Electrophysiology publications

Brain-Machine Interfaces (BMIs) can be used to restore function in people living with paralysis. Current BMIs require extensive calibration that increase the set-up times and external inputs for decoder training that may be difficult to produce in paralyzed individuals. Both these factors have presented challenges in transitioning the technology from research environments to activities of daily living (ADL). For BMIs to be seamlessly used in ADL, these issues should be handled with minimal external input thus reducing the need for a technician/caregiver to calibrate the system. Reinforcement Learning (RL) based BMIs are a good tool to be used when there is no external training signal and can provide an adaptive modality to train BMI decoders. However, RL based BMIs are sensitive to the feedback provided to adapt the BMI. In actor-critic BMIs, this feedback is provided by the critic and the overall system performance is limited by the critic accuracy. In this work, we developed an adaptive BMI that could handle inaccuracies in the critic feedback in an effort to produce more accurate RL based BMIs. We developed a confidence measure, which indicated how appropriate the feedback is for updating the decoding parameters of the actor. The results show that with the new update formulation, the critic accuracy is no longer a limiting factor for the overall performance. We tested and validated the system onthree different data sets: synthetic data generated by an Izhikevich neural spiking model, synthetic data with a Gaussian noise distribution, and data collected from a non-human primate engaged in a reaching task. All results indicated that the system with the critic confidence built in always outperformed the system without the critic confidence. Results of this study suggest the potential application of the technique in developing an autonomous BMI that does not need an external signal for training or extensive calibration.

Improving performance in motor skill acquisition is proposed to be supported by tuning of neural networks. To address this issue we investigated changes of phase-amplitude cross-frequency coupling (paCFC) in neuronal networks during motor performance improvement. We recorded intracranially from subdural electrodes (electrocorticogram; ECoG) from 6 patients who learned 3 distinct motor tasks requiring coordination of finger movements with an external cue (serial response task, auditory motor coordination task, go/no-go). Performance improved in all subjects and all tasks during the first block and plateaued in subsequent blocks. Performance improvement was paralled by increasing neural changes in the trial-to-trial paCFC between theta ([Formula: see text]; 4-8 Hz) phase and high gamma (HG; 80-180 Hz) amplitude. Electrodes showing this covariation pattern (Pearson's r ranging up to .45) were located contralateral to the limb performing the task and were observed predominantly in motor brain regions. We observed stable paCFC when task performance asymptoted. Our results indicate that motor performance improvement is accompanied by adjustments in the dynamics and topology of neuronal network interactions in the [Formula: see text] and HG range. The location of the involved electrodes suggests that oscillatory dynamics in motor cortices support performance improvement with practice.

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

Dysregulated endoplasmic reticulum (ER) calcium (Ca(2+)) signaling is reported to play an important role in Alzheimer disease (AD) pathogenesis. The role of ER Ca(2+) release channels, the ryanodine receptors (RyanRs), has been extensivelys tudied in AD models and RyanR expression and activity are upregulated in the brains of various familial AD (FAD) models.The objective of this study was to utilize a genetic approach to evaluate the importance of RyanR type 3 (RyanR3) in the context of AD pathology.The expression of RyanR3 was also elevated in hippocampus of APPPS1 mice (Thy1-APPKM670/671NL, Thy1-PS1L166P).In young (≤ 3 mo) APPPS1 mice, the deletion of RyanR3 increased hippocampal neuronal network excitability and accelerated AD pathology, leading to mushroom spine loss and increased amyloid accumulation. In contrast, deletion of RyanR3 in older APPPS1 mice (≥ 6 mo) rescued network excitability and mushroom spine loss, reduced amyloid plaque load and reduced spontaneous seizure occurrence.Our data suggests a dual role for RyanR3 in AD pathology. In young AD neurons, RyanR3 protects AD neurons from synaptic and network dysfunction. In older AD neurons, increased RyanR3 activity contributes to pathology. These results imply that blockade of RyanR3 may be beneficial for those in the later stages of the disease, but RyanR activators may be beneficial when used prior to disease onset or in its initial stages. Caffeine is an activator of RyanRs and our results may help to explain a complex epidemiological connection between coffee consumption in mid-life and risk of AD development in old age.

