SAFE-OPT: A Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints
To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed...
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Cold Spring Harbor
Cold Spring Harbor Laboratory Press
16.02.2024
Cold Spring Harbor Laboratory |
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| Abstract | To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. We then deploy both SAFE-OPT and conventional Bayesian optimization in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject-specific safety threshold. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of deep brain stimulation.Competing Interest StatementThe authors have declared no competing interest. |
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| AbstractList | To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. We then deploy both SAFE-OPT and conventional Bayesian optimization in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject-specific safety threshold. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of deep brain stimulation.Competing Interest StatementThe authors have declared no competing interest. To treat neurological and psychiatric diseases with deep brain stimulation, a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects. In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searching in silico. We then deploy both SAFE-OPT and conventional Bayesian optimization in new subjects in vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject’s safety threshold. The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of deep brain stimulation. |
| Author | Sendi, Mohammad Robert Elkan Gross Cole, Eric R Ghetiya, Mihir Kashlan, Adam Eggers, Thomas Connolly, Mark |
| Author_xml | – sequence: 1 givenname: Eric surname: Cole middlename: R fullname: Cole, Eric R – sequence: 2 givenname: Mark surname: Connolly fullname: Connolly, Mark – sequence: 3 givenname: Mihir surname: Ghetiya fullname: Ghetiya, Mihir – sequence: 4 givenname: Mohammad surname: Sendi fullname: Sendi, Mohammad – sequence: 5 givenname: Adam surname: Kashlan fullname: Kashlan, Adam – sequence: 6 givenname: Thomas surname: Eggers fullname: Eggers, Thomas – sequence: 7 fullname: Robert Elkan Gross |
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| DOI | 10.1101/2024.02.13.580142 |
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| Keywords | hippocampus real-time optimization data-driven Neuromodulation |
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| References | Salanova, Sperling, Gross, Irwin, Vollhaber, Giftakis (2024.02.13.580142v1.3) 2021; 62 Cole, Connolly, Park, Grogan, Buxton, Eggers (2024.02.13.580142v1.13) 2021 Eric, Cole, Stento, Funk, Blanpain, Dabiri, Laxpati, Kahana, Gross (2024.02.13.580142v1.32) 2023 Lorenz, Simmons, Monti, Arthur, Limal, Laakso (2024.02.13.580142v1.16) 2019; 12 Cole, Grogan, Eggers, Connolly, Laxpati, Gross (2024.02.13.580142v1.17) 2021 Ashmaig, Connolly, Gross, Mahmoudi (2024.02.13.580142v1.26) 2018; 2018 Antunes, Biala (2024.02.13.580142v1.27) 2012; 13 Connolly, Cole, Isbaine, de Hemptinne, Starr, Willie (2024.02.13.580142v1.30) 2021; 18 Turchetta (2024.02.13.580142v1.24); 32 Maslen, Cheeran, Pugh, Pycroft, Boccard, Prangnell (2024.02.13.580142v1.23) 2018; 21 Zarzycki, Domitrz (2024.02.13.580142v1.22) 2020; 32 Fisher, Salanova, Witt, Worth, Henry, Gross (2024.02.13.580142v1.8) 2010; 51 Reuter, Deuschl, Berg, Helmers, Falk, Witt (2024.02.13.580142v1.20) 2018; 56 Sarikhani, Ferleger, Mitchell, Ostrem, Herron, Mahmoudi (2024.02.13.580142v1.35) 2022 Nair, Laxer, Weber, Murro, Park, Barkley (2024.02.13.580142v1.2) 2020; 95 Mayberg, Lozano, Voon, McNeely, Seminowicz, Hamani (2024.02.13.580142v1.5) 2005; 45 Cole, Grogan, Laxpati, Fernandez, Skelton, Isbaine (2024.02.13.580142v1.9) 2022 Shen, Campbell, Cote, Paquet (2024.02.13.580142v1.39) 2020; 14 Desai, Rolston, McCracken, Potter, Gross (2024.02.13.580142v1.25) 2016; 9 Rasmussen, Nickisch (2024.02.13.580142v1.28) 2010; 11 Acerbo, Botzanowski, Dellavale, Stern, Cole, Gutekunst (2024.02.13.580142v1.40) 2024 Stern, Cole, Gross, Berglund (2024.02.13.580142v1.41) 2024; 11 Accolla, Pollo (2024.02.13.580142v1.21) 2019; 10 Picillo, Lozano, Kou, Puppi Munhoz, Fasano (2024.02.13.580142v1.7) 2016; 9 Eric Brochu, Nando (2024.02.13.580142v1.29) 2010 Schrum, Connolly, Cole, Ghetiya, Gross, Gombolay (2024.02.13.580142v1.37) 2022 Yanan Sui, Burdick, Yue (2024.02.13.580142v1.38) 2018 Geller, Skarpaas, Gross, Goodman, Barkley, Bazil (2024.02.13.580142v1.1) 2017; 58 van Dijk, Verhagen, Bour, Heida, Veltink (2024.02.13.580142v1.18) 2018; 21 Romann, Beber, Cielo, Rieder (2024.02.13.580142v1.19) 