Cortical neuroprosthetics from a clinical perspective

Robust and accurate decoding of hand kinematics from entire spiking activity using deep learning

OBJECTIVE

Brain-machine interfaces (BMIs) seek to restore lost motor functions in individuals with neurological disorders by enabling them to control external devices directly with their thoughts. This work aims to improve robustness and decoding accuracy that currently become major challenges in the clinical translation of intracortical BMIs.

APPROACH

We propose entire spiking activity (ESA) -an envelope of spiking activity that can be extracted by a simple, threshold-less, and automated technique- as the input signal. We couple ESA with deep learning-based decoding algorithm that uses quasi-recurrent neural network (QRNN) architecture. We evaluate comprehensively the performance of ESA-driven QRNN decoder for decoding hand kinematics from neural signals chronically recorded from the primary motor cortex area of three non-human primates performing different tasks.

MAIN RESULTS

Our proposed method yields consistently higher decoding performance than any other combinations of the input signal and decoding algorithm previously reported across long term recording sessions. It can sustain high decoding performance even when removing spikes from the raw signals, when using the different number of channels, and when using a smaller amount of training data.

SIGNIFICANCE

Overall results demonstrate exceptionally high decoding accuracy and chronic robustness, which is highly desirable given it is an unresolved challenge in BMIs.

Injecting Information into the Mammalian Cortex: Progress, Challenges, and Promise

For 150 years artificial stimulation has been used to study the function of the nervous system. Such stimulation-whether electrical or optogenetic-eventually may be used in neuroprosthetic devices to replace lost sensory inputs and to otherwise introduce information into the nervous system. Efforts toward this goal can be classified broadly as either biomimetic or arbitrary. Biomimetic stimulation aims to mimic patterns of natural neural activity, so that the subject immediately experiences the artificial stimulation as if it were natural sensation. Arbitrary stimulation, in contrast, makes no attempt to mimic natural patterns of neural activity. Instead, different stimuli-at different locations and/or in different patterns-are assigned different meanings randomly. The subject's time and effort then are required to learn to interpret different stimuli, a process that engages the brain's inherent plasticity. Here we will examine progress in using artificial stimulation to inject information into the cerebral cortex and discuss the challenges for and the promise of future development.

Mechanically Robust, Softening Shape Memory Polymer Probes for Intracortical Recording

While intracortical microelectrode arrays (MEAs) may be useful in a variety of basic and clinical scenarios, their implementation is hindered by a variety of factors, many of which are related to the stiff material composition of the device. MEAs are often fabricated from high modulus materials such as silicon, leaving devices vulnerable to brittle fracture and thus complicating device fabrication and handling. For this reason, polymer-based devices are being heavily investigated; however, their implementation is often difficult due to mechanical instability that requires insertion aids during implantation. In this study, we design and fabricate intracortical MEAs from a shape memory polymer (SMP) substrate that remains stiff at room temperature but softens to 20 MPa after implantation, therefore allowing the device to be implanted without aids. We demonstrate chronic recordings and electrochemical measurements for 16 weeks in rat cortex and show that the devices are robust to physical deformation, therefore making them advantageous for surgical implementation.

Real-Time Performance of a Tactile Neuroprosthesis on Awake Behaving Rats

With the advancement of electrode and equipment technology, neuroprosthetics have become a promising alternative to partially compensate for the loss of sensorimotor function in amputees and patients with neurological diseases. Cortical neural interfaces are suitable especially for spinal cord injuries and amyotrophic lateral sclerosis. Although considerable success has been achieved in the literature by spike decoding of motor signals from the human brain, somatosensory feedback is essential for better motor control, interaction with objects, and the embodiment of prosthetic devices. In this paper, we present a tactile neuroprosthesis for rats based on intracortical microstimulation (ICMS). The rats wore mechanically-isolated boots covered with tactile sensors while performing a psychophysical detection task. The vibrotactile stimuli were measured by the artificial sensors and by using a real-time processor, this information was converted to electrical current pulses for ICMS. Some parameters of the real-time processor algorithm were specific to individual rats and were based on psychometric equivalence functions established earlier. Rats could detect the effects of the vibrotactile stimuli better (i.e., higher sensitivity indices) when the tactile neuroprosthesis was switched on compared to the boot only condition during active movement. In other words, the rats could decode the tactile information embedded in ICMS and use that in a behaviorally relevant manner. The presented animal model without peripheral nerve injury or amputation is also a promising tool to test various hardware and software components of neuroprosthetic systems in general.