Here we propose a methodology to analyze volumetric electrical activity of neuronal masses in the somatosensory barrel field of Wistar rats. The key elements of the proposed methodology are a three-dimensional microelectrode array, which was customized by our group to observe extracellular recordings from an extended area of the barrel field, and a novel method for the current source density analysis. By means of this methodology, we were able to localize single barrels from their event-related responses to single whisker deflection. It was also possible to assess the spatiotemporal dynamics of neuronal aggregates in several barrels at the same time with the resolution of single neurons. We used simulations to study the robustness of our methodology to unavoidable physiological noise and electrode configuration. We compared the accuracy to reconstruct neocortical current sources with that obtained with a previous method. This constitutes a type of electrophysiological microscopy with high spatial and temporal resolution, which could change the way we analyze the activity of cortical neurons in the future.

The efficacy of spinal cord stimulators is dependent on the ability of the device to functionally activate targeted structures within the spinal cord, while avoiding activation of near-by non-targeted structures. In theory, these objectives can best be achieved by delivering electrical stimuli directly to the surface of the spinal cord. The current experiments were performed to study the influence of different stimulating electrode positions on patterns of spinal cord electrophysiological activation. A custom-designed spinal cord neurostimulator was used to investigate the effects of lead position and stimulus amplitude on cortical electrophysiological responses to spinal cord stimulation. Brain recordings were obtained from subdural grids placed in four adult sheep. We systematically varied the position of the stimulating lead relative to the spinal cord and the voltage delivered by the device at each position, and then examined how these variables influenced cortical responses. A clear relationship was observed between voltage and electrode position, and the magnitude of high gamma-band oscillations. Direct stimulation of the dorsal column contralateral to the grid required the lowest voltage to evoke brain responses to spinal cord stimulation. Given the lower voltage thresholds associated with direct stimulation of the dorsal column, and its possible impact on the therapeutic window, this intradural modality may have particular clinical advantages over standard epidural techniques now in routine use.

It was recently shown that rhythmic entrainment, long considered a human-specific mechanism, can be demonstrated in a selected group of bird species, and, somewhat surprisingly, not in more closely related species such as nonhuman primates. This observation supports the vocal learning hypothesis that suggests rhythmic entrainment to be a by-product of the vocal learning mechanisms that are shared by several bird and mammal species, including humans, but that are only weakly developed, or missing entirely, in nonhuman primates. To test this hypothesis we measured auditory event-related potentials (ERPs) in two rhesus monkeys (Macaca mulatta), probing a well-documented component in humans, the mismatch negativity (MMN) to study rhythmic expectation. We demonstrate for the first time in rhesus monkeys that, in response to infrequent deviants in pitch that were presented in a continuous sound stream using an oddball paradigm, a comparable ERP component can be detected with negative deflections in early latencies (Experiment 1). Subsequently we tested whether rhesus monkeys can detect gaps (omissions at random positions in the sound stream; Experiment 2) and, using more complex stimuli, also the beat (omissions at the first position of a musical unit, i.e. the 'downbeat'; Experiment 3). In contrast to what has been shown in human adults and newborns (using identical stimuli and experimental paradigm), the results suggest that rhesus monkeys are not able to detect the beat in music. These findings are in support of the hypothesis that beat induction (the cognitive mechanism that supports the perception of a regular pulse from a varying rhythm) is species-specific and absent in nonhuman primates. In addition, the findings support the auditory timing dissociation hypothesis, with rhesus monkeys being sensitive to rhythmic grouping (detecting the start of a rhythmic group), but not to the induced beat (detecting a regularity from a varying rhythm).

Adaptive behavior depends on an animal's ability to ignore uninformative stimuli, such as repeated presentations of the same stimulus, and, instead, detect informative, novel stimuli in its environment. The primate prefrontal cortex (PFC) is known to play a central role in this ability. However, the neural mechanisms underlying the ability to differentiate between repeated and novel stimuli are not clear. We hypothesized that the coupling between different frequency bands of the local field potential (LFP) underlies the PFC's role in differentiating between repeated and novel stimuli. Specifically, we hypothesized that whereas the presentation of a novel-stimulus induces strong cross-frequency coupling, repeated presentations of the same stimulus attenuates this coupling. To test this hypothesis, we recorded LFPs from the ventrolateral PFC (vPFC) of rhesus monkeys while they listened to a novel vocalization and repeated presentations of the same vocalization. We found that the cross-frequency coupling between the gamma-band amplitude and theta-band phase of the LFP was modulated by repeated presentations of a stimulus. During the first (novel) presentation of a stimulus, gamma-band activity was modulated by the theta-band phase. However, with repeated presentations of the same stimulus, this cross-frequency coupling was attenuated. These results suggest that cross-frequency coupling may play a role in the neural computations that underlie the differentiation between novel and repeated stimuli in the vPFC.