2019; 23 Louie, Petrucci, Grado, Lu, Tuite, Lamperski (2024.02.13.580142v1.12) 2021; 18 Connolly, Park, Laxpati, Zaidi, Ghetiya, Fernandez (2024.02.13.580142v1.11) 2020 Grado, Johnson, Netoff (2024.02.13.580142v1.15) 2018; 14 Losanno, Badi, Wurth, Borgognon, Courtine, Capogrosso (2024.02.13.580142v1.31) 2021; 29 Cooper, Netoff (2024.02.13.580142v1.36) 2022 Volkmann, Herzog, Kopper, Deuschl (2024.02.13.580142v1.6) 2002; 17 Benabid (2024.02.13.580142v1.4) 2003; 13 Connolly, Park, Gross (2024.02.13.580142v1.33) 2019; 2019 Nagrale, Yousefi, Netoff, Widge (2024.02.13.580142v1.14) 2023; 20 Cole, Eggers, Weiss, Connolly, Gombolay, Laxpati (2024.02.13.580142v1.34) 2022 Park, Connolly, Exarchos, Fernandez, Ghetiya, Gutekunst (2024.02.13.580142v1.10) 2020; 17 |
| References_xml | – volume: 13 start-page: 93 year: 2012 end-page: 110 ident: 2024.02.13.580142v1.27 article-title: The novel object recognition memory: neurobiology, test procedure, and its modifications publication-title: Cogn Process – volume: 14 start-page: e1006606 year: 2018 ident: 2024.02.13.580142v1.15 article-title: Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson’s disease publication-title: PLoS Comput Biol – volume: 11 start-page: 024202 year: 2024 ident: 2024.02.13.580142v1.41 article-title: Seizure event detection using intravital two-photon calcium imaging data publication-title: Neurophotonics – year: 2010 ident: 2024.02.13.580142v1.29 article-title: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning publication-title: Arxiv – volume: 51 start-page: 899 year: 2010 end-page: 908 ident: 2024.02.13.580142v1.8 article-title: Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy publication-title: Epilepsia – volume: 10 start-page: 617 year: 2019 ident: 2024.02.13.580142v1.21 article-title: Mood Effects After Deep Brain Stimulation for Parkinson’s Disease: An Update publication-title: Front Neurol – start-page: 1 year: 2022 end-page: 8 ident: 2024.02.13.580142v1.37 article-title: Meta-Active Learning in Probabilistically Safe Optimization – volume: 95 start-page: e1244 year: 2020 end-page: e1256 ident: 2024.02.13.580142v1.2 article-title: Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy publication-title: Neurology – volume: 17 start-page: 046009 year: 2020 ident: 2024.02.13.580142v1.10 article-title: Optimizing neuromodulation based on surrogate neural states for seizure suppression in a rat temporal lobe epilepsy model publication-title: J Neural Eng – year: 2022 ident: 2024.02.13.580142v1.9 article-title: Evidence Supporting Deep Brain Stimulation of the Medial Septum in the Treatment of Temporal Lobe Epilepsy publication-title: Epilepsia – volume: 11 start-page: 3011 year: 2010 end-page: 3015 ident: 2024.02.13.580142v1.28 article-title: Gaussian processes for machine learning (GPML) toolbox publication-title: The Journal of Machine Learning Research – volume: 32 start-page: 57 year: 2020 end-page: 64 ident: 2024.02.13.580142v1.22 article-title: Stimulation-induced side effects after deep brain stimulation - a systematic review publication-title: Acta Neuropsychiatr – year: 2022 ident: 2024.02.13.580142v1.36 article-title: Multidimensional Bayesian Estimation for Deep Brain Stimulation Using the SafeOpt Algorithm publication-title: medRxiv – volume: 21 start-page: 553 year: 2018 end-page: 561 ident: 2024.02.13.580142v1.18 article-title: Avoiding Internal Capsule Stimulation With a New Eight-Channel Steering Deep Brain Stimulation Lead publication-title: Neuromodulation – volume: 17 start-page: S181 issue: Suppl 3 year: 2002 end-page: 187 ident: 2024.02.13.580142v1.6 article-title: Introduction to the programming of deep brain stimulators publication-title: Mov Disord – volume: 21 start-page: 135 year: 2018 end-page: 143 ident: 2024.02.13.580142v1.23 article-title: Unexpected Complications of Novel Deep Brain Stimulation Treatments: Ethical Issues and Clinical Recommendations publication-title: Neuromodulation – year: 2024 ident: 2024.02.13.580142v1.40 article-title: Improved Temporal and Spatial Focality of Non-invasive Deep-brain Stimulation using Multipolar Single-pulse Temporal Interference with Applications in Epilepsy publication-title: bioRxiv – year: 2023 ident: 2024.02.13.580142v1.32 article-title: Automated Detection of Evoked Potentials Produced by Intracranial Electrical Stimulation – start-page: 950 year: 2021 end-page: 953 ident: 2024.02.13.580142v1.13 article-title: Autonomous State Inference for Data-Driven Optimization of Neural Modulation – volume: 9 start-page: 425 year: 2016 end-page: 437 ident: 2024.02.13.580142v1.7 article-title: Programming Deep Brain Stimulation