Volitional control of single-electrode high gamma local field potentials by people with paralysis

Intracortical brain-computer interfaces (BCIs) can enable individuals to control effectors, such as a computer cursor, by directly decoding the user's movement intentions from action potentials and local field potentials (LFPs) recorded within the motor cortex. However, the accuracy and complexity of effector control achieved with such "biomimetic" BCIs will depend on the degree to which the intended movements used to elicit control modulate the neural activity. In particular, channels that do not record distinguishable action potentials and only record LFP modulations may be of limited use for BCI control. In contrast, a biofeedback approach may surpass these limitations by letting the participants generate new control signals and learn strategies that improve the volitional control of signals used for effector control. Here, we show that, by using a biofeedback paradigm, three individuals with tetraplegia achieved volitional control of gamma LFPs (40-400 Hz) recorded by a single microelectrode implanted in the precentral gyrus. Control was improved over a pair of consecutive sessions up to 3 days apart. In all but one session, the channel used to achieve control lacked distinguishable action potentials. Our results indicate that biofeedback LFP-based BCIs may potentially contribute to the neural modulation necessary to obtain reliable and useful control of effectors. NEW & NOTEWORTHY Our study demonstrates that people with tetraplegia can volitionally control individual high-gamma local-field potential (LFP) channels recorded from the motor cortex, and that this control can be improved using biofeedback. Motor cortical LFP signals are thought to be both informative and stable intracortical signals and, thus, of importance for future brain-computer interfaces.

Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements

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.

Totally Implantable Bidirectional Neural Prostheses: A Flexible Platform for Innovation in Neuromodulation

Implantable neural prostheses are in widespread use for treating a variety of brain disorders. Until recently, most implantable brain devices have been unidirectional, either delivering neurostimulation without brain sensing, or sensing brain activity to drive external effectors without a stimulation component. Further, many neural interfaces that incorporate a sensing function have relied on hardwired connections, such that subjects are tethered to external computers and cannot move freely. A new generation of neural prostheses has become available, that are both bidirectional (stimulate as well as record brain activity) and totally implantable (no externalized connections). These devices provide an opportunity for discovering the circuit basis for neuropsychiatric disorders, and to prototype personalized neuromodulation therapies that selectively interrupt neural activity underlying specific signs and symptoms.

A Meta-Analysis of Intracortical Device Stiffness and Its Correlation with Histological Outcomes

Neural implants offer solutions for a variety of clinical issues. While commercially available devices can record neural signals for short time periods, they fail to do so chronically, partially due to the sustained tissue response around the device. Our objective was to assess the correlation between device stiffness, a function of both material modulus and cross-sectional area, and the severity of immune response. Meta-analysis data were derived from nine previously published studies which reported device material and geometric properties, as well as histological outcomes. Device bending stiffness was calculated by treating the device shank as a cantilevered beam. Immune response was quantified through analysis of immunohistological images from each study, specifically looking at fluorescent markers for neuronal nuclei and astrocytes, to assess neuronal dieback and gliosis. Results demonstrate that the severity of the immune response, within the first 50 µm of the device, is highly correlated with device stiffness, as opposed to device modulus or cross-sectional area independently. In general, commercially available devices are around two to three orders of magnitude higher in stiffness than devices which induced a minimal tissue response. These results have implications for future device designs aiming to decrease chronic tissue response and achieve increased long-term functionality.

The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces

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.

The adaptive deep brain stimulation challenge

Sub-optimal clinical outcomes of conventional deep brain stimulation (cDBS) in treating Parkinson's Disease (PD) have boosted the development of new solutions to improve DBS therapy. Adaptive DBS (aDBS), consisting of closed-loop, real-time changing of stimulation parameters according to the patient's clinical state, promises to achieve this goal and is attracting increasing interest in overcoming all of the challenges posed by its development and adoption. In the design, implementation, and application of aDBS, the choice of the control variable and of the control algorithm represents the core challenge. The proposed approaches, in fact, differ in the choice of the control variable and control policy, in the system design and its technological limits, in the patient's target symptom, and in the surgical procedure needed. Here, we review the current proposals for aDBS systems, focusing on the choice of the control variable and its advantages and drawbacks, thus providing a general overview of the possible pathways for the clinical translation of aDBS with its benefits, limitations and unsolved issues.