Stephen Ryu, MD
Adjunct Clinical Professor, Neurosurgery
All Publications
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Inferring single-trial neural population dynamics using sequential auto-encoders
NATURE METHODS
2018; 15 (10): 805-+
Abstract
Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.
View details for PubMedID 30224673
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Learning by neural reassociation
NATURE NEUROSCIENCE
2018; 21 (4): 607-+
Abstract
Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.
View details for PubMedID 29531364
View details for PubMedCentralID PMC5876156
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Feasibility Analysis of Genetically-Encoded Calcium Indicators as a Neural Signal Source for All-Optical Brain-Machine Interfaces
IEEE. 2017: 174–80
View details for Web of Science ID 000428143200042
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Making brain-machine interfaces robust to future neural variability
NATURE COMMUNICATIONS
2016; 7
Abstract
A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.
View details for DOI 10.1038/ncomms13749
View details for Web of Science ID 000389627100001
View details for PubMedID 27958268
View details for PubMedCentralID PMC5159828
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Neural constraints on learning
NATURE
2014; 512 (7515): 423-U428
Abstract
Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.
View details for DOI 10.1038/nature13665
View details for Web of Science ID 000340840600032
View details for PubMedID 25164754
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Cortical activity in the null space: permitting preparation without movement.
Nature neuroscience
2014; 17 (3): 440-448
Abstract
Neural circuits must perform computations and then selectively output the results to other circuits. Yet synapses do not change radically at millisecond timescales. A key question then is: how is communication between neural circuits controlled? In motor control, brain areas directly involved in driving movement are active well before movement begins. Muscle activity is some readout of neural activity, yet it remains largely unchanged during preparation. Here we find that during preparation, while the monkey holds still, changes in motor cortical activity cancel out at the level of these population readouts. Motor cortex can thereby prepare the movement without prematurely causing it. Further, we found evidence that this mechanism also operates in dorsal premotor cortex, largely accounting for how preparatory activity is attenuated in primary motor cortex. Selective use of 'output-null' vs. 'output-potent' patterns of activity may thus help control communication to the muscles and between these brain areas.
View details for DOI 10.1038/nn.3643
View details for PubMedID 24487233
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Neural Dynamics of Reaching following Incorrect or Absent Motor Preparation
NEURON
2014; 81 (2): 438-451
Abstract
Moving is thought to take separate preparation and execution steps. During preparation, neural activity in primary motor and dorsal premotor cortices achieves a state specific to an upcoming action but movements are not performed until the execution phase. We investigated whether this preparatory state (more precisely, prepare-and-hold state) is required for movement execution using two complementary experiments. We compared monkeys' neural activity during delayed and nondelayed reaches and in a delayed reaching task in which the target switched locations on a small percentage of trials. Neural population activity bypassed the prepare-and-hold state both in the absence of a delay and if the wrong reach was prepared. However, the initial neural response to the target was similar across behavioral conditions. This suggests that the prepare-and-hold state can be bypassed if needed, but there is a short-latency preparatory step that is performed prior to movement even without a delay.
View details for DOI 10.1016/j.neuron.2013.12.003
View details for Web of Science ID 000330420700020
View details for PubMedID 24462104
View details for PubMedCentralID PMC3936035
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A high-performance neural prosthesis enabled by control algorithm design
NATURE NEUROSCIENCE
2012; 15 (12): 1752-1757
Abstract
Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer individuals with disabilities greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural prostheses are considerably slower, with less accurate control, than the native arm. Here we present a new control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF) that incorporates assumptions about the nature of closed-loop neural prosthetic control. When tested in rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperformed existing neural prosthetic algorithms in all measured domains and halved target acquisition time. This control algorithm permits sustained, uninterrupted use for hours and generalizes to more challenging tasks without retraining. Using this algorithm, we demonstrate repeatable high performance for years after implantation in two monkeys, thereby increasing the clinical viability of neural prostheses.
View details for DOI 10.1038/nn.3265
View details for Web of Science ID 000311706700023
View details for PubMedID 23160043
View details for PubMedCentralID PMC3638087
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Neural population dynamics during reaching
NATURE
2012; 487 (7405): 51-?
Abstract
Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.
View details for DOI 10.1038/nature11129
View details for Web of Science ID 000305982900048
View details for PubMedID 22722855
View details for PubMedCentralID PMC3393826
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Single-Trial Neural Correlates of Arm Movement Preparation
NEURON
2011; 71 (3): 555-564
Abstract
The process by which neural circuitry in the brain plans and executes movements is not well understood. Until recently, most available data were limited either to single-neuron electrophysiological recordings or to measures of aggregate field or metabolism. Neither approach reveals how individual neurons' activities are coordinated within the population, and thus inferences about how the neural circuit forms a motor plan for an upcoming movement have been indirect. Here we build on recent advances in the measurement and description of population activity to frame and test an "initial condition hypothesis" of arm movement preparation and initiation. This hypothesis leads to a model in which the timing of movements may be predicted on each trial using neurons' moment-by-moment firing rates and rates of change of those rates. Using simultaneous microelectrode array recordings from premotor cortex of monkeys performing delayed-reach movements, we compare such single-trial predictions to those of other theories. We show that our model can explain approximately 4-fold more arm-movement reaction-time variance than the best alternative method. Thus, the initial condition hypothesis elucidates a view of the relationship between single-trial preparatory neural population dynamics and single-trial behavior.
View details for DOI 10.1016/j.neuron.2011.05.047
View details for Web of Science ID 000293991700017
View details for PubMedID 21835350
View details for PubMedCentralID PMC3155684
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Stimulus onset quenches neural variability: a widespread cortical phenomenon
NATURE NEUROSCIENCE
2010; 13 (3): 369-U25
Abstract
Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.
View details for DOI 10.1038/nn.2501
View details for Web of Science ID 000274860100020
View details for PubMedID 20173745
View details for PubMedCentralID PMC2828350
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A high-performance brain-computer interface
NATURE
2006; 442 (7099): 195-198
Abstract
Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.
View details for DOI 10.1038/nature04968
View details for Web of Science ID 000238979700046
View details for PubMedID 16838020
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Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements.
Scientific reports
2018; 8 (1): 16357
Abstract
Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector's position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.
View details for PubMedID 30397281
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Development of an optogenetic toolkit for neural circuit dissection in squirrel monkeys
SCIENTIFIC REPORTS
2018; 8: 6775
Abstract
Optogenetic tools have opened a rich experimental landscape for understanding neural function and disease. Here, we present the first validation of eight optogenetic constructs driven by recombinant adeno-associated virus (AAV) vectors and a WGA-Cre based dual injection strategy for projection targeting in a widely-used New World primate model, the common squirrel monkey Saimiri sciureus. We observed opsin expression around the local injection site and in axonal projections to downstream regions, as well as transduction to thalamic neurons, resembling expression patterns observed in macaques. Optical stimulation drove strong, reliable excitatory responses in local neural populations for two depolarizing opsins in anesthetized monkeys. Finally, we observed continued, healthy opsin expression for at least one year. These data suggest that optogenetic tools can be readily applied in squirrel monkeys, an important first step in enabling precise, targeted manipulation of neural circuits in these highly trainable, cognitively sophisticated animals. In conjunction with similar approaches in macaques and marmosets, optogenetic manipulation of neural circuits in squirrel monkeys will provide functional, comparative insights into neural circuits which subserve dextrous motor control as well as other adaptive behaviors across the primate lineage. Additionally, development of these tools in squirrel monkeys, a well-established model system for several human neurological diseases, can aid in identifying novel treatment strategies.
View details for PubMedID 29712920
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Neural Population Dynamics Underlying Motor Learning Transfer
NEURON
2018; 97 (5): 1177-+
Abstract
Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal.
View details for PubMedID 29456026
View details for PubMedCentralID PMC5843544
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Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.
Neuron
2018
Abstract
Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.
View details for PubMedID 29887338
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A High-Performance Neural Prosthesis Incorporating Discrete State Selection With Hidden Markov Models
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2017; 64 (4): 935-945
Abstract
Communication neural prostheses aim to restore efficient communication to people with motor neurological injury or disease by decoding neural activity into control signals. These control signals are both analog (e.g., the velocity of a computer mouse) and discrete (e.g., clicking an icon with a computer mouse) in nature. Effective, high-performing, and intuitive-to-use communication prostheses should be capable of decoding both analog and discrete state variables seamlessly. However, to date, the highest-performing autonomous communication prostheses rely on precise analog decoding and typically do not incorporate high-performance discrete decoding. In this report, we incorporated a hidden Markov model (HMM) into an intracortical communication prosthesis to enable accurate and fast discrete state decoding in parallel with analog decoding. In closed-loop experiments with nonhuman primates implanted with multielectrode arrays, we demonstrate that incorporating an HMM into a neural prosthesis can increase state-of-the-art achieved bitrate by 13.9% and 4.2% in two monkeys ( ). We found that the transition model of the HMM is critical to achieving this performance increase. Further, we found that using an HMM resulted in the highest achieved peak performance we have ever observed for these monkeys, achieving peak bitrates of 6.5, 5.7, and 4.7 bps in Monkeys J, R, and L, respectively. Finally, we found that this neural prosthesis was robustly controllable for the duration of entire experimental sessions. These results demonstrate that high-performance discrete decoding can be beneficially combined with analog decoding to achieve new state-of-the-art levels of performance.
View details for DOI 10.1109/TBME.2016.2582691
View details for Web of Science ID 000398738300020
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Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model.
journal of neuroscience
2017; 37 (7): 1721-1732
Abstract
Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.
View details for DOI 10.1523/JNEUROSCI.1091-16.2016
View details for PubMedID 28087767
View details for PubMedCentralID PMC5320605
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A Nonhuman Primate Brain-Computer Typing Interface
PROCEEDINGS OF THE IEEE
2017; 105 (1): 66-72
View details for DOI 10.1109/JPROC.2016.2586967
View details for Web of Science ID 000391703100006
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Augmenting intracortical brain-machine interface with neurally driven error detectors.
Journal of neural engineering
2017; 14 (6): 066007
Abstract
Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs.We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task.We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error 'detect-and-act' system that attempts to automatically 'undo' or 'prevent' mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF).Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.
