Abstract:
OBJECTIVE:Spinal cord stimulation (SCS) is a common neurostimulation therapy to treat chronic pain. Computational models represent a valuable tool to study the potential mechanisms of action of SCS and to optimize the design and implementation of SCS technologies. However, it is imperative that these computational models include the appropriate level of detail to accurately predict the neural response to SCS and to correlate model predictions with clinical outcomes. Therefore, the goal of this study was to investigate several anatomic and technical factors that may affect model-based predictions of neural activation during thoracic SCS. APPROACH:We developed computational models that consisted of detailed finite element models of the lower thoracic spinal cord, surrounding tissues, and implanted SCS electrode arrays. We positioned multicompartment models of sensory axons within the spinal cord to calculate the activation threshold for each sensory axon. We then investigated how activation thresholds changed as a function of several anatomical variables (e.g. spine geometry, dorsal rootlet anatomy), stimulation type (i.e. voltage-controlled vs. current-controlled), electrode impedance, lead position, lead type, and electrical properties of surrounding tissues (e.g. dura conductivity, frequency-dependent conductivity). MAIN RESULTS:Several anatomic and modeling factors produced significant percent differences or errors in activation thresholds. Rostrocaudal positioning of the cathode with respect to the vertebrae had a large effect (up to 32%) on activation thresholds. Variability in electrode impedance produced significant changes in activation thresholds for voltage-controlled stimulation (38% to 51%), but had little effect on activation thresholds for current-controlled stimulation (less than 13%). Changing the dura conductivity also produced significant differences in activation thresholds. SIGNIFICANCE:This study demonstrates several anatomic and technical factors that can affect the neural response to SCS. These factors should be considered in clinical implementation and in future computational modeling studies of thoracic SCS.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
Zander HJ,Graham RD,Anaya CJ,Lempka SFdoi
10.1088/1741-2552/ab8fc4subject
Has Abstractpub_date
2020-06-12 00:00:00pages
036019issue
3eissn
1741-2560issn
1741-2552journal_volume
17pub_type
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journal_title:Journal of neural engineering
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journal_title:Journal of neural engineering
pub_type: 杂志文章
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更新日期:2012-08-01 00:00:00
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pub_type: 杂志文章
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pub_type: 杂志文章
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更新日期:2018-10-01 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
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更新日期:2013-08-01 00:00:00
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更新日期:2020-08-05 00:00:00
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pub_type: 杂志文章,评审
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更新日期:2018-08-01 00:00:00
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更新日期:2008-12-01 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
doi:10.1088/1741-2560/4/3/003
更新日期:2007-09-01 00:00:00
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journal_title:Journal of neural engineering
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pub_type: 杂志文章
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更新日期:2020-08-04 00:00:00
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pub_type: 杂志文章
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更新日期:2020-12-09 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
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更新日期:2020-01-24 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
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更新日期:2009-10-01 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
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更新日期:2019-02-01 00:00:00
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pub_type: 杂志文章
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更新日期:2016-08-01 00:00:00
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pub_type: 杂志文章
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更新日期:2018-08-01 00:00:00
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journal_title:Journal of neural engineering
pub_type: 杂志文章
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更新日期:2018-10-01 00:00:00