Parabrachial Nucleus Activity in Nociception and Pain in Awake Mice.

The parabrachial nuclear complex (PBN) is a nexus for aversion and for the sensory and affective components of pain perception. We have previously shown that during chronic pain PBN neurons in anesthetized rodents have amplified activity. We report a method to record from PBN neurons of behaving, head-restrained mice while applying reproducible noxious stimuli. We find that both spontaneous and evoked activity are higher in awake animals compared with urethane anesthetized mice. Fiber photometry of calcium responses from calcitonin-gene-related peptide-expressing PBN neurons demonstrates that these neurons respond to noxious stimuli. In both males and females with neuropathic or inflammatory pain, responses of PBN neurons remain amplified for at least 5 weeks, in parallel with increased pain metrics. We also show that PBN neurons can be rapidly conditioned to respond to innocuous stimuli after pairing with noxious stimuli. Finally, we demonstrate that changes in PBN neuronal activity are correlated with changes in arousal, measured as changes in pupil area.SIGNIFICANCE STATEMENT The parabrachial complex is a nexus of aversion, including pain. We report a method to record from parabrachial nucleus neurons of behaving mice while applying reproducible noxious stimuli. This allowed us to track parabrachial activity over time in animals with neuropathic or inflammatory pain. It also allowed us to show that the activity of these neurons correlates with arousal states and that these neurons can be conditioned to respond to innocuous stimuli.

An exploratory data analysis method for identifying brain regions and frequencies of interest from large-scale neural recordings.

High-resolution whole brain recordings have the potential to uncover unknown functionality but also present the challenge of how to find such associations between brain and behavior when presented with a large number of regions and spectral frequencies. In this paper, we propose an exploratory data analysis method that sorts through a massive quantity of multivariate neural recordings to quickly extract a subset of brain regions and frequencies that encode behavior. This approach combines existing tools and exploits low-rank approximation of matrices without a priori selection of regions and frequency bands for analysis. In detail, the spectral content of neural activity across all frequencies of each recording contact is computed and represented as a matrix. Then, the rank-1 approximation of the matrix is computed using singular value decomposition and the associated singular vectors are extracted. The temporal singular vector, which captures the salient features of the spectrogram, is then correlated to the trial-varying behavioral signal. The distribution of correlations for each brain region is efficiently computed and used to find a subset of regions and frequency bands of interest for further examination. As an illustration, we apply this approach to a data set of local field potentials collected using stereoelectroencephalography from a human subject performing a reaching task. Using the proposed procedure, we produced a comprehensive set of brain regions and frequencies related to our specific behavior. We demonstrate how this tool can produce preliminary results that capture neural patterns related to behavior and aid in formulating data-driven hypotheses, hence reducing the time it takes for any scientist to transition from the exploratory to the confirmatory phase.

The role of nonmotor brain regions during human motor control.

Neural prostheses have generally relied on signals from cortical motor regions to control reaching movements of a robotic arm. However, little work has been done in exploring the involvement of nonmotor cortical and associative regions during motor tasks. In this study, we identify regions which may encode direction during planning and movement of a center-out motor task. Local field potentials were collected using stereoelectroencephalography (SEEG) from nine epilepsy patients implanted with multiple depth electrodes for clinical purposes. Spectral analysis of the recorded data was performed using nonparametric statistical techniques to identify regions that may encode direction of movements during the motor task. The analysis revealed several nonmotor regions; including the right insular cortex, right temporal pole, right superior parietal lobule, and the right lingual gyrus, that encode directionality before and after movement onset. We observed that each of these regions encode direction in different frequency bands. This preliminary study suggests that nonmotor regions may be useful in assisting in neural prosthetic control.

Nonmotor regions encode path-related information during movements.

Sensorimotor control and the involvement of motor brain regions has been extensively studied, but the role nonmotor brain regions play during movements has been overlooked. This is particularly due to the difficulty of recording from multiple regions in the brain during motor control. In this study, we utilize stereoelectroencephalography (SEEG) recording techniques to explore the role nonmotor brain areas have on the way we move. Nine humans were implanted with SEEG depth electrodes for clinical purposes, which rendered access to local field potential (LFP) activity in deep and peripheral nonmotor structures. Participants performed fast and slow arm reaching movements using a robotic manipulandum. In this study, we explored whether neural activity in a given nonmotor brain structure correlated to movement path metrics including: path length, path deviation, and path speed. Statistical analysis revealed correlations between averaged neural activity in middle temporal gyrus, supramarginal gyrus, and fusiform gyrus and our path metrics both within and across the subjects. Furthermore, we split trials across subjects into two groups: one group consisted of trials with high values of each path metric and the other with low values. We then found significant differences in LFP power in specific frequency bands (e.g. beta) during movement between each group. These results suggest that nonmotor regions may dynamically encode path-related information during movement.

Parallel Force/Position Control of an Epicardial Parallel Wire Robot.

Gene therapies for heart failure have emerged in recent years, yet they lack an effective method for minimally invasive, uniform delivery. To address this need we developed a minimally invasive parallel wire robot for epicardial interventions. Accurate and safe interventions using this device require control of force in addition to injector position. Accounting for the nonidealities of the device design, however, yields nonlinear and underconstrained statics. This work solves these equations and demonstrates the efficacy of using this information in a parallel control scheme, which is shown to provide superior positioning compared to a position-only controller.

Toward hybrid force/position control for the Cerberus epicardial robot.

Gene therapies have emerged as a promising treatment for congestive heart failure, yet they lack a method for minimally invasive, uniform delivery. To address this need we developed Cerberus, a minimally invasive parallel wire robot for cardiac interventions. Prior work on Cerberus was limited to controlling the device using only position feedback. In order to ensure safety for both the patient and the device, as well as to improve the performance of the device, this paper presents work on enhancing the existing system with force feedback capabilities. By modeling the statics of the system and developing a tension distribution optimization technique, existing position control schemes were modified to a hybrid force/position controller. Experimental results show that using a hybrid force-position control scheme as opposed to position decreases positioning error by 38%.

Auto-Calibration for a Planar Epicardial Wire Robot.

Gene therapies have emerged as a promising treatment for congestive heart failure, yet they lack a method for minimally invasive, uniform delivery. To address this need we developed Cerberus, a minimally invasive parallel wire robot for cardiac interventions. Prior work on controlling the movement of Cerberus required accurate knowledge of device geometry. In order to determine the geometry of the device in vivo, this paper presents work on developing an auto-calibration procedure to measure the geometry of the robot using force sensors to move injector. The presented auto-calibration routine is able to identify the shape of the device to within 0.5 mm and 0.9°.