View details for PubMedID 29130452
View details for PubMedCentralID PMC5742283
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The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces
EXPERIMENTAL NEUROLOGY
2017; 287: 437-451
Abstract
A central goal of neuroscience is to understand how populations of neurons coordinate and cooperate in order to give rise to perception, cognition, and action. Nonhuman primates (NHPs) are an attractive model with which to understand these mechanisms in humans, primarily due to the strong homology of their brains and the cognitively sophisticated behaviors they can be trained to perform. Using electrode recordings, the activity of one to a few hundred individual neurons may be measured electrically, which has enabled many scientific findings and the development of brain-machine interfaces. Despite these successes, electrophysiology samples sparsely from neural populations and provides little information about the genetic identity and spatial micro-organization of recorded neurons. These limitations have spurred the development of all-optical methods for neural circuit interrogation. Fluorescent calcium signals serve as a reporter of neuronal responses, and when combined with post-mortem optical clearing techniques such as CLARITY, provide dense recordings of neuronal populations, spatially organized and annotated with genetic and anatomical information. Here, we advocate that this methodology, which has been of tremendous utility in smaller animal models, can and should be developed for use with NHPs. We review here several of the key opportunities and challenges for calcium-based optical imaging in NHPs. We focus on motor neuroscience and brain-machine interface design as representative domains of opportunity within the larger field of NHP neuroscience.
View details for DOI 10.1016/j.expneurol.2016.08.003
View details for Web of Science ID 000391158800002
View details for PubMedCentralID PMC5154795
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Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1.
PLoS computational biology
2016; 12 (11)
Abstract
Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure-a basic example is the frequency spectrum-and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were 'simplest' (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.
View details for DOI 10.1371/journal.pcbi.1005164
View details for PubMedID 27814353
View details for PubMedCentralID PMC5096707
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Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics
JOURNAL OF NEUROPHYSIOLOGY
2015; 114 (3): 1500-1512
Abstract
A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals. We examined the similarities and differences in the information contained in four signal types recorded simultaneously from multielectrode arrays implanted in primary motor cortex: well-isolated action potentials from putative single units, multiunit threshold crossings, and local field potentials (LFPs) at two distinct frequency bands. We quantified the tuning of these signal types to kinematic parameters of reaching movements. We found 1) threshold crossing activity is not a proxy for single-unit activity; 2) when examined on individual electrodes, threshold crossing activity more closely resembles LFP activity at frequencies between 100 and 300 Hz than it does single-unit activity; 3) when examined across multiple electrodes, threshold crossing activity and LFP integrate neural activity at different spatial scales; and 4) LFP power in the "beta band" (between 10 and 40 Hz) is a reliable indicator of movement onset but does not encode kinematic features on an instant-by-instant basis. These results show that the diverse signals recorded from extracellular electrodes provide somewhat distinct and complementary information. It may be that these signal types arise from biological phenomena that are partially distinct. These results also have practical implications for harnessing richer signals to improve brain-machine interface control.
View details for DOI 10.1152/jn.00293.2014
View details for PubMedID 26133797
View details for PubMedCentralID PMC4556850
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Single-trial dynamics of motor cortex and their applications to brain-machine interfaces
NATURE COMMUNICATIONS
2015; 6
Abstract
Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain-machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs.
View details for DOI 10.1038/ncomms8759
View details for Web of Science ID 000358858800003
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A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes.
Journal of neural engineering
2015; 12 (3): 036009-?
Abstract
Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI.Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together.LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor.These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.
View details for DOI 10.1088/1741-2560/12/3/036009
View details for PubMedID 25946198
View details for PubMedCentralID PMC4457459
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A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes
JOURNAL OF NEURAL ENGINEERING
2015; 12 (3)
Abstract
Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI.Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together.LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor.These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.
View details for DOI 10.1088/1741-2560/12/3/036009
View details for Web of Science ID 000354998600010
View details for PubMedID 25946198
View details for PubMedCentralID PMC4457459
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Natural Grouping of Neural Responses Reveals Spatially Segregated Clusters in Prearcuate Cortex
NEURON
2015; 85 (6): 1359-1373
Abstract
A fundamental challenge in studying the frontal lobe is to parcellate this cortex into "natural" functional modules despite the absence of topographic maps, which are so helpful in primary sensory areas. Here we show that unsupervised clustering algorithms, applied to 96-channel array recordings from prearcuate gyrus, reveal spatially segregated subnetworks that remain stable across behavioral contexts. Looking for natural groupings of neurons based on response similarities, we discovered that the recorded area includes at least two spatially segregated subnetworks that differentially represent behavioral choice and reaction time. Importantly, these subnetworks are detectable during different behavioral states and, surprisingly, are defined better by "common noise" than task-evoked responses. Our parcellation process works well on "spontaneous" neural activity, and thus bears strong resemblance to the identification of "resting-state" networks in fMRI data sets. Our results demonstrate a powerful new tool for identifying cortical subnetworks by objective classification of simultaneously recorded electrophysiological activity.
View details for DOI 10.1016/j.neuron.2015.02.014
View details for Web of Science ID 000351319000020
View details for PubMedID 25728571
View details for PubMedCentralID PMC4366683
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Brain-computer interface control along instructed paths.
Journal of neural engineering
2015; 12 (1): 016015-?
Abstract
Objective. Brain-computer interfaces (BCIs) are being developed to assist paralyzed people and amputees by translating neural activity into movements of a computer cursor or prosthetic limb. Here we introduce a novel BCI task paradigm, intended to help accelerate improvements to BCI systems. Through this task, we can push the performance limits of BCI systems, we can quantify more accurately how well a BCI system captures the user's intent, and we can increase the richness of the BCI movement repertoire. Approach. We have implemented an instructed path task, wherein the user must drive a cursor along a visible path. The instructed path task provides a versatile framework to increase the difficulty of the task and thereby push the limits of performance. Relative to traditional point-to-point tasks, the instructed path task allows more thorough analysis of decoding performance and greater richness of movement kinematics. Main results. We demonstrate that monkeys are able to perform the instructed path task in a closed-loop BCI setting. We further investigate how the performance under BCI control compares to native arm control, whether users can decrease their movement variability in the face of a more demanding task, and how the kinematic richness is enhanced in this task. Significance. The use of the instructed path task has the potential to accelerate the development of BCI systems and their clinical translation.
View details for DOI 10.1088/1741-2560/12/1/016015
View details for PubMedID 25605498
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Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance.
Journal of neural engineering
2015; 12 (1): 016009-?
Abstract
Objective. For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials ('spikes') requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials. Approach. We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position. Main results. We found the highest performance for thresholding when placing a threshold between -3 and -4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded. Significance. For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.
View details for DOI 10.1088/1741-2560/12/1/016009
View details for PubMedID 25504690
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Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance.
Journal of neural engineering
2015; 12 (1): 016009-?
Abstract
Objective. For intracortical brain-machine interfaces (BMIs), action potential voltage waveforms are often sorted to separate out individual neurons. If these neurons contain independent tuning information, this process could increase BMI performance. However, the sorting of action potentials ('spikes') requires high sampling rates and is computationally expensive. To explicitly define the difference between spike sorting and alternative methods, we quantified BMI decoder performance when using threshold-crossing events versus sorted action potentials. Approach. We used data sets from 58 experimental sessions from two rhesus macaques implanted with Utah arrays. Data were recorded while the animals performed a center-out reaching task with seven different angles. For spike sorting, neural signals were sorted into individual units by using a mixture of Gaussians to cluster the first four principal components of the waveforms. For thresholding events, spikes that simply crossed a set threshold were retained. We decoded the data offline using both a Naïve Bayes classifier for reaching direction and a linear regression to evaluate hand position. Main results. We found the highest performance for thresholding when placing a threshold between -3 and -4.5 × Vrms. Spike sorted data outperformed thresholded data for one animal but not the other. The mean Naïve Bayes classification accuracy for sorted data was 88.5% and changed by 5% on average when data were thresholded. The mean correlation coefficient for sorted data was 0.92, and changed by 0.015 on average when thresholded. Significance. For prosthetics applications, these results imply that when thresholding is used instead of spike sorting, only a small amount of performance may be lost. The utilization of threshold-crossing events may significantly extend the lifetime of a device because these events are often still detectable once single neurons are no longer isolated.
View details for DOI 10.1088/1741-2560/12/1/016009
View details for PubMedID 25504690
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A High-Performance Keyboard Neural Prosthesis Enabled by Task Optimization
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2015; 62 (1): 21-29
Abstract
Communication neural prostheses are an emerging class of medical devices that aim to restore efficient communication to people suffering from paralysis. These systems rely on an interface with the user, either via the use of a continuouslymoving cursor (e.g., mouse) or the discrete selection of symbols (e.g., keyboard). In developing these interfaces, many design choices have a significant impact on the performance of the system. The objective of this study was to explore the design choices of a continuously-moving cursor neural prosthesis and optimize the interface to maximize information theoretic performance. We swept interface parameters of two keyboard-like tasks to find task and subject specific optimal parameters as measured by achieved bitrate using two rhesus macaques implanted with multielectrode arrays. In this report, we present the highest performing free-paced neural prosthesis under any recording modality with sustainable communication rates of up to 3.5 bits per second (bps). These findings demonstrate that meaningful high performance can be achieved using an intracortical neural prosthesis, and that, when optimized, these systems may be appropriate for use as communication devices for those with physical disabilities.
View details for DOI 10.1109/TBME.2014.2354697
View details for Web of Science ID 000346765500003
View details for PubMedID 25203982
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Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex.
eLife
2015; 4
Abstract
When choosing actions, we can act decisively, vacillate, or suffer momentary indecision. Studying how individual decisions unfold requires moment-by-moment readouts of brain state. Here we provide such a view from dorsal premotor and primary motor cortex. Two monkeys performed a novel decision task while we recorded from many neurons simultaneously. We found that a decoder trained using 'forced choices' (one target viable) was highly reliable when applied to 'free choices'. However, during free choices internal events formed three categories. Typically, neural activity was consistent with rapid, unwavering choices. Sometimes, though, we observed presumed 'changes of mind': the neural state initially reflected one choice before changing to reflect the final choice. Finally, we observed momentary 'indecision': delay forming any clear motor plan. Further, moments of neural indecision accompanied moments of behavioral indecision. Together, these results reveal the rich and diverse set of internal events long suspected to occur during free choice.
