Biology subjects

Carmena, J. M.

Publications and source records attributed to Carmena, J. M..

10 recordsLinked to original sources

bioRxiv2023

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodent BMI has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from the primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguish between control types at the go cue and target acquisition, respectively. We also found effective connectivity from DLPFC[->]M1 throughout trials across both control types and Cd[->]M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.

neuroscience↗

bioRxiv2021

Diverse operant control of different motor cortex populations during learning

During motor learning, as well as during neuroprosthetic learning, animals learn to control motor cortex activity in order to generate behavior. Two different population of motor cortex neurons, intra-telencephalic (IT) and pyramidal tract (PT) neurons, convey the resulting cortical signals within and outside the telencephalon. Although a large amount of evidence demonstrates contrasting functional organization among both populations, it is unclear whether the brain can equally learn to control the activity of either class of motor cortex neurons. To answer this question, we used a Calcium imaging based brain-machine interface (CaBMI) and trained different groups of mice to modulate the activity of either IT or PT neurons in order to receive a reward. We found that animals learn to control PT neuron activity faster and better than IT neuron activity. Moreover, our findings show that the advantage of PT neurons is the result of characteristics inherent to this population as well as their local circuitry and cortical depth location. Taken together, our results suggest that motor cortex is optimized to control the activity of pyramidal track neurons, embedded deep in cortex, and relaying motor commands outside of the telencephalon.

neuroscience↗

bioRxiv2021

The brain uses invariant dynamics to generalize outputs across movements

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it re-uses 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. Using a brain-machine interface that transformed rhesus macaques motor cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low-dimensional, and critically, they align with the brain-machine interface, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements, and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

neuroscience↗

bioRxiv2021

Left Hemisphere Dominance for Bilateral Kinematic Encoding in the Human Brain

Neurophysiological studies in humans and non-human primates have revealed movement representations in both the contralateral and ipsilateral hemisphere. Inspired by clinical observations, we ask if this bilateral representation differs for the left and right hemispheres. Electrocorticography (ECoG) was recorded in human participants during an instructed-delay reaching task, with movements produced with either the contralateral or ipsilateral arm. Using a cross-validated kinematic encoding model, we found stronger bilateral encoding in the left hemisphere, an effect that was present during preparation and was amplified during execution. Consistent with this asymmetry, we also observed better across-arm generalization in the left hemisphere, indicating similar neural representations for right and left arm movements. Notably, these left hemisphere electrodes were centered over premotor and parietal regions. The more extensive bilateral encoding in the left hemisphere adds a new perspective to the pervasive neuropsychological finding that the left hemisphere plays a dominant role in praxis.

neuroscience↗

bioRxiv2021

Selective modulation of population dynamics during neuroprosthetic skill learning

Learning to control a brain-machine interface (BMI) is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-rated changes in cortical population dynamics within these groups compare. To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this coordination was primarily driven by changes in the direct subpopulation while the indirect subpopulation remained relatively stable. These findings indicate that motor cortex refines cortical dynamics throughout the entire network during learning, with a more pronounced effect in ensembles causally linked to behavior.

neuroscience↗

bioRxiv2020

Bi-directional Modeling Between Cross-Scale Neural Activity

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas the population brain activity has been studied in the micro-to meso-scale in building the connections between the dynamical patterns and the behaviors, such studies were often done at a single length scale and lacked an explanatory theory that identifies the neuronal origin across multiple scales. Here we introduce the NeuroBondGraph Network, a dynamical system incorporating both biological-inspired components and deep learning techniques to capture cross-scale dynamics that can infer and map the neural data from multiple scales. We demonstrated our model is not only 3.5 times more accurate than the popular sphere head model but also able to predict synchronous neural activity and extract correlated low-dimensional latent dynamics. We also showed that we can extend our methods to robustly predict held-out data across several weeks. The extracted effective connectivity agreed with the neuroanatomical hierarchy of motor control. Accordingly, the NeuroBondGraph Network opens the door to revealing comprehensive understanding of the brain computation, where network mechanisms of multi-scale communications are critical.

neuroscience↗

bioRxiv2020

Hybrid local and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals

Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. Using an instructed-delay reaching task in monkeys, we identified two components in population responses spanning PMd and M1. A "dedicated" component, which segregated activity at the level of individual units, emerged in PMd during preparation. It was most prominent following movement when M1 became strongly engaged, and principally involved the contralateral hemisphere. In contrast to recent reports, these dedicated signals solely accounted for divergence of arm-specific neural subspaces. The other "distributed" component mixed signals for each arm within units, and the subspace containing it did not discriminate between arms at any stage. The statistics of the population response suggest two functional layers of the cortical network: one that spans both hemispheres for supporting preparatory and ongoing processes, and another that is predominantly housed in the contralateral hemisphere and specifies unilateral output.

