An adaptive and generalizable closed-loop system for control of medically induced coma and other states of anesthesia

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Objective.Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales.Approach.Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical SID method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and with spiking and local field potential population activity recorded during a naturalistic reach and grasp behavior.Main results.We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior.Significance.Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.

Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity

Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study

Introduction

Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.

Methods

Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.

Results

We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.

Discussion

Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient's condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain pre-clinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of [Formula: see text]. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which [Formula: see text] of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave-one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of [Formula: see text] and [Formula: see text] NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

Modeling and inference methods for switching regime-dependent dynamical systems with multiscale neural observations

Objective.Realizing neurotechnologies that enable long-term neural recordings across multiple spatial-temporal scales during naturalistic behaviors requires new modeling and inference methods that can simultaneously address two challenges. First, the methods should aggregate information across all activity scales from multiple recording sources such as spiking and field potentials. Second, the methods should detect changes in the regimes of behavior and/or neural dynamics during naturalistic scenarios and long-term recordings. Prior regime detection methods are developed for a single scale of activity rather than multiscale activity, and prior multiscale methods have not considered regime switching and are for stationary cases.Approach.Here, we address both challenges by developing a switching multiscale dynamical system model and the associated filtering and smoothing methods. This model describes the encoding of an unobserved brain state in multiscale spike-field activity. It also allows for regime-switching dynamics using an unobserved regime state that dictates the dynamical and encoding parameters at every time-step. We also design the associated switching multiscale inference methods that estimate both the unobserved regime and brain states from simultaneous spike-field activity.Main results.We validate the methods in both extensive numerical simulations and prefrontal spike-field data recorded in a monkey performing saccades for fluid rewards. We show that these methods can successfully combine the spiking and field potential observations to simultaneously track the regime and brain states accurately. Doing so, these methods lead to better state estimation compared with single-scale switching methods or stationary multiscale methods. Also, for single-scale linear Gaussian observations, the new switching smoother can better generalize to diverse system settings compared to prior switching smoothers.Significance.These modeling and inference methods effectively incorporate both regime-detection and multiscale observations. As such, they could facilitate investigation of latent switching neural population dynamics and improve future brain-machine interfaces by enabling inference in naturalistic scenarios where regime-dependent multiscale activity and behavior arise.

Designing and Validating a Robust Adaptive Neuromodulation Algorithm for Closed-Loop Control of Brain States

OBJECTIVE

Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications.

APPROACH

We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity.

MAIN RESULTS

The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms.

SIGNIFICANCE

Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.

Modeling multiscale causal interactions between spiking and field potential signals during behavior

Objective.Brain recordings exhibit dynamics at multiple spatiotemporal scales, which are measured with spike trains and larger-scale field potential signals. To study neural processes, it is important to identify and model causal interactions not only at a single scale of activity, but also across multiple scales, i.e. between spike trains and field potential signals. Standard causality measures are not directly applicable here because spike trains are binary-valued but field potentials are continuous-valued. It is thus important to develop computational tools to recover multiscale neural causality during behavior, assess their performance on neural datasets, and study whether modeling multiscale causalities can improve the prediction of neural signals beyond what is possible with single-scale causality.Approach.We design a multiscale model-based Granger-like causality method based on directed information and evaluate its success both in realistic biophysical spike-field simulations and in motor cortical datasets from two non-human primates (NHP) performing a motor behavior. To compute multiscale causality, we learn point-process generalized linear models that predict the spike events at a given time based on the history of both spike trains and field potential signals. We also learn linear Gaussian models that predict the field potential signals at a given time based on their own history as well as either the history of binary spike events or that of latent firing rates.Main results.We find that our method reveals the true multiscale causality network structure in biophysical simulations despite the presence of model mismatch. Further, models with the identified multiscale causalities in the NHP neural datasets lead to better prediction of both spike trains and field potential signals compared to just modeling single-scale causalities. Finally, we find that latent firing rates are better predictors of field potential signals compared with the binary spike events in the NHP datasets.Significance.This multiscale causality method can reveal the directed functional interactions across spatiotemporal scales of brain activity to inform basic science investigations and neurotechnologies.

