Model predictive control of deep brain stimulation for Parkinsonian tremor

Haddock, Andrew; Velisar, Anca; Herron, Jeffrey A.; Brontë‐Stewart, Helen; Chizeck, H.J.

doi:10.1109/ner.2017.8008364

Cited by 15 publications

(14 citation statements)

References 14 publications

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“…In contrast, Su et al (2019) optimized the parameters of a discrete PI controller to track a dynamic target of betaband power which may be associated with fluctuations of the oscillatory activity during voluntary movement. Haddock et al (2017) illustrated the potential of the approach by deriving an autoregressive model of the relationship between DBS amplitude and parkinsonian tremor from patient data, using the identified model as part of a model predictive controller for parkinsonian tremor (Haddock et al, 2017). The benefit of the autoregressive model approach is that derived models can be simulated in real-time and thus facilitate the use of advanced control techniques which require use of an internal model (Francis and Wonham, 1976).…”

Section: Pi Controller Parametersmentioning

confidence: 99%

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

2020

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Section: Pi Controller Parametersmentioning

confidence: 99%

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

2020

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“…Note that, as described, these works learn separate models for stimulation-on and stimulation-off durations and do not consider stimulation as a driving input to network nodes. A similar split-modeling approach has also been employed in the context of autoregressive models in (Haddock et al, 2017), where hybrid ARX modeling was used to model tremor power obtained from LFP recorded during the stimulation of human brain. The authors here trained an AR and ARX model by separating the data into DBS-off and DBS-on durations, respectively.…”

Section: Dynamical System Modelsmentioning

confidence: 99%

Brain modeling for control: A review

Acharya

Ruf

Nozari

2022

Front. Control Eng.

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Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as epilepsy, Parkinson’s Disease, and depression. The provided stimulation can be of different types, such as electric, magnetic, and optogenetic, and is generally applied to a specific region of the brain in order to drive the local and/or global neural dynamics to a desired state of (in)activity. For most neurostimulation techniques, however, an underlying theoretical understanding of their efficacy is still lacking. From a control-theoretic perspective, it is important to understand how each stimulus modality interacts with the inherent complex network dynamics of the brain in order to assess the controllability of the system and develop neurophysiologically relevant computational models that can be used to design the stimulation profile systematically and in closed loop. In this paper, we review the computational modeling studies of 1) deep brain stimulation, 2) transcranial magnetic stimulation, 3) direct current stimulation, 4) transcranial electrical stimulation, and 5) optogenetics as five of the most popular and commonly used neurostimulation technologies in research and clinical settings. For each technology, we split the reviewed studies into 1) theory-driven biophysical models capturing the low-level physics of the interactions between the stimulation source and neuronal tissue, 2) data-driven stimulus-response models which capture the end-to-end effects of stimulation on various biomarkers of interest, and 3) data-driven dynamical system models that extract the precise dynamics of the brain’s response to neurostimulation from neural data. While our focus is particularly on the latter category due to their greater utility in control design, we review key works in the former two categories as the basis and context in which dynamical system models have been and will be developed. In all cases, we highlight the strength and weaknesses of the reviewed works and conclude the review with discussions on outstanding challenges and critical avenues for future work.

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“…Likewise, dynamics-aware control strategies have also been explored. For example, model predictive control for ET in an aDBS system based on IMU information (Haddock et al, 2017), coordinated-reset in PD patients and animal models (Adamchic et al, 2014;Wang et al, 2016), phasedependent burst stimulation (Cagnan et al, 2016), or contexttriggered strategies based on event-related desynchronization (Herron et al, 2017). These studies are an important indication for considering patient-specific temporal dynamics for control of aDBS systems.…”

Section: Strategies Of Closed-loop Control For Adbsmentioning

confidence: 99%

“…Those fixed mappings between observed NMs and amplitude, however, presuppose the underlying neural system as a stationary process. Nonetheless, this assumption is problematic in aDBS: The dynamics of band power NMs are context-dependent and change upon, e.g., sitting, walking, or during transitory movement states (Bulea et al, 2014;Haddock et al, 2017). In addition, they are co-modulated by other processes, such as the circadian rhythm or medication intake (Pollok et al, 2012).…”

Section: Control Signal Generation Robust To Non-stationary Dynamicsmentioning

confidence: 99%

A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients

Castaño-Candamil

Ferleger

Haddock

et al. 2020

Front. Hum. Neurosci.

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Deep brain stimulation (DBS) is an established therapy for Parkinson's disease (PD) and essential-tremor (ET). In adaptive DBS (aDBS) systems, online tuning of stimulation parameters as a function of neural signals may improve treatment efficacy and reduce side-effects. State-of-the-art aDBS systems use symptom surrogates derived from neural signals-so-called neural markers (NMs)-defined on the patient-group level, and control strategies assuming stationarity of symptoms and NMs. We aim at improving these aDBS systems with (1) a data-driven approach for identifying patient-and session-specific NMs and (2) a control strategy coping with short-term non-stationary dynamics. The two building blocks are implemented as follows: (1) The data-driven NMs are based on a machine learning model estimating tremor intensity from electrocorticographic signals. (2) The control strategy accounts for local variability of tremor statistics. Our study with three chronically implanted ET patients amounted to five online sessions. Tremor quantified from accelerometer data shows that symptom suppression is at least equivalent to that of a continuous DBS strategy in 3 out-of 4 online tests, while considerably reducing net stimulation (at least 24%). In the remaining online test, symptom suppression was not significantly different from either the continuous strategy or the no treatment condition. We introduce a novel aDBS system for ET. It is the first aDBS system based on (1) a machine learning model to identify session-specific NMs, and (2) a control strategy coping with short-term non-stationary dynamics. We show the suitability of our aDBS approach for ET, which opens the door to its further study in a larger patient population.

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Model predictive control of deep brain stimulation for Parkinsonian tremor

Cited by 15 publications

References 14 publications

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

Brain modeling for control: A review

A Pilot Study on Data-Driven Adaptive Deep Brain Stimulation in Chronically Implanted Essential Tremor Patients

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