neuroscience↗

bioRxiv2020

Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque

A major effort is now underway across the brain sciences to identify, characterize and manipulate mesoscale neural circuits in order to elucidate the mechanisms underlying sensory perception, cognition and behavior. Optical imaging technologies, in conjunction with genetically encoded sensors and actuators, serve as important tools toward these goals, allowing access to large-scale genetically defined neuronal populations. In particular, one-photon miniature microscopes, coupled with genetically encoded calcium indicators and microendoscopic gradient-refractive index (GRIN) lenses, enable unprecedented readout of neural circuit dynamics in cortical and deep subcortical brain regions during active behavior in rodents. This has already led to breakthrough discoveries across a wide array of rodent brain regions and behaviors. However, in order to study the neural circuit mechanisms underlying more complex and clinically relevant human behaviors and cognitive functions, it is crucial to translate this technology to non-human primates. Here, we describe the first successful application of this technology in the rhesus macaque. We identified a viral strategy for robust expression of GCaMP, optimized a surgical protocol for microendoscope GRIN lens insertion, and created a chronic cranial chamber and lens mounting system for imaging in gyral cortex. Using these methods, we demonstrate the ability to perform plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from over 100 genetically-targeted neurons simultaneously in dorsal premotor cortex while the macaque performs a naturalistic motor reach task with the head unrestrained and freely moving. The recorded population of neurons exhibited calcium dynamics selective to the direction of reach, which we show can be used to decode the animals trial-by-trial motor behavior. Recordings were stable over several months, allowing us to longitudinally track large populations of individual neurons and monitor their relationship to motor behavior over time. Finally, we demonstrate the ability to conduct simultaneous, multi-site imaging in bilateral dorsal premotor cortices, offering an opportunity to study distributed networks underlying complex behavior and cognition. Together, this work establishes head-mounted microendoscopic calcium imaging in macaque as a powerful new approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease. HighlightsFirst demonstration of head-mounted microendoscopic calcium imaging in behaving macaque. Surgical protocols developed for preparing the animal for calcium imaging, including virus injections to express GCaMP and chronic implantation of a GRIN lens to enable optical access to gyral cortex. Proof of concept plug-and-play calcium imaging in behaving macaques with months long stable recording capability allowing populations of individual neurons to be tracked longitudinally. Bilateral calcium imaging from dorsal premotor cortex exhibited dynamics selective to the animals direction of reach and allowed decoding of the animals motor behavior

neuroscience↗

bioRxiv2019

Flexible Modulation of Neural Variance Facilitates Neuroprosthetic Skill Learning

One hallmark of natural motor control is the brains ability to adapt to perturbations ranging from temporary visual-motor rotations to paresis caused by stroke. These adaptations require modifications of learned neural patterns that can span the time-course of minutes to months. Previous work with brain-machine interfaces (BMI) has shown that over learning, neurons consolidate firing activity onto low-dimensional neural subspaces, and additional studies have shown that neurons require longer timescales to adapt to task perturbations that require neural activity outside of these subspaces. However, it is unclear how the motor cortex adapts alongside task changes that do not require modifications of the existing neural subspace over learning. To answer this question, five nonhuman primates were used in three BMI experiments, which allowed us to track how specific populations of neurons changed firing patterns as task performance improved. In each experiment, neural activity was transformed into cursor kinematics using decoding algorithms that were periodically readapted based on natural arm movements or visual feedback. We found that decoder changes caused neurons to increase exploratory-like patterns on within-day timescales without hindering previously consolidated patterns regardless of task performance. The flexible modulation of these exploratory patterns in contrast to relatively stable consolidated activity suggests a simultaneous exploration-exploitation strategy that adapts existing neural patterns during learning.

neuroscience↗

bioRxiv2018

A high-density carbon fiber neural recording array technology

Abstract.Objective: Microwire and Utah-style neural recording arrays are the predominant devices used for cortical neural recording, but the implanted electrodes cause a significant adverse biological response and suffer from well-studied performance degradation. Recent work has demonstrated that carbon fiber electrodes do not elicit this same adverse response, but these existing designs are not practically scalable to hundreds or thousands of recording sites. We present technology that overcomes these issues while additionally providing fine electrode pitch for spatial oversampling.\n\nApproach: We present a 32-channel carbon fiber monofilament-based intracortical neural recording array fabricated through a combination of bulk silicon microfabrication processing and microassembly. This device represents the first truly two-dimensional carbon fiber neural recording array. The density, channel count, and size scale of this array are enabled by an out-of-plane microassembly technique in which individual fibers are inserted through metallized and isotropically conductive adhesive-filled holes in an oxide-passivated microfabricated silicon substrate.\n\nMain results: Five-micron diameter fibers are spaced at a pitch of 38 microns, four times denser than state of the art one-dimensional arrays. The fine diameter of the carbon fibers affords both minimal cross-section and nearly three orders of magnitude greater lateral compliance than standard tungsten microwires. Typical 1 kHz impedances are on the order of hundreds of kiloohms, and successful in vivo recording is demonstrated in the motor cortex of a rat. 22 total units are recorded on 20 channels, with unit SNR ranging from 0.85 to 4.2.\n\nSignificance: This is the highest density microwire-style electrode array to date, and this fabrication technique is scalable to a larger number of electrodes and allows for the potential future integration of microelectronics. Large-scale carbon fiber neural recording arrays are a promising technology for reducing the inflammatory response and increasing the information density, particularly in neural recording applications where microwire arrays are already used.

bioengineering↗