Enhancement of Closed-Loop Cognitive Stress Regulation using Supervised Control Architectures

Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

Closed-Loop Fuzzy Energy Regulation in Patients With Hypercortisolism via Inhibitory and Excitatory Intermittent Actuation

Hypercortisolism or Cushing's disease, which corresponds to the excessive levels of cortisol hormone, is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy levels in hypercortisolism. In the present simulation study, we generate multi-day cortisol profile data for ten subjects both in healthy and disease conditions. To relate an internal hidden cognitive energy state to one's cortisol secretion patterns, we employ a state-space model. Particularly, we consider circadian upper and lower bound envelopes on cortisol levels, and timings of hypothalamic pulsatile activity underlying cortisol secretions as continuous and binary observations, respectively. To estimate the hidden cognitive energy-related state, we use Bayesian filtering. In our proposed architecture, we infer one's cognitive energy-related state using wearable devices rather than monitoring the brain activity directly and close the loop utilizing fuzzy control. To model actuation in the real-time closed-loop architecture, we simulate two types of medications that result in increasing and decreasing the energy levels in the body. Finally, we close the loop using a knowledge-based control approach. The results on ten simulated profiles verify how the proposed architecture is able to track the energy state and regulate it using hypothetical medications. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. This simulation study is a first step toward the ultimate goal of managing hypercortisolism in real-world situations.

Improving scalability in systems neuroscience

Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.

Adaptive latent state modeling of brain network dynamics with real-time learning rate optimization

Objective. Dynamic latent state models are widely used to characterize the dynamics of brain network activity for various neural signal types. To date, dynamic latent state models have largely been developed for stationary brain network dynamics. However, brain network dynamics can be non-stationary for example due to learning, plasticity or recording instability. To enable modeling these non-stationarities, two problems need to be resolved. First, novel methods should be developed that can adaptively update the parameters of latent state models, which is difficult due to the state being latent. Second, new methods are needed to optimize the adaptation learning rate, which specifies how fast new neural observations update the model parameters and can significantly influence adaptation accuracy.Approach. We develop a Rate Optimized-adaptive Linear State-Space Modeling (RO-adaptive LSSM) algorithm that solves these two problems. First, to enable adaptation, we derive a computation- and memory-efficient adaptive LSSM fitting algorithm that updates the LSSM parameters recursively and in real time in the presence of the latent state. Second, we develop a real-time learning rate optimization algorithm. We use comprehensive simulations of a broad range of non-stationary brain network dynamics to validate both algorithms, which together constitute the RO-adaptive LSSM.Main results. We show that the adaptive LSSM fitting algorithm can accurately track the broad simulated non-stationary brain network dynamics. We also find that the learning rate significantly affects the LSSM fitting accuracy. Finally, we show that the real-time learning rate optimization algorithm can run in parallel with the adaptive LSSM fitting algorithm. Doing so, the combined RO-adaptive LSSM algorithm rapidly converges to the optimal learning rate and accurately tracks non-stationarities.Significance. These algorithms can be used to study time-varying neural dynamics underlying various brain functions and enhance future neurotechnologies such as brain-machine interfaces and closed-loop brain stimulation systems.

Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation

Direct electrical stimulation can modulate the activity of brain networks for the treatment of several neurological and neuropsychiatric disorders and for restoring lost function. However, precise neuromodulation in an individual requires the accurate modelling and prediction of the effects of stimulation on the activity of their large-scale brain networks. Here, we report the development of dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing microstimulation. In experiments with two awake rhesus macaques, we show that the activities of brain networks are modulated by changes in both stimulation amplitude and frequency, that they exhibit damping and oscillatory response dynamics, and that variabilities in prediction accuracy and in estimated response strength across brain regions can be explained by an at-rest functional connectivity measure computed without stimulation. Input-output models of brain dynamics may enable precise neuromodulation for the treatment of disease and facilitate the investigation of the functional organization of large-scale brain networks.

Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

Motor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode's decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification

Neural activity exhibits complex dynamics related to various brain functions, internal states and behaviors. Understanding how neural dynamics explain specific measured behaviors requires dissociating behaviorally relevant and irrelevant dynamics, which is not achieved with current neural dynamic models as they are learned without considering behavior. We develop preferential subspace identification (PSID), which is an algorithm that models neural activity while dissociating and prioritizing its behaviorally relevant dynamics. Modeling data in two monkeys performing three-dimensional reach and grasp tasks, PSID revealed that the behaviorally relevant dynamics are significantly lower-dimensional than otherwise implied. Moreover, PSID discovered distinct rotational dynamics that were more predictive of behavior. Furthermore, PSID more accurately learned behaviorally relevant dynamics for each joint and recording channel. Finally, modeling data in two monkeys performing saccades demonstrated the generalization of PSID across behaviors, brain regions and neural signal types. PSID provides a general new tool to reveal behaviorally relevant neural dynamics that can otherwise go unnoticed.

A point-process matched filter for event detection and decoding from population spike trains

OBJECTIVE

Information encoding in neurons can be described through their response fields. The spatial response field of a neuron is the region of space in which a sensory stimulus or a behavioral event causes that neuron to fire. Neurons can also exhibit temporal response fields (TRFs), which characterize a transient response to stimulus or behavioral event onsets. These neurons can thus be described by a spatio-temporal response field (STRF). The activity of neurons with STRFs can be well-described with point process models that characterize binary spike trains with an instantaneous firing rate that is a function of both time and space. However, developing decoders for point process models of neurons that exhibit TRFs is challenging because it requires prior knowledge of event onset times, which are unknown. Indeed, point process filters (PPF) to date have largely focused on decoding neuronal activity without considering TRFs. Also, neural classifiers have required data to be behavior- or stimulus-aligned, i.e. event times to be known, which is often not possible in real-world applications. Our objective in this work is to develop a viable decoder for neurons with STRFs when event times are unknown.

APPROACH

To enable decoding of neurons with STRFs, we develop a novel point-process matched filter (PPMF) that can detect events and estimate their onset times from population spike trains. We also devise a PPF for neurons with transient responses as characterized by STRFs. When neurons exhibit STRFs and event times are unknown, the PPMF can be combined with the PPF or with discrete classifiers for continuous and discrete brain state decoding, respectively.

MAIN RESULTS

We validate our algorithm on two datasets: simulated spikes from neurons that encode visual saliency in response to stimuli, and prefrontal spikes recorded in a monkey performing a delayed-saccade task. We show that the PPMF can estimate the stimulus times and saccade times accurately. Further, the PPMF combined with the PPF can decode visual saliency maps without knowing the stimulus times. Similarly, the PPMF combined with a point process classifier can decode the saccade direction without knowing the saccade times.

SIGNIFICANCE

These event detection and decoding algorithms can help develop neurotechnologies to decode cognitive states from neural responses that exhibit STRFs.

Myopic control of neural dynamics

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment “input” to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This “myopic” controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this control’s utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic control’s utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.

Stimulating a neural system and observing its effect through simultaneous observation offers the promise to better understand how neural systems perform computations, as well as for the treatment of neurological disorders. A powerful perspective for understanding a neural system’s behavior undergoing stimulation is to conceptualize them as dynamical systems, which considers the global effect that stimulation has on the brain, rather than only assessing what impact it has on the recorded signal from the brain. With this more comprehensive perspective comes a central challenge of determining what requirements need to be satisfied to harness neural observations and then stimulate to make one dynamical system function as another one entirely. This could lead to applications such as neural stimulators that make a diseased brain behave like its healthy counterpart, or to make a neural system previously capable of only hasty decision making to wait and accumulate more evidence for a more informed decision. In this work we explore the implications of this new perspective on neural stimulation and derive a simple prescription for using neural observations to inform stimulation protocol that makes one neural system behave like another one.

Developing a personalized closed-loop controller of medically-induced coma in a rodent model

OBJECTIVE

Personalized automatic control of medically-induced coma, a critical multi-day therapy in the intensive care unit, could greatly benefit clinical care and further provide a novel scientific tool for investigating how the brain response to anesthetic infusion rate changes during therapy. Personalized control would require real-time tracking of inter- and intra-subject variabilities in the brain response to anesthetic infusion rate while simultaneously delivering the therapy, which has not been achieved. Current control systems for medically-induced coma require a separate offline model fitting experiment to deal with inter-subject variabilities, which would lead to therapy interruption. Removing the need for these offline interruptions could help facilitate clinical feasbility. In addition, current systems do not track intra-subject variabilities. Tracking intra-subject variabilities is essential for studying whether or how the brain response to anesthetic infusion rate changes during therapy. Further, such tracking could enhance control precison and thus help facilitate clinical feasibility.

APPROACH

Here we develop a personalized closed-loop anesthetic delivery (CLAD) system in a rodent model that tracks both inter- and intra-subject variabilities in real time while simultaneously controlling the anesthetic in closed loop. We tested the CLAD in rats by administrating propofol to control the electroencephalogram (EEG) burst suppression. We first examined whether the CLAD can remove the need for offline model fitting interruption. We then used the CLAD as a tool to study whether and how the brain response to anesthetic infusion rate changes as a function of changes in the depth of medically-induced coma. Finally, we studied whether the CLAD can enhance control compared with prior systems by tracking intra-subject variabilities.

MAIN RESULTS

The CLAD precisely controlled the EEG burst suppression in each rat without performing offline model fitting experiments. Further, using the CLAD, we discovered that the brain response to anesthetic infusion rate varied during control, and that these variations correlated with the depth of medically-induced coma in a consistent manner across individual rats. Finally, tracking these variations reduced control bias and error by more than 70% compared with prior systems.

SIGNIFICANCE

This personalized CLAD provides a new tool to study the dynamics of brain response to anesthetic infusion rate and has significant implications for enabling clinically-feasible automatic control of medically-induced coma.

Estimating the Memory Order of Electrocorticography Recordings

OBJECTIVE

This paper presents a data-driven method for estimating the memory order (the average length of the statistical dependence of a given sample on previous samples) of a recorded electrocorticography (ECoG) sequence.

METHODS

The proposed inference method is based on the relationship between the loss in predicting the next sample in a time-series and the dependence of this sample on the previous samples. Specifically, the memory order is estimated to be the number of past samples that minimize the least squares error (LSE) in predicting the next sample. To deal with the lack of an analytical model for ECoG recordings, the proposed method combines a collection of different predictors, thereby achieving LSE at least as low as the LSE achieved by each of the different predictors.

RESULTS

ECoG recordings from six patients with epilepsy were analyzed, and the empirical cumulative density functions (ECDFs) of the memory orders estimated from these recordings were generated, for rest as well as pre-ictal time intervals. For pre-ictal time intervals, the electrodes corresponding to the seizure-onset-zone were separately analyzed. The estimated ECDFs were different between patients and between different types of blocks. For all the analyzed patients, the estimated memory orders were on the order of tens of milliseconds (up to 100 ms).

SIGNIFICANCE

The proposed method facilitates the estimation of the causal associations between ECoG recordings, as these associations strongly depend on the recordings' memory. An improved estimation of causal associations can improve the performance of algorithms that use ECoG recordings to localize the epileptogenic zone. Such algorithms can aid doctors in their pre-surgical planning for the surgery of patients with epilepsy.

Optimizing the learning rate for adaptive estimation of neural encoding models

Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error and the convergence time of the estimated model parameters. We derive explicit functions that predict the effect of learning rate on error and convergence time. Using these functions, our calibration algorithm can keep the steady-state parameter error covariance smaller than a desired upper-bound while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. We derive the algorithm both for discrete-valued spikes modeled as point processes nonlinearly dependent on the brain state, and for continuous-valued neural recordings modeled as Gaussian processes linearly dependent on the brain state. Using extensive closed-loop simulations, we show that the analytical solution of the calibration algorithm accurately predicts the effect of learning rate on parameter error and convergence time. Moreover, the calibration algorithm allows for fast and accurate learning of the encoding model and for fast convergence of decoding to accurate performance. Finally, larger learning rates result in inaccurate encoding models and decoders, and smaller learning rates delay their convergence. The calibration algorithm provides a novel analytical approach to predictably achieve a desired level of error and convergence time in adaptive learning, with application to closed-loop neurotechnologies and other signal processing domains.

Dynamic tracking of non-stationarity in human ECoG activity

Modeling brain network dynamics is essential in understanding neural mechanisms and developing neurotechnologies such as closed-loop stimulation therapies for a wide range of neurological disorders. Brain network activity could have non-stationary and time-variant dynamics, especially when the subject's brain is monitored for a long period, e.g., using the electrocorticogram (ECoG). This non-stationarity makes the modeling of dynamics challenging. In our prior work, we developed a framework to identify time-invariant linear state-space models (SSMs) to describe both stationary spontaneous neural population dynamics and input-output (IO) neural dynamics in response to electrical stimulation. Here, we develop an adaptive identification algorithm that estimates time-variant SSMs to track possible non-stationarity in brain network dynamics. We apply the adaptive algorithm to track high-density human ECoG dynamics in three subjects over a long time-period. We find that the adaptive identification algorithm can estimate time-variant SSMs that significantly outperform time-invariant SSMs in all subjects. Our results demonstrate that non-stationary dynamics exist in high-dimensional human ECoG signals over long time-periods, and that the proposed adaptive SSM identification algorithm can successfully track these non-stationarities. These results have important implications for more accurate estimation of neural biomarkers for different brain states and for adaptive closed-loop stimulation therapy across a wide range of neurological disorders.