Optimization of Spinal Cord Stimulation Using Bayesian Preference Learning and Its Validation

Zhao, Zixi; Ahmadi, Aliya; Hoover, Caleb; Grado, Logan L.; Peterson, Nicholas; Wang, Xinran; Freeman, David J.; Murray, Thomas A.; Lamperski, Andrew; Darrow, David; Netoff, Theoden I.

doi:10.1109/tnsre.2021.3113636

Cited by 21 publications

(25 citation statements)

References 18 publications

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“…Every month, a preference model was created based on daily preference evaluations with preference predictions also for untested parameters and new settings, with promising results. 46 These results on pilot data do not apply to selecting patients suitable for SCS, but on selecting patients for specific stimulation paradigms. As widely acknowledged in this field and despite the inclusion of a temporary SCS-screening trial before IPG implantation, the initial effectiveness of SCS generally declines over time, due to growing tolerance of the central nervous system, 47 which eventually causes loss of efficacy.…”

Section: Outcome-based Patient Selectionmentioning

confidence: 99%

“…Instead of focusing only on whether SCS will be effective in the long term, a recent study explored the value of a Bayesian preference-optimization algorithm to assist clinicians in the systematic programming of individualized therapeutic stimulation, based on expressed preferences for stimulation settings. 46 The main focus was on optimization of temporal stimulation parameters, namely pulse frequency and pulse width, in combination with optimal spatial configuration, which was determined by clinician tuning and electromyograph measurements. Every month, a preference model was created based on daily preference evaluations with preference predictions also for untested parameters and new settings, with promising results.…”

Section: Outcome-based Patient Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Patient Selection for Spinal Cord Stimulation in Treatment of Pain: Sequential Decision-Making Model — A Narrative Review

Goudman¹,

Rigoard²,

Billot³

et al. 2022

JPR

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Despite the well-known efficacy of spinal cord stimulation (SCS) in chronic pain management, patient selection in clinical practice remains challenging. The aim of this review is to provide an overview of the factors that can influence the process of patient selection for SCS treatment. A sequential decision-making model is presented within a tier system that operates in clinical practice. The first level incorporates the underlying disease as a primary indication for SCS, country-related reimbursement rules, and SCS screening–trial criteria in combination with underlying psychological factors as initial selection criteria in evaluating patient eligibility for SCS. The second tier is aligned with the individualized approach within precision pain medicine, whereby individual goals and expectations and the potential need for preoperative optimizations are emphasized. Additionally, this tier relies on results from prediction models to provide an estimate of the efficacy of SCS in the long term. In the third tier, selection bias, MRI compatibility, and ethical beliefs are included, together with recent technological innovations, superiority of specific stimulation paradigms, and new feedback systems that could indirectly influence the decision-making of the physician. Both patients and physicians should be aware of the different aspects that influence patient selection in relation to SCS for pain management to make an independent decision on whether or not to initiate a treatment trajectory with SCS.

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Section: Outcome-based Patient Selectionmentioning

confidence: 99%

Section: Outcome-based Patient Selectionmentioning

confidence: 99%

Patient Selection for Spinal Cord Stimulation in Treatment of Pain: Sequential Decision-Making Model — A Narrative Review

Goudman¹,

Rigoard²,

Billot³

et al. 2022

JPR

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show abstract

“…On the third and fourth days, optimal single-site stimulation parameters (frequency, pulse width, and amplitude) were identified separately for the prior contact choices based on the result of a Bayesian parameter space optimization utilizing a probit Gaussian process to assess her preferences, similar to what we have previously described in Zhao et al [ 7 ]. Four initial settings were chosen, from the combination of two frequencies (50 and 20 Hz for M1 and 26 and 50 Hz for dlPFC) and two pulse widths (150 and 60 μs for M1 and 150 and 300 μs for dlPFC).…”

Section: Case Presentationmentioning

confidence: 99%

“…Since many of these reports were published, advancements in neuromodulation and its underlying technology have fueled innovation in novel targets, personalization, and closing the loop between electrophysiology and neurostimulation [ 4 , 5 , 6 ]. Recently, we have developed and tested a novel Bayesian optimization platform for objectively determining neurostimulation parameters based on patient feedback with forced binary choice [ 7 ]. This approach facilitates a methodology for continuous optimization that we hypothesized could aid with finding optimal parameters during a trial externalization for CPSP.…”

