Carlos E. Vargas-Irwin

Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex

Multiple-electrode arrays are valuable both as a research tool and as a sensor for neuromotor prosthetic devices, which could potentially restore voluntary motion and functional independence to paralyzed humans. Long-term array reliability is an important requirement for these applications. Here, we demonstrate the reliability of a regular array of 100 microelectrodes to obtain neural recordings from primary motor cortex (MI) of monkeys for at least three months and up to 1.5 years. We implanted Bionic (Cyberkinetics, Inc., Foxboro, MA) silicon probe arrays in MI of three Macaque monkeys. Neural signals were recorded during performance of an eight-direction, push-button task. Recording reliability was evaluated for 18, 35, or 51 sessions distributed over 83, 179, and 569 days after implantation, respectively, using qualitative and quantitative measures. A four-point signal quality scale was defined based on the waveform amplitude relative to noise. A single observer applied this scale to score signal quality for each electrode. A mean of 120 (/spl plusmn/17.6 SD), 146 (/spl plusmn/7.3), and 119 (/spl plusmn/16.9) neural-like waveforms were observed from 65-85 electrodes across subjects for all recording sessions of which over 80% were of high quality. Quantitative measures demonstrated that waveforms had signal-to-noise ratio (SNR) up to 20 with maximum peak-to-peak amplitude of over 1200 /spl mu/v with a mean SNR of 4.8 for signals ranked as high quality. Mean signal quality did not change over the duration of the evaluation period (slope 0.001, 0.0068 and 0.03; NS). By contrast, neural waveform shape varied between, but not within days in all animals, suggesting a shifting population of recorded neurons over time. Arm-movement related modulation was common and 66% of all recorded neurons were tuned to reach direction. The ability for the array to record neural signals from parietal cortex was also established. These results demonstrate that neural recordings that can provide movement related signals for neural prostheses, as well as for fundamental research applications, can be reliably obtained for long time periods using a monolithic microelectrode array in primate MI and potentially from other cortical areas as well.

Decoding Complete Reach and Grasp Actions from Local Primary Motor Cortex Populations

How the activity of populations of cortical neurons generates coordinated multijoint actions of the arm, wrist, and hand is poorly understood. This study combined multielectrode recording techniques with full arm motion capture to relate neural activity in primary motor cortex (M1) of macaques (Macaca mulatta) to arm, wrist, and hand postures during movement. We find that the firing rate of individual M1 neurons is typically modulated by the kinematics of multiple joints and that small, local ensembles of M1 neurons contain sufficient information to reconstruct 25 measured joint angles (representing an estimated 10 functionally independent degrees of freedom). Beyond showing that the spiking patterns of local M1 ensembles represent a rich set of naturalistic movements involving the entire upper limb, the results also suggest that achieving high-dimensional reach and grasp actions with neuroprosthetic devices may be possible using small intracortical arrays like those already being tested in human pilot clinical trials.

Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates

OBJECTIVE Brain-computer interfaces (BCIs) using chronically implanted intracortical microelectrode arrays (MEAs) have the potential to restore lost function to people with disabilities if they work reliably for years. Current sensors fail to provide reliably useful signals over extended periods of time for reasons that are not clear. This study reports a comprehensive retrospective analysis from a large set of implants of a single type of intracortical MEA in a single species, with a common set of measures in order to evaluate failure modes. APPROACH Since 1996, 78 silicon MEAs were implanted in 27 monkeys (Macaca mulatta). We used two approaches to find reasons for sensor failure. First, we classified the time course leading up to complete recording failure as acute (abrupt) or chronic (progressive). Second, we evaluated the quality of electrode recordings over time based on signal features and electrode impedance. Failure modes were divided into four categories: biological, material, mechanical, and unknown. MAIN RESULTS Recording duration ranged from 0 to 2104 days (5.75 years), with a mean of 387 days and a median of 182 days (n = 78). Sixty-two arrays failed completely with a mean time to failure of 332 days (median = 133 days) while nine array experiments were electively terminated for experimental reasons (mean = 486 days). Seven remained active at the close of this study (mean = 753 days). Most failures (56%) occurred within a year of implantation, with acute mechanical failures the most common class (48%), largely because of connector issues (83%). Among grossly observable biological failures (24%), a progressive meningeal reaction that separated the array from the parenchyma was most prevalent (14.5%). In the absence of acute interruptions, electrode recordings showed a slow progressive decline in spike amplitude, noise amplitude, and number of viable channels that predicts complete signal loss by about eight years. Impedance measurements showed systematic early increases, which did not appear to affect recording quality, followed by a slow decline over years. The combination of slowly falling impedance and signal quality in these arrays indicates that insulating material failure is the most significant factor. SIGNIFICANCE This is the first long-term failure mode analysis of an emerging BCI technology in a large series of non-human primates. The classification system introduced here may be used to standardize how neuroprosthetic failure modes are evaluated. The results demonstrate the potential for these arrays to record for many years, but achieving reliable sensors will require replacing connectors with implantable wireless systems, controlling the meningeal reaction, and improving insulation materials. These results will focus future research in order to create clinical neuroprosthetic sensors, as well as valuable research tools, that are able to safely provide reliable neural signals for over a decade.

Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.

Neural activity in motor cortex during reach and grasp movements shows modulations in a broad range of signals from single-neuron spiking activity (SA) to various frequency bands in broadband local field potentials (LFPs). In particular, spatiotemporal patterns in multiband LFPs are thought to reflect dendritic integration of local and interareal synaptic inputs, attentional and preparatory processes, and multiunit activity (MUA) related to movement representation in the local motor area. Nevertheless, the relationship between multiband LFPs and SA, and their relationship to movement parameters and their relative value as brain-computer interface (BCI) control signals, remain poorly understood. Also, although this broad range of signals may provide complementary information channels in primary (MI) and ventral premotor (PMv) areas, areal differences in information have not been systematically examined. Here, for the first time, the amount of information in SA and multiband LFPs was compared for MI and PMv by recording from dual 96-multielectrode arrays while monkeys made naturalistic reach and grasp actions. Information was assessed as decoding accuracy for 3D arm end point and grip aperture kinematics based on SA or LFPs in MI and PMv, or combinations of signal types across areas. In contrast with previous studies with ≤16 simultaneous electrodes, here ensembles of >16 units (on average) carried more information than multiband, multichannel LFPs. Furthermore, reach and grasp information added by various LFP frequency bands was not independent from that in SA ensembles but rather typically less than and primarily contained within the latter. Notably, MI and PMv did not show a particular bias toward reach or grasp for this task or for a broad range of signal types. For BCIs, our results indicate that neuronal ensemble spiking is the preferred signal for decoding, while LFPs and combined signals from PMv and MI can add robustness to BCI control.

Decoding 3-D Reach and Grasp Kinematics From High-Frequency Local Field Potentials in Primate Primary Motor Cortex

Intracortical microelectrode array recordings generate a variety of neural signals with potential application as control signals in neural interface systems. Previous studies have focused on single and multiunit activity (MUA), as well as low-frequency local field potentials (LFPs), but have not explored higher frequency (>200 Hz) LFPs. In addition, the potential to decode 3-D reach and grasp kinematics based on LFPs has not been demonstrated. Here, we use mutual information and decoding analyses to probe the information content about 3-D reaching and grasping of seven different LFP frequency bands in the range of 0.3-400 Hz. LFPs were recorded via 96-microelectrode arrays in primary motor cortex (M1) of two monkeys performing free reaching to grasp moving objects. Mutual information analyses revealed that higher frequency bands (e.g., 100-200 and 200-400 Hz) carried the most information about the examined kinematics. Furthermore, Kalman filter decoding revealed that broad-band high frequency LFPs, likely reflecting MUA, provided the best decoding performance as well as substantial accuracy in reconstructing reach kinematics, grasp aperture, and aperture velocity. These results indicate that LFPs, especially high frequency bands, could be useful signals for neural interfaces controlling 3-D reach and grasp kinematics.

