Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.

Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (μECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.

NSF DARE-Transforming modeling in neurorehabilitation: Four threads for catalyzing progress.

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation's Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation.

Assistive sensory-motor perturbations influence learned neural representations.

Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days.

Inserting a Neuropixels probe into awake monkey cortex: two probes, two methods.

Background: Neuropixels probes have revolutionized neurophysiological studies in the rodent, but inserting these probes through the much thicker primate dura remains a challenge.

New Methods: Here we describe two methods we have developed for the insertion of two types of Neuropixels probes acutely into the awake macaque monkey cortex. For the fine rodent probe (Neuropixels 1.

Invariant neural dynamics drive commands to control different movements.

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement.

Neural Plasticity in Sensorimotor Brain-Machine Interfaces.

Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance.

Quantifying the influence of stimulation protocols on neural network connectivity inference to optimize rapid network measurements.

Connectivity is key to understanding neural circuit computations. However, estimating in vivo connectivity using recording of activity alone is challenging. Issues include common input and bias errors in inference, and limited temporal resolution due to large data requirements.

Windows and periscopes into primate behavior.

Calcium imaging of neurons in monkeys making reaches is complicated by brain movements and limited by shallow imaging depth. In a pair of recent studies, Trautmann et al., 2021 and Bollimunta et al.

Multi-scale neural decoding and analysis.

. Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code.

Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human µECoG.

. Large channel count surface-based electrophysiology arrays (e.g.

Development of a neural interface for high-definition, long-term recording in rodents and nonhuman primates.

Long-lasting, high-resolution neural interfaces that are ultrathin and flexible are essential for precise brain mapping and high-performance neuroprosthetic systems. Scaling to sample thousands of sites across large brain regions requires integrating powered electronics to multiplex many electrodes to a few external wires. However, existing multiplexed electrode arrays rely on encapsulation strategies that have limited implant lifetimes.

A Modular Implant System for Multimodal Recording and Manipulation of the Primate Brain.

Neural circuitry can be investigated and manipulated using a variety of techniques, including electrical and optical recording and stimulation. At present, most neural interfaces are designed to accommodate a single mode of neural recording and/or manipulation, which limits the amount of data that can be extracted from a single population of neurons. To overcome these technical limitations, we developed a chronic, multi-scale, multi-modal chamber-based neural implant for use in non-human primates that accommodates electrophysiological recording and stimulation, optical manipulation, and wide-field imaging.

Monkey-MIMMS: Towards Automated Cellular Resolution Large- Scale Two-Photon Microscopy In The Awake Macaque Monkey.

The size and curvature of the macaque brain present challenges for two photon laser scanning microscopy (2P-LSM). General access to the cortex requires 5-axis positioning over a range of motion wider than existing designs offer. In addition, movement artifacts due to physiological pulsations and bodily movement present particular challenges.

Parsing learning in networks using brain-machine interfaces.

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain.

Development of semi-chronic microdrive system for large-scale circuit mapping in macaque mesolimbic and basal ganglia systems.

The development of novel neurotechnologies for treating refractory neuropsychiatry disorders depends on understanding and manipulating the dynamics of neural circuits across large-scale brain networks. The mesolimbic pathway plays an essential role in reward processing and mood regulation and disorders of this pathway underlie many neuropsychiatric disorders. Here, we present the design of a customized semi-chronic microdrive array that precisely targets the anatomical structures of non-human primate (NHP) mesolimbic and basal ganglia systems.

Rapid control and feedback rates enhance neuroprosthetic control.

Brain-machine interfaces (BMI) create novel sensorimotor pathways for action. Much as the sensorimotor apparatus shapes natural motor control, the BMI pathway characteristics may also influence neuroprosthetic control. Here, we explore the influence of control and feedback rates, where control rate indicates how often motor commands are sent from the brain to the prosthetic, and feedback rate indicates how often visual feedback of the prosthetic is provided to the subject.

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering.

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking.

High-performance brain-machine interface enabled by an adaptive optimal feedback-controlled point process decoder.

Brain-machine interface (BMI) performance has been improved using Kalman filters (KF) combined with closed-loop decoder adaptation (CLDA). CLDA fits the decoder parameters during closed-loop BMI operation based on the neural activity and inferred user velocity intention. These advances have resulted in the recent ReFIT-KF and SmoothBatch-KF decoders.

Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control.

Neuroplasticity may play a critical role in developing robust, naturally controlled neuroprostheses. This learning, however, is sensitive to system changes such as the neural activity used for control. The ultimate utility of neuroplasticity in real-world neuroprostheses is thus unclear.

Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces.

Closed-loop decoder adaptation (CLDA) is an emerging paradigm for both improving and maintaining online performance in brain-machine interfaces (BMIs). The time required for initial decoder training and any subsequent decoder recalibrations could be potentially reduced by performing continuous adaptation, in which decoder parameters are updated at every time step during these procedures, rather than waiting to update the decoder at periodic intervals in a more batch-based process. Here, we present recursive maximum likelihood (RML), a CLDA algorithm that performs continuous adaptation of a Kalman filter decoder's parameters.

Designing dynamical properties of brain-machine interfaces to optimize task-specific performance.

Brain-machine interfaces (BMIs) are dynamical systems whose properties ultimately influence performance. For instance, a 2-D BMI in which cursor position is controlled using a Kalman filter will, by default, create an attractor point that "pulls" the cursor to particular points in the workspace. If created unintentionally, such effects may not be beneficial for BMI performance.