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2.7. Empirical determination of the sensitivity threshold α
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2.7. Empirical determination of the sensitivity threshold α
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2.7. Empirical determination of the sensitivity threshold α
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2.7. Empirical determination of the sensitivity threshold α
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Image Search Results


2.7. Empirical determination of the sensitivity threshold α

Journal: Journal of neural engineering

Article Title: A retrospective evaluation of automated optimization of deep brain stimulation parameters

doi: 10.1088/1741-2552/ab35b1

Figure Lengend Snippet: 2.7. Empirical determination of the sensitivity threshold α

Article Snippet: Furthermore, given that recent simulation studies have demonstrated the possible benefit of anodic stimulation ( Anderson, Duffley, Vorwerk, Dorval and Butson; 2019 ), the optimization algorithm should also be evaluated without being restricted to cathodic stimulation in both computational and patient studies.

Techniques:

a) Target activation, measured in % of ΩVIM for which ap is above the sensitivity α, for the result of the optimization algorithm (purple) and clinically chosen stimulation setting (orange). b) Predicted power consumption for the result of the optimization algorithm and clinically chosen setting.

Journal: Journal of neural engineering

Article Title: A retrospective evaluation of automated optimization of deep brain stimulation parameters

doi: 10.1088/1741-2552/ab35b1

Figure Lengend Snippet: a) Target activation, measured in % of ΩVIM for which ap is above the sensitivity α, for the result of the optimization algorithm (purple) and clinically chosen stimulation setting (orange). b) Predicted power consumption for the result of the optimization algorithm and clinically chosen setting.

Article Snippet: Furthermore, given that recent simulation studies have demonstrated the possible benefit of anodic stimulation ( Anderson, Duffley, Vorwerk, Dorval and Butson; 2019 ), the optimization algorithm should also be evaluated without being restricted to cathodic stimulation in both computational and patient studies.

Techniques: Activation Assay

Change in predicted target activation and predicted power consumption for optimized  stimulation  settings in comparison to monopolar  stimulation  with the best therapeutic effect.

Journal: Journal of neural engineering

Article Title: A retrospective evaluation of automated optimization of deep brain stimulation parameters

doi: 10.1088/1741-2552/ab35b1

Figure Lengend Snippet: Change in predicted target activation and predicted power consumption for optimized stimulation settings in comparison to monopolar stimulation with the best therapeutic effect.

Article Snippet: Furthermore, given that recent simulation studies have demonstrated the possible benefit of anodic stimulation ( Anderson, Duffley, Vorwerk, Dorval and Butson; 2019 ), the optimization algorithm should also be evaluated without being restricted to cathodic stimulation in both computational and patient studies.

Techniques: Activation Assay, Comparison

P1R - Visualization of VIM (green), ap and af mapped on Vc and IC (left colorbar in mV/mm2) and stimulation voltage mapped on lead contacts (right colorbar in V), respectively, for a) optimization result and b) monopolar stimulation with 2.5 V at C3.

Journal: Journal of neural engineering

Article Title: A retrospective evaluation of automated optimization of deep brain stimulation parameters

doi: 10.1088/1741-2552/ab35b1

Figure Lengend Snippet: P1R - Visualization of VIM (green), ap and af mapped on Vc and IC (left colorbar in mV/mm2) and stimulation voltage mapped on lead contacts (right colorbar in V), respectively, for a) optimization result and b) monopolar stimulation with 2.5 V at C3.

Article Snippet: Furthermore, given that recent simulation studies have demonstrated the possible benefit of anodic stimulation ( Anderson, Duffley, Vorwerk, Dorval and Butson; 2019 ), the optimization algorithm should also be evaluated without being restricted to cathodic stimulation in both computational and patient studies.

Techniques: