A versatile toolbox for studying cortical physiology in primates

Neuroelectrophysiology-compatible electrolytic lesioning

Lesion studies have historically been instrumental for establishing causal connections between brain and behavior. They stand to provide additional insight if integrated with multielectrode techniques common in systems neuroscience. Here, we present and test a platform for creating electrolytic lesions through chronically implanted, intracortical multielectrode probes without compromising the ability to acquire neuroelectrophysiology. A custom-built current source provides stable current and allows for controlled, repeatable lesions in awake-behaving animals. Performance of this novel lesioning technique was validated using histology from ex vivo and in vivo testing, current and voltage traces from the device, and measurements of spiking activity before and after lesioning. This electrolytic lesioning method avoids disruptive procedures, provides millimeter precision over the extent and submillimeter precision over the location of the injury, and permits electrophysiological recording of single-unit activity from the remaining neuronal population after lesioning. This technique can be used in many areas of cortex, in several species, and theoretically with any multielectrode probe. The low-cost, external lesioning device can also easily be adopted into an existing electrophysiology recording setup. This technique is expected to enable future causal investigations of the recorded neuronal population's role in neuronal circuit function, while simultaneously providing new insight into local reorganization after neuron loss.

Flexible modeling of large-scale neural network stimulation: electrical and optical extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX)

Computational models that predict effects of neural stimulation can be used as a preliminary tool to inform in-vivo research, reducing the costs, time, and ethical considerations involved. However, current models do not support the diverse neural stimulation techniques used in-vivo, including the expanding selection of electrodes, stimulation modalities, and stimulation paradigms. To develop a more comprehensive software, we created several extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX), the MATLAB-based neural stimulation tool from Newcastle University. VERTEX simulates input currents in a large population of multi-compartment neurons within a small cortical slice to model electric field stimulation, while recording local field potentials (LFPs) and spiking activity. Our extensions to its existing electric field stimulation framework include multiple pairs of parametrically defined electrodes and biphasic, bipolar stimulation delivered at programmable delays. To support the growing use of optogenetic approaches for targeted neural stimulation, we introduced a feature that models optogenetic stimulation through an additional VERTEX input function that converts irradiance to currents at optogenetically responsive neurons. Finally, we added extensions to allow complex stimulation protocols including paired-pulse, spatiotemporal patterned, and closed-loop stimulation. We demonstrated our novel features using VERTEX's built-in functionalities, illustrating how these extensions can be used to efficiently and systematically test diverse, targeted, and individualized stimulation patterns.

A large-scale optogenetic neurophysiology platform for improving accessibility in NHP behavioral experiments

Optogenetics has been a powerful scientific tool for two decades, yet its integration with non-human primate (NHP) electrophysiology has been limited due to several technical challenges. These include a lack of electrode arrays capable of supporting large-scale and long-term optical access, inaccessible viral vector delivery methods for transfection of large regions of cortex, a paucity of hardware designed for large-scale patterned cortical illumination, and inflexible designs for multi-modal experimentation. To address these gaps, we introduce a highly accessible platform integrating optogenetics and electrophysiology for behavioral and neural modulation with neurophysiological recording in NHPs. We employed this platform in two rhesus macaques and showcased its capability of optogenetically disrupting reaches, while simultaneously monitoring ongoing electrocorticography activity underlying the stimulation-induced behavioral changes. The platform exhibits long-term stability and functionality, thereby facilitating large-scale electrophysiology, optical imaging, and optogenetics over months, which is crucial for translationally relevant multi-modal studies of neurological and neuropsychiatric disorders.

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. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling 'In-the-Wild'. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.

Early Intervention with Electrical Stimulation Reduces Neural Damage After Stroke in Non-human Primates

Ischemic stroke is a neurological condition that results in significant mortality and long-term disability for adults, creating huge health burdens worldwide. For stroke patients, acute intervention offers the most critical therapeutic opportunity as it can reduce irreversible tissue injury and improve functional outcomes. However, currently available treatments within the acute window are highly limited. Although emerging neuromodulation therapies have been tested for chronic stroke patients, acute stimulation is rarely studied due to the risk of causing adverse effects related to ischemia-induced electrical instability. To address this gap, we combined electrophysiology and histology tools to investigate the effects of acute electrical stimulation on ischemic neural damage in non-human primates. Specifically, we induced photothrombotic lesions in the monkey sensorimotor cortex while collecting electrocorticography (ECoG) signals through a customized neural interface. Gamma activity in ECoG was used as an electrophysiological marker to track the effects of stimulation on neural activation. Meanwhile, histological analysis including Nissl, cFos, and microglial staining was performed to evaluate the tissue response to ischemic injury. Comparing stimulated monkeys to controls, we found that theta-burst stimulation administered directly adjacent to the ischemic infarct at 1 hour post-stroke briefly inhibits peri-infarct neuronal activation as reflected by decreased ECoG gamma power and cFos expression. Meanwhile, lower microglial activation and smaller lesion volumes were observed in animals receiving post-stroke stimulation. Together, these results suggest that acute electrical stimulation can be used safely and effectively as an early stroke intervention to reduce excitotoxicity and inflammation, thus mitigating neural damage and enhancing stroke outcomes.

Large-scale multimodal surface neural interfaces for primates

Deciphering the function of neural circuits can help with the understanding of brain function and treating neurological disorders. Progress toward this goal relies on the development of chronically stable neural interfaces capable of recording and modulating neural circuits with high spatial and temporal precision across large areas of the brain. Advanced innovations in designing high-density neural interfaces for small animal models have enabled breakthrough discoveries in neuroscience research. Developing similar neurotechnology for larger animal models such as nonhuman primates (NHPs) is critical to gain significant insights for translation to humans, yet still it remains elusive due to the challenges in design, fabrication, and system-level integration of such devices. This review focuses on implantable surface neural interfaces with electrical and optical functionalities with emphasis on the required technological features to realize scalable multimodal and chronically stable implants to address the unique challenges associated with nonhuman primate studies.

Demonstration of an Optimized Large-scale Optogenetic Cortical Interface for Non-human Primates

Optogenetics is a powerful neuroscientific tool which allows neurons to be modulated by optical stimulation. Despite widespread optogenetic experimentation in small animal models, optogenetics in non-human primates (NHPs) remains a niche field, particularly at the large scales necessary for multi-regional neural research. We previously published a large-scale, chronic optogenetic cortical interface for NHPs which was successful but came with a number of limitations. In this work, we present an optimized interface which improves upon the stability and scale of our previous interface while using more easily replicable methods to increase our system's availability to the scientific community. Specifically, we (1) demonstrate the long-term (~3 months) optical access to the brain achievable using a commercially-available transparent artificial dura with embedded electrodes, (2) showcase large-scale optogenetic expression achievable with simplified (magnetic resonance-free) surgical techniques, and (3) effectively modulated the expressing areas at large scales (~1 cm²) by light emitting diode (LED) arrays assembled in-house.