A novel vibrotactile system for stimulating the glabrous skin of awake freely behaving rats during operant conditioning

Decoding sensorimotor information from somatosensory cortex by flexible epicorticalμECoG arrays in unrestrained behaving rats

Objective.Brain-computer interfaces (BCI) are promising for severe neurological conditions and there are ongoing efforts to develop state-of-the-art neural interfaces, hardware, and software tools. We tested the potential of novel reduced graphene oxide (rGO) electrodes implanted epidurally over the hind limb representation of the primary somatosensory (S1) cortex of rats, and compared them to commercial platinum-iridium (Pt-Ir) 16-channel electrodes (active site diameter: 25μm).Approach.Motor and somatosensory information was decoded offline from microelectrocorticography (μECoG) signals recorded while unrestrained rats performed a simple behavioral task: pressing a lever and the subsequent vibrotactile stimulation of the glabrous skin at three displacement amplitude levels and at two sinusoidal frequencies.μECoG data were initially analyzed by standard time-frequency methods. Next, signal powers of oscillatory bands recorded from multiple electrode channels were used as features for sensorimotor classification by a machine learning algorithm.Main results.Both electrode types performed quite well and similar to each other for predicting the motor interval and the presence of the vibrotactile stimulus. Average accuracies were relatively lower for predicting 3-class vibrotactile frequency and 4-class amplitude level by both electrode types.Significance.Given some confounding factors during the free movement of rats, the results show that both sensory and motor information can be recorded reliably from the hind limb area of S1 cortex by usingμECoG arrays. The chronic use of novel rGO electrodes was demonstrated successfully. The hind limb area may be convenient for the future evaluation of new tools in neurotechnology, especially those for bidirectional BCIs.

Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays

AIM OF THE STUDY

Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms.

MATERIALS AND METHODS

16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and µECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel µECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such.

RESULTS

The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': -0.11) had higher bias for the right lever than Rat 1 (B'': - 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine.

CONCLUSION

According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.

Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex

Decoding of sensorimotor information is essential for brain-computer interfaces (BCIs) as well as in normal functioning organisms. In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 µm, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), overlapping also with the associated representation in the primary motor cortex (M1). Single-/multi-unit average spike rate (R_d) within the stimulus analysis window was used as the predictor of the stimulus state and the behavioral response at each trial based on a Bayesian network model. Due to high neural and psychophysical response variability for each rat and also across subjects, mean R_d was not correlated with hit and false alarm rates. Despite the fluctuations in the neural data, the Bayesian model for each rat generated moderately good accuracy (0.60-0.90) and good class prediction scores (recall, precision, F1) and was also tested with subsets of data (e.g. regular vs. fast spike groups). It was generally observed that the models were better for rats with lower psychophysical performance (lower sensitivity index A'). This suggests that Bayesian inference and similar machine learning techniques may be especially helpful during the training phase of BCIs or for rehabilitation with neuroprostheses.

Psychophysical detection and learning in freely behaving rats: a probabilistic dynamical model for operant conditioning

We present a stochastic learning model that combines the essential elements of Hebbian and Rescorla-Wagner theories for operant conditioning. The model was used to predict the behavioral data of rats performing a vibrotactile yes/no detection task. Probabilistic nature of learning was implemented by trial-by-trial variability in the random distributions of associative strengths between the sensory and the response representations. By using measures derived from log-likelihoods (corrected Akaike and Bayesian information criteria), the proposed model and its subtypes were compared with each other, and with previous models in the literature, including reinforcement learning model with softmax rule and drift diffusion model. The main difference between these models was the level of stochasticity which was implemented as associative variation or response selection. The proposed model with subject-dependent variance coefficient (SVC) and with trial-dependent variance coefficient (TVC) resulted in better trial-by-trial fits to experimental data than the other tested models based on information criteria. Additionally, surrogate data were simulated with estimated parameters and the performance of the models were compared based on psychophysical measures (A': non-parametric sensitivity index, hits and false alarms on receiver operating characteristics). Especially the TVC model could produce psychophysical measures closer to those of the experimental data than the alternative models. The presented approach is novel for linking psychophysical response measures with learning in a yes/no detection task, and may be used in neural engineering applications.

Effects of basal forebrain stimulation on the vibrotactile responses of neurons from the hindpaw representation in the rat SI cortex

Basal forebrain (BF) cholinergic system is important for attention and modulates sensory processing. We focused on the hindpaw representation in rat primary somatosensory cortex (S1), which receives inputs related to mechanoreceptors identical to those in human glabrous skin. Spike data were recorded from S1 tactile neurons (n = 87) with (ON condition: 0.5-ms bipolar current pulses at 100 Hz; amplitude 50 μA, duration 0.5 s at each trial) and without (OFF condition) electrical stimulation of BF in anesthetized rats. We expected that prior activation of BF would induce changes in the vibrotactile responses of neurons during sinusoidal (5, 40, and 250 Hz) mechanical stimulation of the glabrous skin. The experiment consisted of sequential OFF-ON conditions in two-time blocks separated by 30 min to test possible remaining effects. Average firing rates (AFRs) and vector strengths of spike phases (VS) were analyzed for different neuron types [regular spiking (RS) and fast spiking (FS)] in different cortical layers (III-VI). Immediate effect of BF activation was only significant by increasing synchronization to 5-Hz vibrotactile stimulus within the second block. Regardless of frequency, ON-OFF paired VS differences were significantly higher in the second block compared to the first, more prominent for RS neurons, and in general for neurons in layers III and VI. No such effects could be found on AFRs. The results suggest that cholinergic activation induces some changes in the hindpaw area, enabling relatively higher increases in synchronization to vibrotactile inputs with subsequent BF modulation. In addition, this modulation depends on neuron type and layer, which may be related to detailed projection pattern from BF.

Real-Time Performance of a Tactile Neuroprosthesis on Awake Behaving Rats

With the advancement of electrode and equipment technology, neuroprosthetics have become a promising alternative to partially compensate for the loss of sensorimotor function in amputees and patients with neurological diseases. Cortical neural interfaces are suitable especially for spinal cord injuries and amyotrophic lateral sclerosis. Although considerable success has been achieved in the literature by spike decoding of motor signals from the human brain, somatosensory feedback is essential for better motor control, interaction with objects, and the embodiment of prosthetic devices. In this paper, we present a tactile neuroprosthesis for rats based on intracortical microstimulation (ICMS). The rats wore mechanically-isolated boots covered with tactile sensors while performing a psychophysical detection task. The vibrotactile stimuli were measured by the artificial sensors and by using a real-time processor, this information was converted to electrical current pulses for ICMS. Some parameters of the real-time processor algorithm were specific to individual rats and were based on psychometric equivalence functions established earlier. Rats could detect the effects of the vibrotactile stimuli better (i.e., higher sensitivity indices) when the tactile neuroprosthesis was switched on compared to the boot only condition during active movement. In other words, the rats could decode the tactile information embedded in ICMS and use that in a behaviorally relevant manner. The presented animal model without peripheral nerve injury or amputation is also a promising tool to test various hardware and software components of neuroprosthetic systems in general.

Psychophysical correspondence between vibrotactile intensity and intracortical microstimulation for tactile neuroprostheses in rats

OBJECTIVE

Recent studies showed that intracortical microstimulation (ICMS) generates artificial sensations which can be utilized as somatosensory feedback in cortical neuroprostheses. To mimic the natural psychophysical response, ICMS parameters are modulated according to psychometric equivalence functions (PEFs). PEFs match the intensity levels of ICMS and mechanical stimuli, which elicit equal detection probabilities, but they typically do not include the frequency as a control variable. We aimed to establish frequency-dependent PEFs for vibrotactile stimulation of the glabrous skin and ICMS in the primary somatosensory cortex of awake freely behaving rats.

APPROACH

We collected psychometric data for vibrotactile and ICMS detection at three stimulation frequencies (40, 60 and 80 Hz). The psychometric data were fitted with a model equation of two independent variables (stimulus intensity and frequency) and four subject-dependent parameters. For each rat, we constructed a separate PEF which was used to estimate the ICMS current amplitude for a given displacement amplitude and frequency. The ICMS frequency was set equal to the vibrotactile frequency. We validated the PEFs in a modified task which included randomly selected probe trials presented either with a vibrotactile or an ICMS stimulus, and also at frequencies and intensity levels not tested before.

MAIN RESULTS

The PEFs were generally successful in estimating the ICMS current intensities (no significant differences between vibrotactile and ICMS trials in Kolmogorov-Smirnov tests). Specifically, hit rates from both trial conditions were significantly correlated in 86% of the cases, and 52% of all data had perfect match in linear regression.

SIGNIFICANCE

The psychometric correspondence model presented in this study was constructed based on surface functions which define psychophysical detection probability as a function of stimulus intensity and frequency. Therefore, it may be used for the real-time modulation of the frequency and intensity of ICMS pulses in somatosensory neuroprostheses.