Stephen I. Ryu

Stimulus onset quenches neural variability: a widespread cortical phenomenon

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.

A high-performance brain–computer interface

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 ∼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.

Neural population dynamics during reaching

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.

Image-guided Hypo-fractionated Stereotactic Radiosurgery to Spinal Lesions

OBJECTIVEThis 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. METHODSStereotactic 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. RESULTSTests 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. CONCLUSIONThis experience demonstrates the feasibility of image-guided robotic radiosurgery for previously untreatable spinal lesions.

A high-performance neural prosthesis enabled by control algorithm design

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.

Neural variability in premotor cortex provides a signature of motor preparation

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.

Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

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 subjects 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.

Wireless Neural Recording With Single Low-Power Integrated Circuit

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.

Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

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.

Neural constraints on learning

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.