View details for DOI 10.7554/eLife.04677
View details for PubMedID 25942352
View details for PubMedCentralID PMC4415122
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Single-trial dynamics of motor cortex and their applications to brain-machine interfaces.
Nature communications
2015; 6: 7759-?
Abstract
Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain-machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs.
View details for DOI 10.1038/ncomms8759
View details for PubMedID 26220660
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Performance sustaining intracortical neural prostheses.
Journal of neural engineering
2014; 11 (6): 066003-?
Abstract
Objective. Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr(-1). In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make neural prostheses more practical to use.
View details for DOI 10.1088/1741-2560/11/6/066003
View details for PubMedID 25307561
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Performance sustaining intracortical neural prostheses
JOURNAL OF NEURAL ENGINEERING
2014; 11 (6)
Abstract
Objective. Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr(-1). In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make neural prostheses more practical to use.
View details for DOI 10.1088/1741-2560/11/6/066003
View details for Web of Science ID 000345647700009
View details for PubMedID 25307561
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A freely-moving monkey treadmill model
JOURNAL OF NEURAL ENGINEERING
2014; 11 (4)
Abstract
Objective. Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement. Approach. We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the flexibility and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill. Main results. Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average firing rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions. Significance. Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.
View details for DOI 10.1088/1741-2560/11/4/046020
View details for Web of Science ID 000340046500020
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Self-recalibrating classifiers for intracortical brain-computer interfaces.
Journal of neural engineering
2014; 11 (2): 026001-?
Abstract
Objective. Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). Approach. We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. Main results. We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a [Formula: see text] increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. Significance. We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.
View details for DOI 10.1088/1741-2560/11/2/026001
View details for PubMedID 24503597
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Self-recalibrating classifiers for intracortical brain-computer interfaces.
Journal of neural engineering
2014; 11 (2): 026001-?
Abstract
Objective. Intracortical brain-computer interface (BCI) decoders are typically retrained daily to maintain stable performance. Self-recalibrating decoders aim to remove the burden this may present in the clinic by training themselves autonomously during normal use but have only been developed for continuous control. Here we address the problem for discrete decoding (classifiers). Approach. We recorded threshold crossings from 96-electrode arrays implanted in the motor cortex of two rhesus macaques performing center-out reaches in 7 directions over 41 and 36 separate days spanning 48 and 58 days in total for offline analysis. Main results. We show that for the purposes of developing a self-recalibrating classifier, tuning parameters can be considered as fixed within days and that parameters on the same electrode move up and down together between days. Further, drift is constrained across time, which is reflected in the performance of a standard classifier which does not progressively worsen if it is not retrained daily, though overall performance is reduced by more than 10% compared to a daily retrained classifier. Two novel self-recalibrating classifiers produce a [Formula: see text] increase in classification accuracy over that achieved by the non-retrained classifier to nearly recover the performance of the daily retrained classifier. Significance. We believe that the development of classifiers that require no daily retraining will accelerate the clinical translation of BCI systems. Future work should test these results in a closed-loop setting.
View details for DOI 10.1088/1741-2560/11/2/026001
View details for PubMedID 24503597
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Intention estimation in brain-machine interfaces.
Journal of neural engineering
2014; 11 (1): 016004-?
Abstract
The objective of this work was to quantitatively investigate the mechanisms underlying the performance gains of the recently reported 'recalibrated feedback intention-trained Kalman Filter' (ReFIT-KF).This was accomplished by designing variants of the ReFIT-KF algorithm and evaluating training and online data to understand the neural basis of this improvement. We focused on assessing the contribution of two training set innovations of the ReFIT-KF algorithm: intention estimation and the two-stage training paradigm.Within the two-stage training paradigm, we found that intention estimation independently increased target acquisition rates by 37% and 59%, respectively, across two monkeys implanted with multiunit intracortical arrays. Intention estimation improved performance by enhancing the tuning properties and the mutual information between the kinematic and neural training data. Furthermore, intention estimation led to fewer shifts in channel tuning between the training set and online control, suggesting that less adaptation was required during online control. Retraining the decoder with online BMI training data also reduced shifts in tuning, suggesting a benefit of training a decoder in the same behavioral context; however, retraining also led to slower online decode velocities. Finally, we demonstrated that one- and two-stage training paradigms performed comparably when intention estimation is applied.These findings highlight the utility of intention estimation in reducing the need for adaptive strategies and improving the online performance of BMIs, helping to guide future BMI design decisions.
View details for PubMedID 24654266
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Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
2014; 2014: 3041-3044
Abstract
The best-performing brain-machine interfaces (BMIs) to date decode movement intention from intracortically recorded spikes, but these signals may be lost over time. A way to increase the useful lifespan of BMIs is to make more comprehensive use of available neural signals. Recent studies have demonstrated that the local field potential (LFP), a potentially more robust signal, can also be used to control a BMI. However, LFP-driven performance has fallen short of the best spikes-driven performance. Here we report a biomimetic BMI driven by low-frequency LFP that enabled a rhesus monkey to acquire and hold randomly placed targets with 99% success rate. Although LFP-driven performance was still worse than when decoding spikes, to the best of our knowledge this represents the highest-performing LFP-based BMI. We also demonstrate a new hybrid BMI that decodes cursor velocity using both spikes and LFP. This hybrid decoder improved performance over spikes-only decoding. Our results suggest that LFP can complement spikes when spikes are available or provide an alternative control signal if spikes are absent.
View details for DOI 10.1109/EMBC.2014.6944264
View details for PubMedID 25570632
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DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.
Journal of neural engineering
2013; 10 (6): 066012-?
Abstract
Objective. Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.
View details for DOI 10.1088/1741-2560/10/6/066012
View details for PubMedID 24216250
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DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.
Journal of neural engineering
2013; 10 (6): 066012-?
Abstract
Objective. Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.
View details for DOI 10.1088/1741-2560/10/6/066012
View details for PubMedID 24216250
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Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2013; 2013: 293-298
Abstract
The intraday robustness of brain-machine interfaces (BMIs) is important to their clinical viability. In particular, BMIs must be robust to intraday perturbations in neuron firing rates, which may arise from several factors including recording loss and external noise. Using a state-of-the-art decode algorithm, the Recalibrated Feedback Intention Trained Kalman filter (ReFIT-KF) [1] we introduce two novel modifications: (1) a normalization of the firing rates, and (2) a reduction of the dimensionality of the data via principal component analysis (PCA). We demonstrate in online studies that a ReFIT-KF equipped with normalization and PCA (NPC-ReFIT-KF) (1) achieves comparable performance to a standard ReFIT-KF when at least 60% of the neural variance is captured, and (2) is more robust to the undetected loss of channels. We present intuition as to how both modifications may increase the robustness of BMIs, and investigate the contribution of each modification to robustness. These advances, which lead to a decoder achieving state-of-the-art performance with improved robustness, are important for the clinical viability of BMI systems.
View details for DOI 10.1109/EMBC.2013.6609495
View details for PubMedID 24109682
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Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.
Journal of neural engineering
2013; 10 (3): 036008-?
Abstract
Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.
View details for DOI 10.1088/1741-2560/10/3/036008
View details for PubMedID 23574919
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Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.
Journal of neural engineering
2013; 10 (3): 036008-?
Abstract
Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.
View details for DOI 10.1088/1741-2560/10/3/036008
View details for PubMedID 23574919
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Direction and Speed Tuning of Motor-Cortex Multi-Unit Activity and Local Field Potentials During Reaching Movements
35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)
IEEE. 2013: 299–302
Abstract
Primary motor-cortex multi-unit activity (MUA) and local-field potentials (LFPs) have both been suggested as potential control signals for brain-computer interfaces (BCIs) aimed at movement restoration. Some studies report that LFP-based decoding is comparable to spiking-based decoding, while others offer contradicting evidence. Differences in experimental paradigms, tuning models and decoding techniques make it hard to directly compare these results. Here, we use regression and mutual information analyses to study how MUA and LFP encode various kinematic parameters during reaching movements. We find that in addition to previously reported directional tuning, MUA also contains prominent speed tuning. LFP activity in low-frequency bands (15-40Hz, LFPL) is primarily speed tuned, and contains more speed information than both high-frequency LFP (100-300Hz, LFPH) and MUA. LFPH contains more directional information compared to LFPL, but less information when compared with MUA. Our results suggest that a velocity and speed encoding model is most appropriate for both MUA and LFPH, whereas a speed only encoding model is adequate for LFPL.
View details for Web of Science ID 000341702100073
View details for PubMedID 24109683
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An L (1)-regularized logistic model for detecting short-term neuronal interactions
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
2012; 32 (3): 479-497
Abstract
Interactions among neurons are a key component of neural signal processing. Rich neural data sets potentially containing evidence of interactions can now be collected readily in the laboratory, but existing analysis methods are often not sufficiently sensitive and specific to reveal these interactions. Generalized linear models offer a platform for analyzing multi-electrode recordings of neuronal spike train data. Here we suggest an L(1)-regularized logistic regression model (L(1)L method) to detect short-term (order of 3 ms) neuronal interactions. We estimate the parameters in this model using a coordinate descent algorithm, and determine the optimal tuning parameter using a Bayesian Information Criterion. Simulation studies show that in general the L(1)L method has better sensitivities and specificities than those of the traditional shuffle-corrected cross-correlogram (covariogram) method. The L(1)L method is able to detect excitatory interactions with both high sensitivity and specificity with reasonably large recordings, even when the magnitude of the interactions is small; similar results hold for inhibition given sufficiently high baseline firing rates. Our study also suggests that the false positives can be further removed by thresholding, because their magnitudes are typically smaller than true interactions. Simulations also show that the L(1)L method is somewhat robust to partially observed networks. We apply the method to multi-electrode recordings collected in the monkey dorsal premotor cortex (PMd) while the animal prepares to make reaching arm movements. The results show that some neurons interact differently depending on task conditions. The stronger interactions detected with our L(1)L method were also visible using the covariogram method.
View details for DOI 10.1007/s10827-011-0365-5
View details for Web of Science ID 000303589500007
View details for PubMedID 22038503
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A recurrent neural network for closed-loop intracortical brain-machine interface decoders
JOURNAL OF NEURAL ENGINEERING
2012; 9 (2)
Abstract
Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.