Decoding grasp aperture from motor-cortical population activity

The direct neural control of external prosthetic devices such as robot hands requires the accurate decoding of neural activity representing continuous movement. This requirement becomes formidable when multiple degrees of freedom (DoFs) are to be controlled as in the case of the fingers of a robotic hand. In this paper a methodology is proposed for estimating grasp aperture using the spiking activity of multiple neurons recorded with an electrode array implanted in the arm/hand area of primary motor cortex (Ml). Grasp aperture provides a reasonable approximation to the hand configuration during grasping tasks, while it offers a large reduction in the number of DoFs that must be estimated. A family of state space models with hidden variables is used to decode each finger grasp aperture with respect to the thumb from a population of motor-cortical neurons. The firing rates of multiple neurons in Ml were found to be correlated with grasp aperture and were used as inputs to our decoding algorithm. The proposed decoding architecture was evaluated off-line by decoding pre-recorded neural activity from monkey motor cortex during a natural grasping task. We found that our model was able to accurately reconstruct finger grasp aperture from a small population of cells. This demonstrates the first decoding of continuous grasp aperture from Ml suggesting the feasibility for neural control of prosthetic robotic hands from neuronal population signals

Optogenetically induced spatiotemporal gamma oscillations and neuronal spiking activity in primate motor cortex.

Transient gamma-band (40-80 Hz) spatiotemporal patterns are hypothesized to play important roles in cortical function. Here we report the direct observation of gamma oscillations as spatiotemporal waves induced by targeted optogenetic stimulation, recorded by intracortical multichannel extracellular techniques in macaque monkeys during their awake resting states. Microelectrode arrays integrating an optical fiber at their center were chronically implanted in primary motor (M1) and ventral premotor (PMv) cortices of two subjects. Targeted brain tissue was transduced with the red-shifted opsin C1V1(T/T). Constant (1-s square pulses) and ramp stimulation induced narrowband gamma oscillations during awake resting states. Recordings across 95 microelectrodes (4 × 4-mm array) enabled us to track the transient gamma spatiotemporal patterns manifested, e.g., as concentric expanding and spiral waves. Gamma oscillations were induced well beyond the light stimulation volume, via network interactions at distal electrode sites, depending on optical power. Despite stimulation-related modulation in spiking rates, neuronal spiking remained highly asynchronous during induced gamma oscillations. In one subject we examined stimulation effects during preparation and execution of a motor task and observed that movement execution largely attenuated optically induced gamma oscillations. Our findings demonstrate that, beyond previously reported induced gamma activity under periodic drive, a prolonged constant stimulus above a certain threshold may carry primate motor cortex network dynamics into gamma oscillations, likely via a Hopf bifurcation. More broadly, the experimental capability in combining microelectrode array recordings and optogenetic stimulation provides an important approach for probing spatiotemporal dynamics in primate cortical networks during various physiological and behavioral conditions.

Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex.

Neural activity in ventral premotor cortex (PMv) has been associated with the process of matching perceived objects with the motor commands needed to grasp them. It remains unclear how PMv networks can flexibly link percepts of objects affording multiple grasp options into a final desired hand action. Here, we use a relational encoding approach to track the functional state of PMv neuronal ensembles in macaque monkeys through the process of passive viewing, grip planning, and grasping movement execution. We used objects affording multiple possible grip strategies. The task included separate instructed delay periods for object presentation and grip instruction. This approach allowed us to distinguish responses elicited by the visual presentation of the objects from those associated with selecting a given motor plan for grasping. We show that PMv continuously incorporates information related to object shape and grip strategy as it becomes available, revealing a transition from a set of ensemble states initially most closely related to objects, to a new set of ensemble patterns reflecting unique object-grip combinations. These results suggest that PMv dynamically combines percepts, gradually navigating toward activity patterns associated with specific volitional actions, rather than directly mapping perceptual object properties onto categorical grip representations. Our results support the idea that PMv is part of a network that dynamically computes motor plans from perceptual information. SIGNIFICANCE STATEMENT The present work demonstrates that the activity of groups of neurons in primate ventral premotor cortex reflects information related to visually presented objects, as well as the motor strategy used to grasp them, linking individual objects to multiple possible grips. PMv could provide useful control signals for neuroprosthetic assistive devices designed to interact with objects in a flexible way.

Spike train similarity space (ssims): A framework for single neuron and ensemble data analysis

Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large-scale ensemble activity beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how spike train SIMilarity space (SSIMS) analysis captures the relationship between goal directions for an eight-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution.

Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (<100 ms) spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters.