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

Yang, Yuxiao; Shanechi, Maryam M.

doi:10.1088/1741-2560/13/6/066019

Cited by 42 publications

(52 citation statements)

References 94 publications

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“…To formulate the dynamical model for neural activity, we exploit a latent state-space formulation. A state-space model consists of a state equation and an observation equation [17][18][19]21,22,35,42,46,47,49,53,[60][61][62][63]68,70,72 . We build three dynamical models: a single-scale dynamical model for spiking activity, a single-scale dynamical model for LFP activity, and a multiscale dynamical model for combined spike-LFP activity.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…Moreover, the application of a closed-loop system has guided the controlled release of a treatment drug in a medical context. Yang and Shanechi (2016) used a closed-loop system to monitor the brain state via EEG and control the level of burst suppression (i.e., amount of reduction in bursts of increased electrical activity); this feedback approach was successfully used to regulate, in real time, the injection of an anesthetic drug to keep the patient in a constant state.…”

Section: Applications Of Open- and Closed-loop Systemsmentioning

confidence: 99%

Integrating TMS, EEG, and MRI as an Approach for Studying Brain Connectivity

2020

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The human brain is a complex network in which hundreds of brain regions are interconnected via thousands of axonal pathways. The capability of such a complex system emerges from specific interactions among smaller entities, a set of events that can be described by the activation of interconnections between brain areas. Studies that focus on brain connectivity have the aim of understanding and modeling brain function, taking into account the spatiotemporal dynamics of neural communication between brain regions. Much of the current knowledge regarding brain connectivity has been obtained from stand-alone neuroimaging methods. Nevertheless, the use of a multimodal approach seems to be a powerful way to investigate effective brain connectivity, overcoming the limitations of unimodal approaches. In this review, we will present the advantages of an integrative approach in which transcranial magnetic stimulation–electroencephalography coregistration is combined with magnetic resonance imaging methods to explore effective neural interactions. Moreover, we will describe possible implementations of the integrative approach in open- and closed-loop frameworks where real-time brain activity becomes a contributor to the study of cognitive brain networks.

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“…Finally, the calibration algorithm has the potential to be generalized to Bayesian filters beyond the KF and PPF, e.g., the unscented Kalman filter [ 42 ], an adaptive filter with a binomial distribution as the observation model [ 44 ], or hybrid spike-LFP filters [ 91 ]. The derivations of Eqs (7) and (8) in theorems 1 and 3 are based on the recursive equation for estimation error dynamics, which is derived from the desired Bayesian filter.…”

Section: Discussionmentioning

confidence: 99%

“…A calibration algorithm that can write an explicit function for the effect of the learning rate on error and/or convergence time for both linear and nonlinear observation models would also provide a novel approach for learning rate selection in other signal processing domains [ 41 – 47 ]. For example, in image processing [ 43 ], in electrocardiography [ 41 ], in anesthesia control [ 44 ], in automated heart beat detection [ 46 , 47 ], and in unscented Kalman filters [ 42 ], adaptive filters with learning rates are used in decoding system states or in learning system parameters in real time (see Discussions ).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the learning rate for adaptive estimation of neural encoding models

Hsieh

Shanechi

2018

PLoS Comput Biol

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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.

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

Abstract: These results have significant implications for clinically viable CLAD design for a wide range of anesthetic states, with potential cost-saving and therapeutic benefits.

Cited by 42 publications

References 94 publications

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

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

Integrating TMS, EEG, and MRI as an Approach for Studying Brain Connectivity

Optimizing the learning rate for adaptive estimation of neural encoding models

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