for Parkinson’s Disease: The Toronto Western Hospital Algorithms publication-title: Brain Stimul – year: 2022 ident: 2024.02.13.580142v1.34 article-title: Irregular optogenetic stimulation waveforms can induce naturalistic patterns of hippocampal spectral activity publication-title: bioRxiv – volume: 62 start-page: 1306 year: 2021 end-page: 1317 ident: 2024.02.13.580142v1.3 article-title: The SANTE study at 10 years of follow-up: Effectiveness, safety, and sudden unexpected death in epilepsy publication-title: Epilepsia – volume: 9 start-page: 86 year: 2016 end-page: 100 ident: 2024.02.13.580142v1.25 article-title: Asynchronous Distributed Multielectrode Microstimulation Reduces Seizures in the Dorsal Tetanus Toxin Model of Temporal Lobe Epilepsy publication-title: Brain Stimul – volume: 13 start-page: 696 year: 2003 end-page: 706 ident: 2024.02.13.580142v1.4 article-title: Deep brain stimulation for Parkinson’s disease publication-title: Curr Opin Neurobiol – volume: 58 start-page: 994 year: 2017 end-page: 1004 ident: 2024.02.13.580142v1.1 article-title: Brain-responsive neurostimulation in patients with medically intractable mesial temporal lobe epilepsy publication-title: Epilepsia – volume: 45 start-page: 651 year: 2005 end-page: 660 ident: 2024.02.13.580142v1.5 article-title: Deep brain stimulation for treatment-resistant depression publication-title: Neuron – year: 2018 ident: 2024.02.13.580142v1.38 article-title: Stagewise Safe Bayesian Optimization with Gaussian Processes – volume: 14 start-page: 41 year: 2020 ident: 2024.02.13.580142v1.39 article-title: Challenges for Therapeutic Applications of Opsin-Based Optogenetic Tools in Humans publication-title: Front Neural Circuits – volume: 23 start-page: 203 year: 2019 end-page: 208 ident: 2024.02.13.580142v1.19 article-title: Acoustic Voice Modifications in Individuals with Parkinson Disease Submitted to Deep Brain Stimulation publication-title: Int Arch Otorhinolaryngol – volume: 18 year: 2021 ident: 2024.02.13.580142v1.30 article-title: Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson’s disease publication-title: J Neural Eng – volume: 32 ident: 2024.02.13.580142v1.24 article-title: Felix Berkenkamp, and Andreas Krause publication-title: Advances in Neural Information Processing Systems – volume: 2018 start-page: 2683 year: 2018 end-page: 2686 ident: 2024.02.13.580142v1.26 article-title: Bayesian Optimization of Asynchronous Distributed Microelectrode Theta Stimulation and Spatial Memory – year: 2020 ident: 2024.02.13.580142v1.11 article-title: A framework for designing data-driven optimization systems for neural modulation publication-title: J Neural Eng – start-page: 19 year: 2022 ident: 2024.02.13.580142v1.35 article-title: Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson’s disease and essential tremor publication-title: J Neural Eng – volume: 2019 start-page: 6454 year: 2019 end-page: 6457 ident: 2024.02.13.580142v1.33 article-title: Learning State-Dependent Neural Modulation Policies with Bayesian Optimization – start-page: 281 year: 2021 end-page: 284 ident: 2024.02.13.580142v1.17 article-title: Model-Driven Collection of Neural Modulation Data – volume: 18 start-page: 83 year: 2021 ident: 2024.02.13.580142v1.12 article-title: Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson’s disease publication-title: J Neuroeng Rehabil – volume: 20 year: 2023 ident: 2024.02.13.580142v1.14 article-title: . In silicodevelopment and validation of Bayesian methods for optimizing deep brain stimulation to enhance cognitive control publication-title: J Neural Eng – volume: 29 start-page: 18 year: 2021 ident: 2024.02.13.580142v1.31 article-title: Bayesian optimization of peripheral intraneural stimulation protocols to evoke distal limb movements publication-title: J Neural Eng – volume: 12 start-page: 1484 year: 2019 end-page: 1489 ident: 2024.02.13.580142v1.16 article-title: Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization publication-title: Brain Stimul – volume: 56 start-page: 88 year: 2018 end-page: 92 ident: 2024.02.13.580142v1.20 article-title: Life-threatening DBS withdrawal syndrome in Parkinson’s disease can be treated with early reimplantation publication-title: Parkinsonism Relat Disord |
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| SubjectTerms | Algorithms Bayesian analysis Bioengineering Deep brain stimulation Learning Mental disorders Mental task performance Optimization algorithms Patient safety Safety Side effects Spatial memory |
| Title | SAFE-OPT: A Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints |
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