View details for DOI 10.1088/1741-2560/9/2/026027
View details for Web of Science ID 000302144100027
View details for PubMedID 22427488
View details for PubMedCentralID PMC3638090
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A brain machine interface control algorithm designed from a feedback control perspective
34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)
IEEE. 2012: 1318–1322
Abstract
We present a novel brain machine interface (BMI) control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF). The design of ReFIT-KF is motivated from a feedback control perspective applied to existing BMI control algorithms. The result is two design innovations that alter the modeling assumptions made by these algorithms and the methods by which these algorithms are trained. In online neural control experiments recording from a 96-electrode array implanted in M1 of a macaque monkey, the ReFIT-KF control algorithm demonstrates large performance improvements over the current state of the art velocity Kalman filter, reducing target acquisition time by a factor of two, while maintaining a 500 ms hold period, thereby increasing the clinical viability of BMI systems.
View details for Web of Science ID 000313296501144
View details for PubMedID 23366141
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DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity
34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)
IEEE. 2012: 4607–4610
Abstract
The activity of tens to hundreds of neurons can be succinctly summarized by a smaller number of latent variables extracted using dimensionality reduction methods. These latent variables define a reduced-dimensional space in which we can study how population activity varies over time, across trials, and across experimental conditions. Ideally, we would like to visualize the population activity directly in the reduced-dimensional space, whose optimal dimensionality (as determined from the data) is typically greater than 3. However, direct plotting can only provide a 2D or 3D view. To address this limitation, we developed a Matlab graphical user interface (GUI) that allows the user to quickly navigate through a continuum of different 2D projections of the reduced-dimensional space. To demonstrate the utility and versatility of this GUI, we applied it to visualize population activity recorded in premotor and motor cortices during reaching tasks. Examples include single-trial population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded sequentially using single electrodes. Because any single 2D projection may provide a misleading impression of the data, being able to see a large number of 2D projections is critical for intuition-and hypothesis-building during exploratory data analysis. The GUI includes a suite of additional interactive tools, including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses. The use of visualization tools like the GUI developed here, in tandem with dimensionality reduction methods, has the potential to further our understanding of neural population activity.
View details for Web of Science ID 000313296504203
View details for PubMedID 23366954
View details for PubMedCentralID PMC3745770
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A framework for relating neural activity to freely moving behavior
34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)
IEEE. 2012: 2736–2739
Abstract
Two research communities, motor systems neuroscience and motor prosthetics, examine the relationship between neural activity in the motor cortex and movement. The former community aims to understand how the brain controls and generates movement; the latter community focuses on how to decode neural activity as control signals for a prosthetic cursor or limb. Both have made progress toward understanding the relationship between neural activity in the motor cortex and behavior. However, these findings are tested using animal models in an environment that constrains behavior to simple, limited movements. These experiments show that, in constrained settings, simple reaching motions can be decoded from small populations of spiking neurons. It is unclear whether these findings hold for more complex, full-body behaviors in unconstrained settings. Here we present the results of freely-moving behavioral experiments from a monkey with simultaneous intracortical recording. We investigated neural firing rates while the monkey performed various tasks such as walking on a treadmill, reaching for food, and sitting idly. We show that even in such an unconstrained and varied context, neural firing rates are well tuned to behavior, supporting findings of basic neuroscience. Further, we demonstrate that the various behavioral tasks can be reliably classified with over 95% accuracy, illustrating the viability of decoding techniques despite significant variation and environmental distractions associated with unconstrained behavior. Such encouraging results hint at potential utility of the freely-moving experimental paradigm.
View details for Web of Science ID 000313296502238
View details for PubMedID 23366491
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Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex
39th Neural Interfaces Conference (NIC2010)
IOP PUBLISHING LTD. 2011
Abstract
Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.
View details for DOI 10.1088/1741-2560/8/4/045005
View details for Web of Science ID 000292962800009
View details for PubMedID 21775782
View details for PubMedCentralID PMC3644617
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A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces
JOURNAL OF NEUROPHYSIOLOGY
2011; 105 (4): 1932-1949
Abstract
Neural prosthetic systems seek to improve the lives of severely disabled people by decoding neural activity into useful behavioral commands. These systems and their decoding algorithms are typically developed "offline," using neural activity previously gathered from a healthy animal, and the decoded movement is then compared with the true movement that accompanied the recorded neural activity. However, this offline design and testing may neglect important features of a real prosthesis, most notably the critical role of feedback control, which enables the user to adjust neural activity while using the prosthesis. We hypothesize that understanding and optimally designing high-performance decoders require an experimental platform where humans are in closed-loop with the various candidate decode systems and algorithms. It remains unexplored the extent to which the subject can, for a particular decode system, algorithm, or parameter, engage feedback and other strategies to improve decode performance. Closed-loop testing may suggest different choices than offline analyses. Here we ask if a healthy human subject, using a closed-loop neural prosthesis driven by synthetic neural activity, can inform system design. We use this online prosthesis simulator (OPS) to optimize "online" decode performance based on a key parameter of a current state-of-the-art decode algorithm, the bin width of a Kalman filter. First, we show that offline and online analyses indeed suggest different parameter choices. Previous literature and our offline analyses agree that neural activity should be analyzed in bins of 100- to 300-ms width. OPS analysis, which incorporates feedback control, suggests that much shorter bin widths (25-50 ms) yield higher decode performance. Second, we confirm this surprising finding using a closed-loop rhesus monkey prosthetic system. These findings illustrate the type of discovery made possible by the OPS, and so we hypothesize that this novel testing approach will help in the design of prosthetic systems that will translate well to human patients.
View details for DOI 10.1152/jn.00503.2010
View details for Web of Science ID 000289620500044
View details for PubMedID 20943945
View details for PubMedCentralID PMC3075301
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Combining Wireless Neural Recording and Video Capture for the Analysis of Natural Gait
5th International IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER)
IEEE. 2011: 613–616
View details for Web of Science ID 000298735800147
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High-performance neural prosthetic control along instructed paths
5th International IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER)
IEEE. 2011: 601–604
View details for Web of Science ID 000298735800144
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Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine?
NEURON
2010; 68 (3): 387-400
Abstract
The motor cortices are active during both movement and movement preparation. A common assumption is that preparatory activity constitutes a subthreshold form of movement activity: a neuron active during rightward movements becomes modestly active during preparation of a rightward movement. We asked whether this pattern of activity is, in fact, observed. We found that it was not: at the level of a single neuron, preparatory tuning was weakly correlated with movement-period tuning. Yet, somewhat paradoxically, preparatory tuning could be captured by a preferred direction in an abstract "space" that described the population-level pattern of movement activity. In fact, this relationship accounted for preparatory responses better than did traditional tuning models. These results are expected if preparatory activity provides the initial state of a dynamical system whose evolution produces movement activity. Our results thus suggest that preparatory activity may not represent specific factors, and may instead play a more mechanistic role.
View details for DOI 10.1016/j.neuron.2010.09.015
View details for Web of Science ID 000284255800009
View details for PubMedID 21040842
View details for PubMedCentralID PMC2991102
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Pituitary Adenomas Can Appear as Hypermetabolic Lesions in 18F-FDG PET Imaging
JOURNAL OF NEUROIMAGING
2010; 20 (4): 393-396
Abstract
The 2-deoxy-2-[(18) F] fluoro-D-glucose positron emission tomography (FDG-PET) scan is commonly used in detection and staging of many malignant neoplasms. However, several benign or non-neoplastic conditions avidly accumulate (18) F-FDG, causing ambiguity in interpretation of results. It is unknown whether pituitary adenomas uptake (18) F-FDG and appear positive in PET imaging. Here, we present 2 cases of benign pituitary adenoma with elevated metabolic activity in (18) F-FDG PET scan.Medical, neurologic, and psychiatric histories; physical examination findings; laboratory work up results; and pathologic and imaging studies were documented.The (18) F-FDG-PET images revealed foci of marked FDG uptake in pituitary adenomas of 2 patients.Pituitary micro- and macro-adenomas may present as hypermetabolic foci on (18) F-FDG PET scan.
View details for DOI 10.1111/j.1552-6569.2008.00347.x
View details for Web of Science ID 000282574100015
View details for PubMedID 19453834
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Roles of Monkey Premotor Neuron Classes in Movement Preparation and Execution
JOURNAL OF NEUROPHYSIOLOGY
2010; 104 (2): 799-810
Abstract
Dorsal premotor cortex (PMd) is known to be involved in the planning and execution of reaching movements. However, it is not understood how PMd plan activity-often present in the very same neurons that respond during movement-is prevented from itself producing movement. We investigated whether inhibitory interneurons might "gate" output from PMd, by maintaining high levels of inhibition during planning and reducing inhibition during execution. Recently developed methods permit distinguishing interneurons from pyramidal neurons using extracellular recordings. We extend these methods here for use with chronically implanted multi-electrode arrays. We then applied these methods to single- and multi-electrode recordings in PMd of two monkeys performing delayed-reach tasks. Responses of putative interneurons were not generally in agreement with the hypothesis that they act to gate output from the area: in particular it was not the case that interneurons tended to reduce their firing rates around the time of movement. In fact, interneurons increased their rates more than putative pyramidal neurons during both the planning and movement epochs. The two classes of neurons also differed in a number of other ways, including greater modulation across conditions for interneurons, and interneurons more frequently exhibiting increases in firing rate during movement planning and execution. These findings provide novel information about the greater responsiveness of putative PMd interneurons in motor planning and execution and suggest that we may need to consider new possibilities for how planning activity is structured such that it does not itself produce movement.
View details for DOI 10.1152/jn.00231.2009
View details for Web of Science ID 000280932400023
View details for PubMedID 20538784
View details for PubMedCentralID PMC2934936
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Low-Dimensional Neural Features Predict Muscle EMG Signals
32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)
IEEE. 2010: 6027–6033
Abstract
Understanding the relationship between neural activity in motor cortex and muscle activity during movements is important both for basic science and for the design of neural prostheses. While there has been significant work in decoding muscle EMG from neural data, decoders often require many parameters which make the analysis susceptible to overfitting, which reduces generalizability and makes the results difficult to interpret. To address this issue, we recorded simultaneous neural activity from the motor cortices (M1/PMd) of rhesus monkeys performing an arm-reaching task while recording EMG from arm muscles. In this work, we focused on relating the mean neural activity (averaged across reach trials to one target) to the corresponding mean EMG. We reduced the dimensionality of the neural data and found that the curvature of the low-dimensional (low-D) neural activity could be used as a signature of muscle activity. Using this signature, and without directly fitting EMG data to the neural activity, we derived neural axes based on reaches to only one reach target (< 5% of the data) that could explain EMG for reaches across multiple targets (average R(2) = 0.65). Our results suggest that cortical population activity is tightly related to muscle EMG measurements, predicting a lag between cortical activity and movement generation of 47.5 ms. Furthermore, our ability to predict EMG features across different movements suggests that there are fundamental axes or directions in the low-D neural space along which the neural population activity moves to activate particular muscles.
