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We present a neural network approach for closed-loop deep brain stimulation (DBS). We cast the problem of finding an optimal neurostimulation strategy as a control problem. In this setting, control policies aim to optimize therapeutic outcomes by tailoring the parameters of a DBS system, typically via electrical stimulation, in real time based on the patient’s ongoing neuronal activity. We approximate the value function offline using a neural network to enable generating controls (stimuli) in real time via the feedback form. The neuronal activity is characterized by a nonlinear, stiff system of differential equations as dictated by the Hodgkin-Huxley model. Our training process leverages the relationship between Pontryagin’s maximum principle and Hamilton-Jacobi-Bellman equations to update the value function estimates simultaneously. Our numerical experiments illustrate the accuracy of our approach for out-of-distribution samples and the robustness to moderate shocks and disturbances in the system.
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Responsive manipulation of neural circuit pathology by fully implantable, front-end multiplexed embedded neuroelectronics
Responsive neurostimulation is increasingly required to probe neural circuit function and treat neuropsychiatric disorders. We introduce a multiplex-then-amplify (MTA) scheme that, in contrast to current approaches (which necessitate an equal number of amplifiers as number of channels), only requires one amplifier per multiplexer, significantly reducing the number of components and the size of electronics in multichannel acquisition systems. It also enables simultaneous stimulation of arbitrary waveforms on multiple independent channels. We validated the function of MTA by developing a fully implantable, responsive embedded system that merges the ability to acquire individual neural action potentials using conformable conducting polymer-based electrodes with real-time onboard processing, low-latency arbitrary waveform stimulation, and local data storage within a miniaturized physical footprint. We verified established responsive neurostimulation protocols and developed a network intervention to suppress pathological coupling between the hippocampus and cortex during interictal epileptiform discharges. The MTA design enables effective, self-contained, chronic neural network manipulation with translational relevance to the treatment of neuropsychiatric disease.
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- PAR ID:
- 10300880
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 20
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- e2022659118
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Neurostimulation - the practice of applying exogenous excitation, e.g., via electrical current, to the brain - has been used for decades in clinical applications such as the treatment of motor disorders and neuropsychiatric illnesses. Over the past several years, more emphasis has been placed on understanding and designing neurostimulation from a systems-theoretic perspective, so as to better optimize its use. Particular questions of interest have included designing stimulation waveforms that best induce certain patterns of brain activity while minimizing expenditure of stimulus power. The pursuit of these designs faces a fundamental conundrum, insofar as they presume that the desired pattern (e.g., desyn-chronization of a neural population) is known a priori. In this paper, we present an alternative paradigm wherein the goal of the stimulation is not to induce a prescribed pattern, but rather to simply improve the functionality of the stimulated circuit/system. Here, the notion of functionality is defined in terms of an information-theoretic objective. Specifically, we seek closed loop control designs that maximize the ability of a controlled circuit to encode an afferent `hidden input,' without prescription of dynamics or output. In this way, the control attempts only to make the system `effective' without knowing beforehand the dynamics that are needed to be induced. We devote most of our effort to defining this framework mathematically, providing algorithmic procedures that demonstrate its solution and interpreting the results of this procedure for simple, prototypical dynamical systems. Simulation results are provided for more complex models, including an example involving control of a canonical neural mass model.more » « less
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Abstract Objective.A major challenge in designing closed-loop brain-computer interfaces is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and different objectives. Traditional approaches, such as those currently used for deep brain stimulation, have largely followed a manual trial-and-error strategy to search for effective open-loop stimulation parameters, a strategy that is inefficient and does not generalize to closed-loop activity-dependent stimulation.Approach.To achieve goal-directed closed-loop neurostimulation, we propose the use of brain co-processors, devices which exploit artificial intelligence to shape neural activity and bridge injured neural circuits for targeted repair and restoration of function. Here we investigate a specific type of co-processor called a ‘neural co-processor’ which uses artificial neural networks and deep learning to learn optimal closed-loop stimulation policies. The co-processor adapts the stimulation policy as the biological circuit itself adapts to the stimulation, achieving a form of brain-device co-adaptation. Here we use simulations to lay the groundwork for futurein vivotests of neural co-processors. We leverage a previously published cortical model of grasping, to which we applied various forms of simulated lesions. We used our simulations to develop the critical learning algorithms and study adaptations to non-stationarity in preparation for futurein vivotests.Main results.Our simulations show the ability of a neural co-processor to learn a stimulation policy using a supervised learning approach, and to adapt that policy as the underlying brain and sensors change. Our co-processor successfully co-adapted with the simulated brain to accomplish the reach-and-grasp task after a variety of lesions were applied, achieving recovery towards healthy function in the range 75%–90%.Significance.Our results provide the first proof-of-concept demonstration, using computer simulations, of a neural co-processor for adaptive activity-dependent closed-loop neurostimulation for optimizing a rehabilitation goal after injury. While a significant gap remains between simulations andin vivoapplications, our results provide insights on how such co-processors may eventually be developed for learning complex adaptive stimulation policies for a variety of neural rehabilitation and neuroprosthetic applications.more » « less
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Abstract Despite promising advancements, closed-loop neurostimulation for drug-resistant epilepsy (DRE) still relies on manual tuning and produces variable outcomes, while automated predictable algorithms remain an aspiration. As a fundamental step towards addressing this gap, here we study predictive dynamical models of human intracranial EEG (iEEG) response under parametrically rich neurostimulation. Using data fromn= 13 DRE patients, we find that stimulation-triggered switched-linear models with ~300 ms of causal historical dependence best explain evoked iEEG dynamics. These models are highly consistent across different stimulation amplitudes and frequencies, allowing for learning a generalizable model from abundant STIM OFF and limited STIM ON data. Further, evoked iEEG in nearly all subjects exhibited a distance-dependent pattern, whereby stimulationdirectlyimpacts the actuation site and nearby regions (≲ 20 mm), affects medium-distance regions (20 ~ 100 mm) through network interactions, and hardly reaches more distal areas (≳ 100 mm). Peak network interaction occurs at 60 ~ 80 mm from the stimulation site. Due to their predictive accuracy and mechanistic interpretability, these models hold significant potential for model-based seizure forecasting and closed-loop neurostimulation design.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2022659118
