temporal difference reinforcement learning (TDRL) models and neural activity in dopaminergic brain centers. Dopamine is know to be necessary for sensorimotor learning induced synaptic plasticity in the motor cortex (M1), and thus one might expect to see the hallmarks of TDRL in M1, which we show here in the form of a state value function and reward prediction error during. We see these hallmarks even when a conditioned stimulus is not available, but the environment is predictable, during manual tasks with agency, as well as observational tasks without agency. This information has implications towards autonomously updating brain machine interfaces as others and we have proposed and published on.

neuroscience↗

bioRxiv2018

Near Perfect Neural Critic from Motor Cortical Activity Toward an Autonomously Updating Brain Machine Interface

We are developing an autonomously updating brain machine interface (BMI) utilizing reinforcement learning principles. One aspect of this system is a neural critic that determines reward expectations from neural activity. This critic is then used to update a BMI decoder towards an improved performance from the users perspective. Here we demonstrate the ability of a neural critic to classify trial reward value given activity from the primary motor cortex (M1), using neural features from single/multi units (SU/MU), and local field potentials (LFPs) with prediction accuracies up to 97% correct. A nonhuman primate subject conducted a cued center out reaching task, either manually, or observationally. The cue indicated the reward value of a trial. Features such as power spectral density (PSD) of the LFPs and spike-field coherence (SFC) between SU/MU and corresponding LFPs were calculated and used as inputs to several classifiers. We conclude that hybrid features of PSD and SFC show higher classification performance than PSD or SFC alone (accuracy was 92% for manual tasks, and 97% for observational). In the future, we will employ these hybrid features towards our autonomously updating BMI.

neuroscience↗

bioRxiv2017

Layer-Specific Molecular Dynamics of PKMζ in Sensorimotor Cortex During Acquisition and Storage of Procedural Memory

Procedural memories, such as for riding a bicycle, can be maintained without practice for long periods of time and are thought to be supported by the persistent reorganization of sensorimotor cortices (S1/M1). Whereas enhanced synaptic strength and structural changes accompany the learning of motor tasks, the persistent molecular modifications that store long-term procedural memories within specific layers of sensorimotor cortex have not been identified. The persistent increase in the autonomously active, atypical PKC isoform, PKM{zeta}, is a putative molecular mechanism for maintaining enhanced synaptic strength during long-term potentiation (LTP) and several forms of long-term memory. Here we examine whether persistent increases in PKM{zeta} store long-term memory for a reaching task in rat sensorimotor cortex that could reveal the sites of procedural memory storage. Perturbing PKM{zeta} synthesis with PKM{zeta} -antisense oligodeoxynucleotides or blocking atypical PKC activity with zeta inhibitory peptide (ZIP) in S1/M1 disrupts and erases the maintenance of long-term motor memories. Only memories that are maintained without daily reinforcement are affected, indicating atypical PKCs (via ZIP) and PKM{zeta} specifically (via antisense) stores consolidated long-term procedural memories. Analysis of changes in the amount of PKM{zeta} in S1/M1 reveals PKM{zeta} increases in layers II/III and V of both S1 and M1 cortices as performance improves to an asymptote during training. After storage for 1 month without reinforcement, the increase in M1 layer V but not other layers persists without decrement. Thus, the sustained increases in PKM{zeta} reveal that the persistent molecular changes storing long-term procedural memory are localized to the descending output layer of primary motor cortex.

neuroscience↗