View details for Web of Science ID 000287964006107
View details for PubMedID 21097116
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Methods for estimating neural firing rates, and their application to brain-machine interfaces
NEURAL NETWORKS
2009; 22 (9): 1235-1246
Abstract
Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.
View details for DOI 10.1016/j.neunet.2009.02.004
View details for Web of Science ID 000272073800005
View details for PubMedID 19349143
View details for PubMedCentralID PMC2783748
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Fracture-related Thoracic Kyphotic Deformity Correction by Single-stage Posterolateral Vertebrectomy With Circumferential Reconstruction and Stabilization Outcomes in 30 Cases
JOURNAL OF SPINAL DISORDERS & TECHNIQUES
2009; 22 (7): 492-501
Abstract
This paper is a retrospective chart review.This study assesses single-stage thoracic vertebrectomy with circumferential reconstruction and stabilization. Preoperative and postoperative thoracic kyphotic angles and other outcomes are analyzed.Pathologic and traumatic thoracic vertebral body fracture deformity can be corrected by an anterior vertebral body corpectomy and reconstruction. If the pathology is primarily posterior, then laminectomy and posterolateral instrumentation may be preferred. In some patients, simultaneous anterior and posterior correction of instability and fracture is necessary and is now possible with a single-stage Stanford University Medical Center (SUMC) technique with similar results to the traditional 2-stage approach.Thirty patients who underwent 31 single-stage thoracic vertebrectomies with circumferential reconstructions for thoracic spine fractures between 2004 and 2006 at SUMC were retrospectively reviewed. All surgeries were performed prone; operative technical details are reported. The preoperative and postoperative thoracic kyphotic angles were measured by Cobb angle evaluation using lateral chest plain films and magnetic resonance imaging. Other outcome measures evaluated included operative time, blood loss, neurologic and functional outcomes, postoperative pain, and treatment complications.The mean follow-up was 17.21 months (range: 9 to 30 mo) and preoperative kyphosis was 20.4 degrees (range: 6.0 to 57.9 degrees). The average postoperative kyphosis was 8.3 degrees (range: 1.8 to 2.67 degrees) and correction of kyphosis was 16.2 degrees (range: 6 to 30 degrees). The median estimated blood loss was 1411.67 mL (range: 300 to 4000 mL) and mean operating time was 4.8 hours (range: 2.8 to 8.6 h). Complications included 2 hardware failures requiring revision, 2 infections, and 1 dural laceration. Pain, Frankel Grade, and functional status were improved in all, except 1 preoperatively bedridden patient.Thoracic kyphotic correction is possible through a prone single-stage simultaneous anterior vertebrectomy and posterior reconstruction. Sufficient anterior and posterior correction of instability and fracture using the SUMC technique is possible with similar results to the traditional 2-stage approach.
View details for DOI 10.1097/BSD.0b013e31818f0ec3
View details for Web of Science ID 000279665400006
View details for PubMedID 20075812
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Factor-Analysis Methods for Higher-Performance Neural Prostheses
JOURNAL OF NEUROPHYSIOLOGY
2009; 102 (2): 1315-1330
Abstract
Neural prostheses aim to provide treatment options for individuals with nervous-system disease or injury. It is necessary, however, to increase the performance of such systems before they can be clinically viable for patients with motor dysfunction. One performance limitation is the presence of correlated trial-to-trial variability that can cause neural responses to wax and wane in concert as the subject is, for example, more attentive or more fatigued. If a system does not properly account for this variability, it may mistakenly interpret such variability as an entirely different intention by the subject. We report here the design and characterization of factor-analysis (FA)-based decoding algorithms that can contend with this confound. We characterize the decoders (classifiers) on experimental data where monkeys performed both a real reach task and a prosthetic cursor task while we recorded from 96 electrodes implanted in dorsal premotor cortex. The decoder attempts to infer the underlying factors that comodulate the neurons' responses and can use this information to substantially lower error rates (one of eight reach endpoint predictions) by
View details for DOI 10.1152/jn.00097.2009
View details for Web of Science ID 000269500400061
View details for PubMedID 19297518
View details for PubMedCentralID PMC2724333
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HermesC: Low-Power Wireless Neural Recording System for Freely Moving Primates
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
2009; 17 (4): 330-338
Abstract
Neural prosthetic systems have the potential to restore lost functionality to amputees or patients suffering from neurological injury or disease. Current systems have primarily been designed for immobile patients, such as tetraplegics functioning in a rather static, carefully tailored environment. However, an active patient such as amputee in a normal dynamic, everyday environment may be quite different in terms of the neural control of movement. In order to study motor control in a more unconstrained natural setting, we seek to develop an animal model of freely moving humans. Therefore, we have developed and tested HermesC-INI3, a system for recording and wirelessly transmitting neural data from electrode arrays implanted in rhesus macaques who are freely moving. This system is based on the integrated neural interface (INI3) microchip which amplifies, digitizes, and transmits neural data across a approximately 900 MHz wireless channel. The wireless transmission has a range of approximately 4 m in free space. All together this device consumes 15.8 mA and 63.2 mW. On a single 2 A-hr battery pack, this device runs contiguously for approximately six days. The smaller size and power consumption of the custom IC allows for a smaller package (51 x 38 x 38 mm (3)) than previous primate systems. The HermesC-INI3 system was used to record and telemeter one channel of broadband neural data at 15.7 kSps from a monkey performing routine daily activities in the home cage.
View details for DOI 10.1109/TNSRE.2009.2023293
View details for Web of Science ID 000268900300004
View details for PubMedID 19497829
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Wireless Neural Recording With Single Low-Power Integrated Circuit
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
2009; 17 (4): 322-329
Abstract
We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902-928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor.
View details for DOI 10.1109/TNSRE.2009.2023298
View details for Web of Science ID 000268900300003
View details for PubMedID 19497825
View details for PubMedCentralID PMC2941647
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Human cortical prostheses: lost in translation?
NEUROSURGICAL FOCUS
2009; 27 (1)
Abstract
Direct brain control of a prosthetic system is the subject of much popular and scientific news. Neural technology and science have advanced to the point that proof-of-concept systems exist for cortically-controlled prostheses in rats, monkeys, and even humans. However, realizing the dream of making such technology available to everyone is still far off. Fortunately today there is great public and scientific interest in making this happen, but it will only occur when the functional benefits of such systems outweigh the risks. In this article, the authors briefly summarize the state of the art and then highlight many issues that will directly limit clinical translation, including system durability, system performance, and patient risk. Despite the challenges, scientists and clinicians are in the desirable position of having both public and fiscal support to begin addressing these issues directly. The ultimate challenge now is to determine definitively whether these prosthetic systems will become clinical reality or forever unrealized.
View details for DOI 10.3171/2009.4.FOCUS0987
View details for Web of Science ID 000268583300005
View details for PubMedID 19569893
View details for PubMedCentralID PMC3614414
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Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity
JOURNAL OF NEUROPHYSIOLOGY
2009; 102 (1): 614-635
Abstract
We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality-reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories-Gaussian-process factor analysis (GPFA)-which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.
View details for DOI 10.1152/jn.90941.2008
View details for Web of Science ID 000267446000056
View details for PubMedID 19357332
View details for PubMedCentralID PMC2712272
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Neural Prosthetic Systems: Current Problems and Future Directions
Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2009: 3369–3375
Abstract
By decoding neural activity into useful behavioral commands, neural prosthetic systems seek to improve the lives of severely disabled human patients. Motor decoding algorithms, which map neural spiking data to control parameters of a device such as a prosthetic arm, have received particular attention in the literature. Here, we highlight several outstanding problems that exist in most current approaches to decode algorithm design. These include two problems that we argue will unlikely result in further dramatic increases in performance, specifically spike sorting and spiking models. We also discuss three issues that have been less examined in the literature, and we argue that addressing these issues may result in dramatic future increases in performance. These include: non-stationarity of recorded waveforms, limitations of a linear mappings between neural activity and movement kinematics, and the low signal to noise ratio of the neural data. We demonstrate these problems with data from 39 experimental sessions with a non-human primate performing reaches and with recent literature. In all, this study suggests that research in cortically-controlled prosthetic systems may require reprioritization to achieve performance that is acceptable for a clinically viable human system.
View details for Web of Science ID 000280543602209
View details for PubMedID 19963796
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Toward Optimal Target Placement for Neural Prosthetic Devices
JOURNAL OF NEUROPHYSIOLOGY
2008; 100 (6): 3445-3457
Abstract
Neural prosthetic systems have been designed to estimate continuous reach trajectories (motor prostheses) and to predict discrete reach targets (communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses.
View details for DOI 10.1152/jn.90833.2008
View details for Web of Science ID 000261449400040
View details for PubMedID 18829845
View details for PubMedCentralID PMC2604856
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Detecting neural-state transitions using hidden Markov models for motor cortical prostheses
JOURNAL OF NEUROPHYSIOLOGY
2008; 100 (4): 2441-2452
Abstract
Neural prosthetic interfaces use neural activity related to the planning and perimovement epochs of arm reaching to afford brain-directed control of external devices. Previous research has primarily centered on accurately decoding movement intention from either plan or perimovement activity, but has assumed that temporal boundaries between these epochs are known to the decoding system. In this work, we develop a technique to automatically differentiate between baseline, plan, and perimovement epochs of neural activity. Specifically, we use a generative model of neural activity to capture how neural activity varies between these three epochs. Our approach is based on a hidden Markov model (HMM), in which the latent variable (state) corresponds to the epoch of neural activity, coupled with a state-dependent Poisson firing model. Using an HMM, we demonstrate that the time of transition from baseline to plan epochs, a transition in neural activity that is not accompanied by any external behavior changes, can be detected using a threshold on the a posteriori HMM state probabilities. Following detection of the plan epoch, we show that the intended target of a center-out movement can be detected about as accurately as that by a maximum-likelihood estimator using a window of known plan activity. In addition, we demonstrate that our HMM can detect transitions in neural activity corresponding to targets not found in training data. Thus the HMM technique for automatically detecting transitions between epochs of neural activity enables prosthetic interfaces that can operate autonomously.
