A cortical-spinal prosthesis for targeted limb movement in paralyzed primate avatars

Motor paralysis is among the most disabling aspects of injury to the central nervous system. Here we develop and test a target-based cortical-spinal neural prosthesis that employs neural activity recorded from pre-motor neurons to control limb movements in functionally paralyzed primate avatars. Given the complexity by which muscle contractions are naturally controlled, we approach the problem of eliciting goal-directed limb movement in paralyzed animals by focusing on the intended targets of movement rather than their intermediate trajectories. We then match this information in real-time with spinal cord and muscle stimulation parameters that produce free planar limb movements to those intended target locations. We demonstrate that both the decoded activities of pre-motor populations and their adaptive responses can be used, after brief training, to effectively direct an avatar’s limb to distinct targets variably displayed on a screen. These findings advance the future possibility of reconstituting targeted limb movement in paralyzed subjects

Universal codes for parallel Gaussian channels

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 97).In this thesis we study the design of universal codes for parallel Gaussian channels with 2 sub-channels present. We study the universality both in terms of the uncertainty in the relative quality of the two sub-channels for a fixed maximum rate, C*, and in terms of the uncertainty of the achievable maximum rate. In our architecture, we will convert the parallel Gaussian channel into a set of scalar Gaussian channels and use good base codes designed for the corresponding scalar channel in the coding schemes. In Chapter 2, a universal layered code with deterministic dithers is developed. The code is repeated across the two sub-channels with possibly different dithers. Symbols in each of the layer codewords can be combined using unitary transformations of dimension, m. A minimum mean squared error (MMSE) receiver combined with successive cancellation is used for decoding. We show that increasing m does not improve the efficiency. The efficiency increases by adding more layers up to a certain number and after that it saturates. We find an expression for this saturation efficiency. We show that partial CSIT improves the efficiency significantly. At the end we compare the performance of maximal ratio combining (MRC) and MMSE receivers and show that they are close in the coding scheme with no CSIT. In Chapter 3, we design an alternative universal code and extend it to be rateless. This is a sub-block structured code symmetric with respect to all layers that gets repeated across the two sub-channels and in time using i.i.d. Bernoulli (1/2) dithers. The decoder uses an MRC receiver combined with successive cancellation. We prove that in the limit of large L when L is increased exponentially with C*, the code is capacity achieving. We perform efficiency analyses when L is scaled linearly with C* and derive upper and lower bounds on the efficiency. We also show that the scheme has high efficiencies for practical ranges of C* using a low-rate good base code. We discuss the unknown time-varying behavior of the scheme and at the end briefly discuss the use of faster than Nyquist signaling to enable the scheme to have a high efficiency for higher C* values.by Maryam Modir Shanechi.S.M

Real-time brain-machine interface architectures : neural decoding from plan to movement

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-135).Brain-machine interfaces (BMI) aim to enable motor function in individuals with neurological injury or disease, by recording the neural activity, mapping or 'decoding' it into a motor command, and then controlling a device such as a computer interface or robotic arm. BMI research has largely focused on the problem of restoring the original motor function. The goal therefore has been to achieve a performance close to that of the healthy individual. There have been compelling proof of concept demonstrations of the utility of such BMIs in the past decade. However, performance of these systems needs to be significantly improved before they become clinically viable. Moreover, while developing high-performance BMIs with the goal of matching the original motor function is indeed valuable, a compelling goal is that of designing BMIs that can surpass original motor function. In this thesis, we first develop a novel real-time BMI for restoration of natural motor function. We then introduce a BMI architecture aimed at enhancing original motor function. We implement both our designs in rhesus monkeys. To facilitate the restoration of lost motor function, BMIs have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Moreover, both target and trajectory information are encoded in the motor cortical areas. These suggest that BMIs should be designed to combine these principal aspects of movement. We develop a novel two-stage BMI to decode jointly the target and trajectory of a reaching movement. First, we decode the intended target from neural spiking activity before movement initiation. Second, we combine the decoded target with the spiking activity during movement to estimate the trajectory. To do so, we use an optimal feedback-control design that aims to emulate the sensorimotor processing underlying actual motor control and directly processes the spiking activity using point process modeling in real time. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. This BMI also performs significantly better than linear regression approaches demonstrating the advantage of a design that more closely mimics the sensorimotor system.(cont.) While restoring the original motor function is indeed important, a compelling goal is the development of a truly "intelligent" BMI that can transcend such function by considering the higherlevel goal of the motor activity, and reformulating the motor plan accordingly. This would allow, for example, a task to be performed more quickly than possible by natural movement, or more efficiently than originally conceived. Since a typical motor activity consists of a sequence of planned movements, such a BMI must be capable of analyzing the complete sequence before action. As such its feasibility hinges fundamentally on whether all elements of the motor plan can be decoded concurrently from working memory. Here we demonstrate that such concurrent decoding is possible. In particular, we develop and implement a real-time BMI that accurately and simultaneously decodes in advance a sequence of planned movements from neural activity in the premotor cortex. In our experiments, monkeys were trained to add to working memory, in order, two distinct target locations on a screen, then move a cursor to each, in sequence. We find that the two elements of the motor plan, corresponding to the two targets, are encoded concurrently during the working memory period. Additionally, and interestingly, our results reveal: that the elements of the plan are encoded by largely disjoint subpopulations of neurons; that surprisingly small subpopulations are sufficient for reliable decoding of the motor plan; and that the subpopulation dedicated to the first target and their responses are largely unchanged when the second target is added to working memory, so that the process of adding information does not compromise the integrity of existing information. The results have significant implications for the architecture and design of future generations of BMIs with enhanced motor function capabilities.by Maryam Modir Shanechi.Ph.D

