An optimization framework for targeted spinal cord stimulation.
An optimization framework for targeted spinal cord stimulation.
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DOI:
10.1088/1741-2552/acf522
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发表时间:
2023-09-28
影响因子:
4
通讯作者:
中科院分区:
文献类型:
--
作者:
Objective. Spinal cord stimulation (SCS) is a common neurostimulation therapy to manage chronic pain. Technological advances have produced new neurostimulation systems with expanded capabilities in an attempt to improve the clinical outcomes associated with SCS. However, these expanded capabilities have dramatically increased the number of possible stimulation parameters and made it intractable to efficiently explore this large parameter space within the context of standard clinical programming procedures. Therefore, in this study, we developed an optimization approach to define the optimal current amplitudes or fractions across individual contacts in an SCS electrode array(s). Approach. We developed an analytic method using the Lagrange multiplier method along with smoothing approximations. To test our optimization framework, we used a hybrid computational modeling approach that consisted of a finite element method model and multi-compartment models of axons and cells within the spinal cord. Moreover, we extended our approach to multi-objective optimization to explore the trade-off between activating regions of interest (ROIs) and regions of avoidance (ROAs). Main results. For simple ROIs, our framework suggested optimized configurations that resembled simple bipolar configurations. However, when we considered multi-objective optimization, our framework suggested nontrivial stimulation configurations that could be selected from Pareto fronts to target multiple ROIs or avoid ROAs. Significance. We developed an optimization framework for targeted SCS. Our method is analytic, which allows for the fast calculation of optimal solutions. For the first time, we provided a multi-objective approach for selective SCS. Through this approach, we were able to show that novel configurations can provide neural recruitment profiles that are not possible with conventional stimulation configurations (e.g. bipolar stimulation). Most importantly, once integrated with computational models that account for sources of interpatient variability (e.g. anatomy, electrode placement), our optimization framework can be utilized to provide stimulation settings tailored to the needs of individual patients.
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DOI:
10.1109/tnsre.2021.3138297
发表时间:
2021
期刊:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子:
--
作者:
Haji Ghaffari D;Akwaboah AD;Mirzakhalili E;Weiland JD
通讯作者:
Weiland JD
影响因子:
4
作者:
Fauvel, Tristan;Chalk, Matthew
通讯作者:
Chalk, Matthew
影响因子:
2.5
作者:
Billot, Maxime;Naiditch, Nicolas;Rigoard, Philippe
通讯作者:
Rigoard, Philippe
影响因子:
5.3
作者:
Capogrosso, Marco;Wenger, Nikolaus;Micera, Silvestro
通讯作者:
Micera, Silvestro
DOI:
10.1002/ejp.1336
发表时间:
2019-04
期刊:
European journal of pain (London, England)
影响因子:
--
作者:
Jensen MP;Brownstone RM
通讯作者:
Brownstone RM