View details for DOI 10.1152/jn.00924.2007
View details for Web of Science ID 000259967000063
View details for PubMedID 18614757
View details for PubMedCentralID PMC2576226
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Cortical neural prosthesis performance improves when eye position is monitored
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
2008; 16 (1): 24-31
Abstract
Neural prostheses that extract signals directly from cortical neurons have recently become feasible as assistive technologies for tetraplegic individuals. Significant effort toward improving the performance of these systems is now warranted. A simple technique that can improve prosthesis performance is to account for the direction of gaze in the operation of the prosthesis. This proposal stems from recent discoveries that the direction of gaze influences neural activity in several areas that are commonly targeted for electrode implantation in neural prosthetics. Here, we first demonstrate that neural prosthesis performance does improve when eye position is taken into account. We then show that eye position can be estimated directly from neural activity, and thus performance gains can be realized even without a device that tracks eye position.
View details for DOI 10.1109/TNSRE.2007.906958
View details for Web of Science ID 000253442400004
View details for PubMedID 18303802
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Signal processing challenges for neural prostheses
IEEE SIGNAL PROCESSING MAGAZINE
2008; 25 (1): 18-28
View details for DOI 10.1109/MSP.2007.909016
View details for Web of Science ID 000251906900006
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An Efficient Approximation for the Real-Time Implementation of the Mixture of Trajectory Models Decoder
IEEE Biomedical Circuits and Systems Conference - Intelligent Biomedical Systems
IEEE. 2008: 133–136
View details for Web of Science ID 000264876700035
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A Wireless Neural Interface for Chronic Recording
IEEE Biomedical Circuits and Systems Conference - Intelligent Biomedical Systems
IEEE. 2008: 125–128
View details for Web of Science ID 000264876700033
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HermesC: RF wireless low-power neural recording system for freely behaving primates
IEEE International Symposium on Circuits and Systems
IEEE. 2008: 1752–1755
View details for Web of Science ID 000258532101169
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A Factor-Analysis decoder for high-performance neural prostheses
33rd IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE. 2008: 5208–5211
View details for Web of Science ID 000257456703286
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Wireless neural signal acquisition with single low-power integrated circuit
IEEE International Symposium on Circuits and Systems
IEEE. 2008: 1748–1751
View details for Web of Science ID 000258532101168
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HermesB: A continuous neural recording system for freely behaving primates
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
2007; 54 (11): 2037-2050
Abstract
Chronically implanted electrode arrays have enabled a broad range of advances in basic electrophysiology and neural prosthetics. Those successes motivate new experiments, particularly, the development of prototype implantable prosthetic processors for continuous use in freely behaving subjects, both monkeys and humans. However, traditional experimental techniques require the subject to be restrained, limiting both the types and duration of experiments. In this paper, we present a dual-channel, battery-powered neural recording system with an integrated three-axis accelerometer for use with chronically implanted electrode arrays in freely behaving primates. The recording system called HermesB, is self-contained, autonomous, programmable, and capable of recording broadband neural (sampled at 30 kS/s) and acceleration data to a removable compact flash card for up to 48 h. We have collected long-duration data sets with HermesB from an adult macaque monkey which provide insight into time scales and free behaviors inaccessible under traditional experiments. Variations in action potential shape and root-mean square (RMS) noise are observed across a range of time scales. The peak-to-peak voltage of action potentials varied by up to 30% over a 24-h period including step changes in waveform amplitude (up to 25%) coincident with high acceleration movements of the head. These initial results suggest that spike-sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance. During physically active periods (defined by head-mounted accelerometer), significantly reduced 5-25-Hz local field potential (LFP) power and increased firing rate variability were observed. Using a threshold fit to LFP power, 93% of 403 5-min recording blocks were correctly classified as active or inactive, potentially providing an efficient tool for identifying different behavioral contexts in prosthetic applications. These results demonstrate the utility of the HermesB system and motivate using this type of system to advance neural prosthetics and electrophysiological experiments.
View details for DOI 10.1109/TBME.2007.895753
View details for Web of Science ID 000250449200014
View details for PubMedID 18018699
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Single-neuron stability during repeated reaching in macaque premotor cortex
JOURNAL OF NEUROSCIENCE
2007; 27 (40): 10742-10750
Abstract
Some movements that animals and humans make are highly stereotyped, repeated with little variation. The patterns of neural activity associated with repeats of a movement may be highly similar, or the same movement may arise from different patterns of neural activity, if the brain exploits redundancies in the neural projections to muscles. We examined the stability of the relationship between neural activity and behavior. We asked whether the variability in neural activity that we observed during repeated reaching was consistent with a noisy but stable relationship, or with a changing relationship, between neural activity and behavior. Monkeys performed highly similar reaches under tight behavioral control, while many neurons in the dorsal aspect of premotor cortex and the primary motor cortex were simultaneously monitored for several hours. Neural activity was predominantly stable over time in all measured properties: firing rate, directional tuning, and contribution to a decoding model that predicted kinematics from neural activity. The small changes in neural activity that we did observe could be accounted for primarily by subtle changes in behavior. We conclude that the relationship between neural activity and practiced behavior is reasonably stable, at least on timescales of minutes up to 48 h. This finding has significant implications for the design of neural prosthetic systems because it suggests that device recalibration need not be overly frequent, It also has implications for studies of neural plasticity because a stable baseline permits identification of nonstationary shifts.
View details for DOI 10.1523/JNEUROSCI.0959-07.2007
View details for Web of Science ID 000249981400012
View details for PubMedID 17913908
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Free-paced high-performance brain-computer interfaces
JOURNAL OF NEURAL ENGINEERING
2007; 4 (3): 336-347
Abstract
Neural prostheses aim to improve the quality of life of severely disabled patients by translating neural activity into control signals for guiding prosthetic devices or computer cursors. We recently demonstrated that plan activity from premotor cortex, which specifies the endpoint of the upcoming arm movement, can be used to swiftly and accurately guide computer cursors to the desired target locations. However, these systems currently require additional, non-neural information to specify when plan activity is present. We report here the design and performance of state estimator algorithms for automatically detecting the presence of plan activity using neural activity alone. Prosthesis performance was nearly as good when state estimation was used as when perfect plan timing information was provided separately ( approximately 5 percentage points lower, when using 200 ms of plan activity). These results strongly suggest that a completely neurally-driven high-performance brain-computer interface is possible.
View details for DOI 10.1088/1741-2560/4/3/018
View details for Web of Science ID 000250181600025
View details for PubMedID 17873435
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Reference frames for reach planning in macaque dorsal premotor cortex
JOURNAL OF NEUROPHYSIOLOGY
2007; 98 (2): 966-983
Abstract
When a human or animal reaches out to grasp an object, the brain rapidly computes a pattern of muscular contractions that can acquire the target. This computation involves a reference frame transformation because the target's position is initially available only in a visual reference frame, yet the required control signal is a set of commands to the musculature. One of the core brain areas involved in visually guided reaching is the dorsal aspect of the premotor cortex (PMd). Using chronically implanted electrode arrays in two Rhesus monkeys, we studied the contributions of PMd to the reference frame transformation for reaching. PMd neurons are influenced by the locations of reach targets relative to both the arm and the eyes. Some neurons encode reach goals using limb-centered reference frames, whereas others employ eye-centered reference fames. Some cells encode reach goals in a reference frame best described by the combined position of the eyes and hand. In addition to neurons like these where a reference frame could be identified, PMd also contains cells that are influenced by both the eye- and limb-centered locations of reach goals but for which a distinct reference frame could not be determined. We propose two interpretations for these neurons. First, they may encode reach goals using a reference frame we did not investigate, such as intrinsic reference frames. Second, they may not be adequately characterized by any reference frame.
View details for DOI 10.1152/jn.00421.2006
View details for Web of Science ID 000248601100036
View details for PubMedID 17581846
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Mixture of trajectory models for neural decoding of goal-directed movements
JOURNAL OF NEUROPHYSIOLOGY
2007; 97 (5): 3763-3780
Abstract
Probabilistic decoding techniques have been used successfully to infer time-evolving physical state, such as arm trajectory or the path of a foraging rat, from neural data. A vital element of such decoders is the trajectory model, expressing knowledge about the statistical regularities of the movements. Unfortunately, trajectory models that both 1) accurately describe the movement statistics and 2) admit decoders with relatively low computational demands can be hard to construct. Simple models are computationally inexpensive, but often inaccurate. More complex models may gain accuracy, but at the expense of higher computational cost, hindering their use for real-time decoding. Here, we present a new general approach to defining trajectory models that simultaneously meets both requirements. The core idea is to combine simple trajectory models, each accurate within a limited regime of movement, in a probabilistic mixture of trajectory models (MTM). We demonstrate the utility of the approach by using an MTM decoder to infer goal-directed reaching movements to multiple discrete goals from multi-electrode neural data recorded in monkey motor and premotor cortex. Compared with decoders using simpler trajectory models, the MTM decoder reduced the decoding error by 38 (48) percent in two monkeys using 98 (99) units, without a necessary increase in running time. When available, prior information about the identity of the upcoming reach goal can be incorporated in a principled way, further reducing the decoding error by 20 (11) percent. Taken together, these advances should allow prosthetic cursors or limbs to be moved more accurately toward intended reach goals.