A Brain-Machine Interface for Control of Medically-Induced Coma

Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.National Institutes of Health (U.S.) (Director's Transformative Award R01 GM104948)National Institutes of Health (U.S.) (Pioneer Award DP1-OD003646)National Institutes of Health (U.S.) (NIH K08-GM094394)Massachusetts General Hospital. Dept. of Anesthesia and Critical Car

Neural population partitioning and a concurrent brain-machine interface for sequential motor function

Although brain-machine interfaces (BMIs) have focused largely on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence before its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Using population-wide modeling, we discovered two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such marked stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. On the basis of these findings, we developed a BMI that concurrently decodes a full motor sequence in advance of movement and can then accurately execute it as desired.National Institutes of Health (U.S.) (DP1 OD003646

Neural population partitioning and a concurrent brain-machine interface for sequential motor function

While brain-machine interfaces (BMIs) have largely focused on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence prior to its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Here, we show that concurrent BMI decoding is possible. Using population-wide modeling, we discover two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such surprising stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. Based on these findings, we develop a BMI that concurrently decodes a full motor sequence in advance of movement and then can accurately execute it as desired

A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.National Institutes of Health (U.S.) (NIH grant No.DP1-0D003646-01)National Institutes of Health (U.S.) (NIH grant R01-EB006385

Confidence Prediction from EEG Recordings in a Multisensory Environment

This paper investigates the possibility of decoding decision confidence from electroencephalographic (EEG) brain activity of human subjects during a multisensory decision-making task. In recent research we have shown that decision confidence correlates could be extracted from EEG recordings during visual or auditory tasks. Here we extend these initial findings by (a) predicting the confidence in the decision from EEG recordings alone, and (b) investigating the impact of multisensory cues on decision-making behavioral data. Our results obtained from 12 participants recorded at two different sites show that the decision confidence could be predicted from EEG recordings on a single-trial basis with a mean absolute error of 0.226. Moreover, the presence of a multisensory cue did not improve the performance of the participants, but rather distracted them from the main task. Overall, these results may inform the development of cognitive systems that could monitor and alert users when they are not confident about their decisions

Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning

Reinforcement Learning (RL) can be used to fit a mapping from patient state to a medication regimen. Prior studies have used deterministic and value-based tabular learning to learn a propofol dose from an observed anesthetic state. Deep RL replaces the table with a deep neural network and has been used to learn medication regimens from registry databases. Here we perform the first application of deep RL to closed-loop control of anesthetic dosing in a simulated environment. We use the cross-entropy method to train a deep neural network to map an observed anesthetic state to a probability of infusing a fixed propofol dosage. During testing, we implement a deterministic policy that transforms the probability of infusion to a continuous infusion rate. The model is trained and tested on simulated pharmacokinetic/pharmacodynamic models with randomized parameters to ensure robustness to patient variability. The deep RL agent significantly outperformed a proportional-integral-derivative controller (median absolute performance error 1.7% +/- 0.6 and 3.4% +/- 1.2). Modeling continuous input variables instead of a table affords more robust pattern recognition and utilizes our prior domain knowledge. Deep RL learned a smooth policy with a natural interpretation to data scientists and anesthesia care providers alike.Comment: International Conference on Artificial Intelligence in Medicine 202

Defining the Ischemic Penumbra Using Magnetic Resonance Oxygen Metabolic Index

Penumbral biomarkers promise to individualize treatment windows in acute ischemic stroke. We used a novel MRI approach which measures oxygen metabolic index (OMI), a parameter closely related to PET-derived cerebral metabolic rate of oxygen utilization, to derive a pair of ischemic thresholds: (1) an irreversible-injury threshold which differentiates ischemic core from penumbra and (2) a reversible-injury threshold which differentiates penumbra from tissue not-at-risk for infarction