Nicholas G. Hatsopoulos’s research while affiliated with University of Chicago and other places

Objective: The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain–computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control. Approach: Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder. Main results: We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g. reaching vs wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex. Significance: These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.

Objective. As brain–computer interface (BCI) research advances, many new applications are being developed. Tasks can be performed in different virtual environments, and whether a BCI user can switch environments seamlessly will influence the ultimate utility of a clinical device. Approach. Here we investigate the importance of the immersiveness of the virtual environment used to train BCI decoders on the resulting decoder and its generalizability between environments. Two participants who had intracortical electrodes implanted in their precentral gyrus used a BCI to control a virtual arm, both viewed immersively through virtual reality goggles and at a distance on a flat television monitor. Main results. Each participant performed better with a decoder trained and tested in the environment they had used the most prior to the study, one for each environment type. The neural tuning to the desired movement was minimally influenced by the immersiveness of the environment. Finally, in further testing with one of the participants, we found that decoders trained in one environment generalized well to the other environment, but the order in which the environments were experienced within a session mattered. Significance. Overall, experience with an environment was more influential on performance than the immersiveness of the environment, but BCI performance generalized well after accounting for experience. Clinical Trial: NCT01894802

The primary motor cortex (M1) is known to be spatially organized in terms of muscles and body parts, notwithstanding some overlap. However, the classical view is largely static, neglecting potential representational changes on a fast time scale. To address potential representational dynamics, we probed the mutual information between M1 signals recorded across the cortical sheet and electromyography (EMG) activity from a set of muscles while a macaque monkey performed planar reaches. Here, we demonstrated that the spatial organization of the M1 encoding was in fact quite dynamic throughout the course of a reaching movement such that a given cortical site maximally encoded different muscles at different times. Despite these rapid representational changes, a proximal-to-distal gradient of the upper limb representation was preserved particularly close to movement onset. Representation was most stable close to movement onset, with characteristic topographic maps, possibly serving functional needs. This study bridges the important gap between flexible motor encoding and static motor maps, emphasizing the importance of considering space and time together in motor representation studies.

The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and in particular the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of single-unit activity recorded from M1 of a macaque monkey during an instructed delayed-reach task. We explore the hypothesis that the observed dynamics of neural covariation with the direction of motion emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs. We constrain the strength both of synaptic connectivity and of external input parameters by using the data as well as an external input minimization cost. Our analysis suggests that the observed patterns of covariance are shaped by external inputs that are tuned to neurons' preferred directions during movement preparation, and they are dominated by strong direction-specific recurrent connectivity during movement execution, in agreement with recent experimental findings on the relationship between motor-cortical and motor-thalamic activity, both before and during movement execution. We also demonstrate that the manner in which single-neuron tuning properties rearrange over time can explain the level of orthogonality of preparatory and movement-related subspaces. We predict that the level of orthogonality is small enough to prevent premature movement initiation during movement preparation; however, it is not zero, which allows the network to encode a stable direction of motion at the population level without direction-specific external inputs during movement execution.

Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have clearance from the United States Federal Drug Administration (FDA) for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost nine years of experiments, totaling 17 rhesus macaques (Macaca mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization. Significance. Understanding longevity and reliability of microelectrode array recordings allows researchers to set expectations and plan experiments accordingly and maximize the amount of high-quality data gathered. Our results suggest that one can expect chronic unit recordings to last at least two years, with the possibility for arrays to last the better part of a decade.

Marmosets are an increasingly important model system for neuroscience in part due to genetic tractability and enhanced cortical accessibility, due to a lissencephalic neocortex. However, many of the techniques generally employed to record neural activity in primates inhibit the expression of natural behaviors in marmosets precluding neurophysiological insights. To address this challenge, we developed methods for recording neural population activity in unrestrained marmosets across multiple ethological behaviors, multiple brain states, and over multiple years. Notably, our flexible methodological design allows for replacing electrode arrays and removal of implants providing alternative experimental endpoints. We validate the method by recording sensorimotor cortical population activity in freely moving marmosets across their natural behavioral repertoire and during sleep. HIGHLIGHTS – Simultaneous and chronic wireless neural population recordings in multiple freely moving marmosets – Neural recording approach enables studies of natural repertoire of behaviors and sleep – Methyl-methacrylate free surgical approach designed to promote biocompatibility and longitudinal success of the implant – Modular headstage configuration requires minimal daily animal handling for daily neural recordings – Alternative experimental endpoints: implant removal, healing, and electrode array replacement

Generally behavioral neuroscience studies of the common marmoset employ adaptations of well-established training methods used with macaque monkeys. However, in many cases these approaches do not readily generalize to marmosets indicating a need for alternatives. Here we present the development of one such alternate: a platform for semiautomated, voluntary in-home cage behavioral training that allows for the study of naturalistic behaviors. We describe the design and production of a modular behavioral training apparatus using CAD software and digital fabrication. We demonstrate that this apparatus permits voluntary behavioral training and data collection throughout the marmoset’s waking hours with little experimenter intervention. Furthermore, we demonstrate the use of this apparatus to reconstruct the kinematics of the marmoset’s upper limb movement during natural foraging behavior. NEW & NOTEWORTHY The study of marmosets in neuroscience has grown rapidly and presents unique challenges. We address those challenges with an innovative platform for semiautomated, voluntary training that allows marmosets to train throughout their waking hours with minimal experimenter intervention. We describe the use of this platform to capture upper limb kinematics during foraging and to expand the opportunities for behavioral training beyond the limits of traditional training sessions. This flexible platform can easily incorporate other tasks.

Dexterous hand control requires not only a sophisticated motor system but also a sensory system to provide tactile and proprioceptive feedback. To date, the study of the neural basis of proprioception in cortex has focused primarily on reaching movements, at the expense of hand-specific behaviors such as grasp. To fill this gap, we record both the time-varying hand kinematics and the neural activity evoked in somatosensory and motor cortices as monkeys grasp a variety of different objects. We find that neurons in somatosensory cortex, as well as in motor cortex, preferentially track postures of multi-joint combinations spanning the entire hand. This contrasts with neural responses during reaching movements, which preferentially track movement kinematics of the arm rather than its postural configuration. These results suggest different representations of arm and hand movements likely adapted to suit the different functional roles of these two effectors.

A complex action can be described as the composition of a set of elementary movements. While both kinematic and dynamic elements have been proposed to compose complex actions, the structure of movement decomposition and its neural representation remain unknown. Here, we examined movement decomposition by modeling the temporal dynamics of neural populations in the primary motor cortex of macaque monkeys performing forelimb reaching movements. Using a hidden Markov model, we found that global transitions in the neural population activity are associated with a consistent segmentation of the behavioral output into acceleration and deceleration epochs with directional selectivity. Single cells exhibited modulation of firing rates between the kinematic epochs, with abrupt changes in spiking activity timed with the identified transitions. These results reveal distinct encoding of acceleration and deceleration phases at the level of M1, and point to a specific pattern of movement decomposition that arises from the underlying neural activity. A similar approach can be used to probe the structure of movement decomposition in different brain regions, possibly controlling different temporal scales, to reveal the hierarchical structure of movement composition.