Section: Introduction/backgroundmentioning

confidence: 99%

Personalizing Dual-Target Cortical Stimulation with Bayesian Parameter Optimization Successfully Treats Central Post-Stroke Pain: A Case Report

et al. 2021

Self Cite

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Central pain disorders, such as central post-stroke pain, remain clinically challenging to treat, despite many decades of pharmacological advances and the evolution of neuromodulation. For treatment refractory cases, previous studies have highlighted some benefits of cortical stimulation. Recent advances in new targets for pain and the optimization of neuromodulation encouraged our group to develop a dual cortical target approach paired with Bayesian optimization to provide a personalized treatment. Here, we present a case report of a woman who developed left-sided facial pain after multiple thalamic strokes. All previous pharmacologic and interventional treatments failed to mitigate the pain, leaving her incapacitated due to pain and medication side effects. She subsequently underwent a single burr hole for placement of motor cortex (M1) and dorsolateral prefrontal cortex (dlPFC) paddles for stimulation with externalization. By using Bayesian optimization to find optimal stimulation parameters and stimulation sites, we were able to reduce pain from an 8.5/10 to a 0/10 during a 5-day inpatient stay, with pain staying at or below a 2/10 one-month post-procedure. We found optimal treatment to be simultaneous stimulation of M1 and dlPFC without any evidence of seizure induction. In addition, we found no worsening in cognitive performance during a working memory task with dlPFC stimulation. This personalized approach using Bayesian optimization may provide a new foundation for treating central pain and other functional disorders through systematic evaluation of stimulation parameters.

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“…Despite ongoing optimization, we previously reported that the total sEMG activity of the legs seemed to plateau and even decrease after the first six months despite a subjective improvement in motor control (Pino et al, 2020), (Zhao et al, 2021). As a result, we endeavored to objectively characterize changes in neuromuscular control over time with SCS therapy associated with spinal cord plasticity to better elucidate this discrepancy.…”

Section: Introductionmentioning

confidence: 99%

Epidural stimulation restores muscle synergies by modulating neural drives in participants with motor/sensory complete spinal cord injuries

Singh

Ahmadi

Parr

et al. 2022

Preprint

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Multiple studies have corroborated restored volitional motor control after motor-complete spinal cord injury (SCI) through the use of spinal cord stimulation (SCS/eSCS) but rigorous quantitative descriptions have been lacking. Using a structured surface electromyogram based (sEMG) task with and without SCS during the Epidural Stimulation After Neurological Damage (ESTAND) study in participants with chronic, motor and sensory complete SCI, we investigated muscle activity complexity and muscle synergies to better characterize neuro-muscular control.In addition, competition exists between the task and neural origin hypotheses underlying muscle synergies, and this analysis in humans with motor and sensory complete chronic injury provided an opportunity to test these hypotheses. Muscle activity complexity was computed with Higuchi Fractal Dimensional analysis (HFD), and muscle synergies were estimated using non-negative matrix factorization (NNMF) in six participants with AIS A chronic SCI. We found that the complexity of muscle activity is immediately reduced with SCS in the SCI participants. We also found that over the follow-up sessions, the muscle synergy structure of the SCI participants became more defined, and the number of synergies decreased over time, indicating improved coordination between the muscle groups. Lastly, we found that the muscle synergies were restored with SCS, supporting the neural hypothesis of muscle synergies. We conclude that SCS restores muscle movements and muscle synergies that are distinct from healthy, able-bodied controls.

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Optimization of Spinal Cord Stimulation Using Bayesian Preference Learning and Its Validation

Cited by 21 publications

References 18 publications

Patient Selection for Spinal Cord Stimulation in Treatment of Pain: Sequential Decision-Making Model — A Narrative Review

Patient Selection for Spinal Cord Stimulation in Treatment of Pain: Sequential Decision-Making Model — A Narrative Review

Personalizing Dual-Target Cortical Stimulation with Bayesian Parameter Optimization Successfully Treats Central Post-Stroke Pain: A Case Report

Epidural stimulation restores muscle synergies by modulating neural drives in participants with motor/sensory complete spinal cord injuries

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