View details for DOI 10.1152/jn.00482.2006
View details for Web of Science ID 000247933500055
View details for PubMedID 17329627
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Comparison of the biomechanical stability of dense cancellous allograft with tricortical iliac autograft and fibular allograft for cervical interbody fusion
EUROPEAN SPINE JOURNAL
2006; 15 (9): 1339-1345
Abstract
Several choices are available for cervical interbody fusion after anterior cervical discectomy. A recent option is dense cancellous allograft (CS) which is characterized by an open-matrix structure that may promote vascularization and cellular penetration during early osseous integration. However, the biomechanical stability of CS should be comparable to that of the tricortical iliac autograft (AG) and fibular allograft (FA) to be an acceptable alternative to these materials. The purpose of this study was to compare the initial biomechanical stability of CS to that of AG and FA in a one-level anterior cervical discectomy and interbody fusion (ACDF) model. Twelve human cervical spines (C3-T1) were loaded in six modes of motion and evaluated under three conditions: (1) intact, (2) after ACDF using CS, AG, and FA in alternating sequences, and (3) after ACDF with anterior plating. Three reflective markers were placed on the adjacent vertebral bodies. Intervertebral motion was measured with a video-based motion-capture system (MacReflex, Qualisys, Sweden). Torques were applied to a maximum of 2.0 N m. The range-of-motion and neutral-zone values measured in each loading mode were compared. No graft material displayed significant differences in biomechanical stability in any of the tested loading modes, suggesting that the initial stability of CS is comparable to that of AG and FA. Anterior cervical plating significantly increased biomechanical stability in all modes.
View details for DOI 10.1007/s00586-005-0047-y
View details for Web of Science ID 000240362500005
View details for PubMedID 16429289
View details for PubMedCentralID PMC2438562
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Neural variability in premotor cortex provides a signature of motor preparation
JOURNAL OF NEUROSCIENCE
2006; 26 (14): 3697-3712
Abstract
We present experiments and analyses designed to test the idea that firing rates in premotor cortex become optimized during motor preparation, approaching their ideal values over time. We measured the across-trial variability of neural responses in dorsal premotor cortex of three monkeys performing a delayed-reach task. Such variability was initially high, but declined after target onset, and was maintained at a rough plateau during the delay. An additional decline was observed after the go cue. Between target onset and movement onset, variability declined by an average of 34%. This decline in variability was observed even when mean firing rate changed little. We hypothesize that this effect is related to the progress of motor preparation. In this interpretation, firing rates are initially variable across trials but are brought, over time, to their "appropriate" values, becoming consistent in the process. Consistent with this hypothesis, reaction times were longer if the go cue was presented shortly after target onset, when variability was still high, and were shorter if the go cue was presented well after target onset, when variability had fallen to its plateau. A similar effect was observed for the natural variability in reaction time: longer (shorter) reaction times tended to occur on trials in which firing rates were more (less) variable. These results reveal a remarkable degree of temporal structure in the variability of cortical neurons. The relationship with reaction time argues that the changes in variability approximately track the progress of motor preparation.
View details for DOI 10.1523/JNEUROSCI.3762-05.2006
View details for Web of Science ID 000236552400012
View details for PubMedID 16597724
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A prospective randomized study comparing a cervical carbon fiber cage to the Smith-Robinson technique with allograft and plating: up to 24 months follow-up
EUROPEAN SPINE JOURNAL
2006; 15 (2): 157-164
Abstract
Intervertebral carbon fiber cages may reduce graft collapse and promote bony fusion. Their safety and efficacy in the cervical spine have been investigated; however, no study has compared the outcomes of anterior cervical decompression and placement of a carbon fiber cage with placement of allograft and plate.Forty consecutive patients who met inclusion criteria were enrolled and randomized to anterior cervical discectomy with carbon fiber cage alone (n=20) or with allograft with plating (n=20). Clinical and radiographic evaluations were performed at baseline and at 6 weeks, 3, 6, 12 and 24 months. Neck and arm pain as well as neck disability index (NDI) were assessed at every visit. The Short Form (SF)-36 was completed prior to operation and at 12-month intervals. Cervical radiographs were evaluated pre-op and at every follow-up for evidence of fusion and instability.No significant difference was found between the two randomized groups with respect to pre-operative age (mean 50 years), sex, employment status, duration of pain or cervical levels affected. The mean follow-up period was 14 months (range, 6-26 months). The clinical pain and disability improvements were similar for both treatments. Post-operative donor site pain was only present in the cage group, but not of significant long-term disability. At up to 24 months, NDI scores were significantly improved in both groups when compared with baseline. At 12 and 24 months, all SF-36 questionnaire responses were also improved in both the treatment groups. However, there was no statistically significant difference in outcomes between the two groups at any time. The fusion rate was 100% in both groups by 12 and 24 months, without evidence of instability. There were no differences in complications between both groups.The outcomes after cervical decompression and placement of a carbon fiber cage appear to be similar to cervical decompression with allograft and plating by the Smith-Robinson technique.
View details for DOI 10.1007/s00586-005-0951-1
View details for PubMedID 15980998
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Increasing the performance of cortically-controlled prostheses.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2006: 6652-6656
Abstract
Neural prostheses have received considerable attention due to their potential to dramatically improve the quality of life of severely disabled patients. Cortically-controlled prostheses are able to translate neural activity from cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. Non-invasive and invasive electrode techniques can be used to measure neural activity, with the latter promising considerably higher levels of performance and therefore functionality to patients. We review here some of our recent experimental and computational work aimed at establishing a principled design methodology to increase electrode-based cortical prosthesis performance to near theoretical limits. Studies discussed include translating unprecedentedly brief periods of "plan" activity into high information rate (6.5 bits/s)control signals, improving decode algorithms and optimizing visual target locations for further performance increases, and recording from chronically implanted arrays in freely behaving monkeys to characterize neuron stability. Taken together, these results should substantially increase the clinical viability of cortical prostheses.
View details for PubMedID 17959477
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Neural rklecording stability of chronic electrode arrays in freely behaving primates
28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2006: 3784–3788
View details for Web of Science ID 000247284704099
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An autonomous, broadband, multi-channel neural recording system for freely behaving primates
28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2006: 3780–3783
View details for Web of Science ID 000247284704098
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Neural recording stability of chronic electrode arrays in freely behaving primates.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2006; 1: 4387-4391
Abstract
Chronically implanted electrode arrays have enabled a broad range of advances, particularly in the field of neural prosthetics. Those successes motivate development of prototype implantable prosthetic processors for long duration, continuous use in freely behaving subjects. However, traditional experimental protocols have provided limited information regarding the stability of the electrode arrays and their neural recordings. In this paper we present preliminary results derived from long duration neural recordings in a freely behaving primate which show variations in action potential shape and RMS noise across a range of time scales. These preliminary results suggest that spike sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance.
View details for PubMedID 17946626
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An autonomous, broadband, multi-channel neural recording system for freely behaving primates.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2006; 1: 1212-1215
Abstract
Successful laboratory proof-of-concept experiments with neural prosthetic systems motivate continued algorithm and hardware development. For these efforts to move beyond traditional fixed laboratory setups, new tools are needed to enable broadband, multi-channel, long duration neural recording from freely behaving primates. In this paper we present a dual-channel, battery powered, neural recording system with integrated 3-axis accelerometer for use with chronically implanted electrode arrays. The recording system, called HermesB, is self-contained, autonomous, programmable and capable of recording broadband neural and head acceleration data to a removable compact flash card for up to 48 hours.
View details for PubMedID 17946450
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Multiday electrophysiological recordings from freely behaving primates.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2006; 1: 5643-5646
Abstract
Continuous multiday broadband neural data provide a means for observing effects at fine timescales over long periods. In this paper we present analyses on such data sets to demonstrate neural correlates for physically active and inactive time periods, as defined by the response of a head-mounted accelerometer. During active periods, we found that 5-25 Hz local field potential (LFP) power was significantly reduced, firing rate variability increased, and firing rates have greater temporal correlation. Using a single threshold fit to LFP power, 93% of the 403 5 minute blocks tested were correctly classified as active or inactive (as labeled by thresholding each block's maximal accelerometer magnitude). These initial results motivate the use of such data sets for testing neural prosthetics systems and for finding the neural correlates of natural behaviors.
View details for PubMedID 17947159
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A high performance neurally-controlled cursor positioning system
2nd International IEEE/EMBS Conference on Neural Engineering
IEEE. 2005: 494–500
View details for Web of Science ID 000229610400133
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Improving neural prosthetic system performance by combining plan and peri-movement activity.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2004; 6: 4516-4519
Abstract
While most neural prosthetic systems to date estimate arm movements based solely on the activity prior to reaching movements during a delay period (plan activity) or solely on the activity during reaching movements (peri-movement activity), we show that decode classification can be improved by 56% and 71% respectively by using both types of activity together. We recorded from the pre-motor cortex of a rhesus monkey performing a delayed-reach task to one of seven targets. We found that taking into account the time-varying structure in peri-movement activity further improved performance by 15%, while doing the same for plan activity did not improve performance. We also found low correlations in activity between pairs of simultaneously-recorded units and across time periods within a given trial condition. These results show that decode performance can be significantly improved by combining information from the plan and peri-movement periods, and that there is nearly no loss in performance when assuming independence between units and across tune periods within a given trial condition.
View details for PubMedID 17271310
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An extensible infrastructure for fully automated spike sorting during online experiments.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2004; 6: 4380-4384
Abstract
When recording extracellular neural activity, it is often necessary to distinguish action potentials arising from distinct cells near the electrode tip, a process commonly referred to as "spike sorting." In a number of experiments, notably those that involve direct neuroprosthetic control of an effector, this cell-by-cell classification of the incoming signal must be achieved in real time. Several commercial offerings are available for this task, but all of these require some manual supervision per electrode, making each scheme cumbersome with large electrode counts. We present a new infrastructure that leverages existing unsupervised algorithms to sort and subsequently implement the resulting signal classification rules for each electrode using a commercially available Cerebus neural signal processor. We demonstrate an implementation of this infrastructure to classify signals from a cortical electrode array, using a probabilistic clustering algorithm (described elsewhere). The data were collected from a rhesus monkey performing a delayed center-out reach task. We used both sorted and unsorted (thresholded) action potentials from an array implanted in pre-motor cortex to "predict" the reach target, a common decoding operation in neuroprosthetic research. The use of sorted spikes led to an improvement in decoding accuracy of between 3.6 and 6.4%.
View details for PubMedID 17271276
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Improving neural prosthetic system performance by combining plan and peri-movement activity
26th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2004: 4516–4519
Abstract
While most neural prosthetic systems to date estimate arm movements based solely on the activity prior to reaching movements during a delay period (plan activity) or solely on the activity during reaching movements (peri-movement activity), we show that decode classification can be improved by 56% and 71% respectively by using both types of activity together. We recorded from the pre-motor cortex of a rhesus monkey performing a delayed-reach task to one of seven targets. We found that taking into account the time-varying structure in peri-movement activity further improved performance by 15%, while doing the same for plan activity did not improve performance. We also found low correlations in activity between pairs of simultaneously-recorded units and across time periods within a given trial condition. These results show that decode performance can be significantly improved by combining information from the plan and peri-movement periods, and that there is nearly no loss in performance when assuming independence between units and across tune periods within a given trial condition.
View details for Web of Science ID 000225461801180
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An extensible infrastructure for fully automated spike sorting during online experiments
26th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2004: 4380–4384
Abstract
When recording extracellular neural activity, it is often necessary to distinguish action potentials arising from distinct cells near the electrode tip, a process commonly referred to as "spike sorting." In a number of experiments, notably those that involve direct neuroprosthetic control of an effector, this cell-by-cell classification of the incoming signal must be achieved in real time. Several commercial offerings are available for this task, but all of these require some manual supervision per electrode, making each scheme cumbersome with large electrode counts. We present a new infrastructure that leverages existing unsupervised algorithms to sort and subsequently implement the resulting signal classification rules for each electrode using a commercially available Cerebus neural signal processor. We demonstrate an implementation of this infrastructure to classify signals from a cortical electrode array, using a probabilistic clustering algorithm (described elsewhere). The data were collected from a rhesus monkey performing a delayed center-out reach task. We used both sorted and unsorted (thresholded) action potentials from an array implanted in pre-motor cortex to "predict" the reach target, a common decoding operation in neuroprosthetic research. The use of sorted spikes led to an improvement in decoding accuracy of between 3.6 and 6.4%.
View details for Web of Science ID 000225461801145
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Model-based decoding of reaching movements for prosthetic systems
26th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2004: 4524–4528
Abstract
Model-based decoding of neural activity for neuroprosthetic systems has been shown, in simulation, to provide significant gain over traditional linear filter approaches. We tested the model-based decoding approach with real neural and behavioral data and found a 18% reduction in trajectory reconstruction error compared with a linear filter. This corresponds to a 40% reduction in the number of neurons required for equivalent performance. The model-based approach further permits the combination of target-tuned plan activity with movement activity. The addition of plan activity reduced reconstruction error by 23% relative to the linear filter, corresponding to 55% reduction in the number of neurons required. Taken together, these results indicate that a decoding algorithm employing a prior model of reaching kinematics can substantially improve trajectory estimates, thereby improving prosthetic system performance.
View details for Web of Science ID 000225461801182
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Model-based decoding of reaching movements for prosthetic systems.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
2004; 6: 4524-4528
Abstract
Model-based decoding of neural activity for neuroprosthetic systems has been shown, in simulation, to provide significant gain over traditional linear filter approaches. We tested the model-based decoding approach with real neural and behavioral data and found a 18% reduction in trajectory reconstruction error compared with a linear filter. This corresponds to a 40% reduction in the number of neurons required for equivalent performance. The model-based approach further permits the combination of target-tuned plan activity with movement activity. The addition of plan activity reduced reconstruction error by 23% relative to the linear filter, corresponding to 55% reduction in the number of neurons required. Taken together, these results indicate that a decoding algorithm employing a prior model of reaching kinematics can substantially improve trajectory estimates, thereby improving prosthetic system performance.
View details for PubMedID 17271312
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Asymptomatic transient MRI signal changes after unilateral deep brain stimulation electrode implantation for movement disorder
STEREOTACTIC AND FUNCTIONAL NEUROSURGERY
2004; 82 (2-3): 65-69
Abstract
Deep brain stimulation (DBS) is an accepted treatment of movement disorders, but little research on tissue changes induced by these devices has been made. We report findings of MRI signal changes in patients with unilateral DBS implantation and no clinically detectable symptoms. A retrospective review of preoperative stereotactic MRI scans for staged placement of second-side DBS was performed in 38 patients to assess the frequency of signal changes along the previously implanted DBS track. No abnormal signal changes were noted in 23 patients (61%). Increased subcortical signals on T2-weighted fast spin echo MRI sequences along the DBS track were noted in 15 patients (39%) and varied from circumferential hyperintensity along the electrode track to significant involvement of the subcortical white matter. The changes were only detected in scans performed within 3 months of DBS implantation (15 of 27 patients). Despite these changes, the patients were totally asymptomatic. The etiology of the changes is unknown but may reflect a transient tissue response to the implantation of the electrode.
View details for DOI 10.1159/000077402
View details for Web of Science ID 000223321600001
View details for PubMedID 15305076
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Stereotactic radiosurgery for hemangiomas and ependymomas of the spinal cord.
Neurosurgical focus
2003; 15 (5): E10-?
Abstract
The optimal treatment for intramedullary spinal tumors is controversial, because both resection and conventional radiation therapy are associated with potential morbidity. Stereotactic radiosurgery can theoretically deliver highly conformal, high-dose radiation to surgically untreatable lesions while simultaneously mitigating radiation exposure to large portions of the spinal cord. The purpose of this study was to evaluate the authors' initial experience with frameless stereotactic radiosurgery for intramedullary spinal tumors.Between 1998 and 2003, 10 intramedullary spinal tumors were treated with stereotactic radiosurgery at the authors' institution. Seven hemangioblastomas and three ependymomas were treated in four men and three women. These patients either had recurrent tumors, had undergone several previous surgeries, had medical contraindications to surgery, or had declined open resection. Conformal treatment planning delivered a prescribed dose of 1800 to 2500 cGy (mean 2100 cGy) to the lesions in one to three stages. No significant treatment-related complications have been recorded. The mean radiographic and clinical follow-up duration was 12 months (range 1-24 months). One ependymoma and two hemangioblastomas were smaller on follow-up neuroimaging. The remaining tumors were stable at the time of follow-up imaging.Stereotactic radiosurgery for intramedullary spinal tumors is feasible and safe in selected cases and may prove to be another therapeutic option for these challenging lesions.
View details for PubMedID 15323467
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Surgical management and results of 135 tibial nerve lesions at the Louisiana State University Health Sciences Center
NEUROSURGERY
2003; 53 (5): 1114-1124
Abstract
This retrospective study presents 33 years of clinical and surgical experience with 135 tibial nerve lesions to review operative techniques and their results and to provide management guidelines for the proper selection of surgical candidates.Between 1967 and 1999, 135 patients with tibial nerve lesions at the knee level or below were managed surgically at the Louisiana State University Health Sciences Center. We reviewed these cases.Of the 135 cases, traumatic injury accounted for 71, tarsal tunnel syndrome for 46, and nerve sheath tumor for 18. Of 22 lesions not in continuity, functional recovery of Grade 3 or better was achieved in 4 (67%) of 6 patients who required end-to-end suture repair and 11 (69%) of 16 patients who required graft repair. One hundred thirteen tibial nerve lesions in continuity underwent primarily external or internal neurolysis or resection of the lesions. A few received end-to-end suture or graft repair. Direct intraoperative recording of nerve action potentials guided case management decisions. Among the 113 patients with lesions in continuity, 76 (81%) of 94 patients receiving neurolysis, 5 (83%) of 6 receiving suture repair, and 11 (85%) of 13 receiving graft repair recovered function to Grade 3 or better. Repair results were best in patients with recordable nerve action potentials treated by external neurolysis. Results were poor in a few patients with very lengthy lesions in continuity and in reoperated patients with tarsal tunnel syndrome.Surgical exploration and repair of tibial nerve lesions, including nerve sheath tumors and tarsal tunnel syndromes, achieved excellent outcomes.
View details for PubMedID 14580278
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Image-guided spinal stereotactic radiosurgery
TECHNIQUES IN NEUROSURGERY
2003; 8 (1): 56-64
View details for Web of Science ID 000181739000008
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Influence of movement speed on plan activity in monkey pre-motor cortex and implications for high-performance neural prosthetic system design
25th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
IEEE. 2003: 1897–1900
View details for Web of Science ID 000189395300496
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Image-guided hypo-fractionated stereotactic radiosurgery to spinal lesions
NEUROSURGERY
2001; 49 (4): 838-846
Abstract
This article demonstrates the technical feasibility of noninvasive treatment of unresectable spinal vascular malformations and primary and metastatic spinal tumors by use of image-guided frameless stereotactic radiosurgery.Stereotactic radiosurgery delivers a high dose of radiation to a tumor volume or vascular malformation in a limited number of fractions and minimizes the dose to adjacent normal structures. Frameless image-guided radiosurgery was developed by coupling an orthogonal pair of x-ray cameras to a dynamically manipulated robot-mounted linear accelerator that guides the therapy beam to treatment sites within the spine or spinal cord, in an outpatient setting, and without the use of frame-based fixation. The system relies on skeletal landmarks or implanted fiducial markers to locate treatment targets. Sixteen patients with spinal lesions (hemangioblastomas, vascular malformations, metastatic carcinomas, schwannomas, a meningioma, and a chordoma) were treated with total treatment doses of 1100 to 2500 cGy in one to five fractions by use of image-guided frameless radiosurgery with the CyberKnife system (Accuray, Inc., Sunnyvale, CA). Thirteen radiosurgery plans were analyzed for compliance with conventional radiation therapy.Tests demonstrated alignment of the treatment dose with the target volume within +/-1 mm by use of spine fiducials and the CyberKnife treatment planning system. Tumor patients with at least 6 months of follow-up have demonstrated no progression of disease. Radiographic follow-up is pending for the remaining patients. To date, no patients have experienced complications as a result of the procedure.This experience demonstrates the feasibility of image-guided robotic radiosurgery for previously untreatable spinal lesions.
View details for PubMedID 11564244
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Posterior cerebral circulation revascularization
NEUROSURGERY CLINICS OF NORTH AMERICA
2001; 12 (3): 519-?
Abstract
Posterior circulation revascularization has evolved as a method to treat selected patients with vertebrobasilar ischemia who have inaccessible atherosclerotic occlusive disease and who have failed maximal medical therapy. In addition, complex unclippable aneurysms of the posterior circulation are another indication for revascularization of the vertebrobasilar territory. Careful preoperative evaluation and meticulous attention to detail intraoperatively yield good patient outcomes with minimal morbidity and mortality. This article reviews the vascular anatomy of the posterior circulation and the indications, preoperative evaluation, operative techniques, clinical outcomes, and alternative treatments for patients requiring posterior circulation revascularization procedures.
View details for Web of Science ID 000169857500006
View details for PubMedID 11390312