Method and system for displaying a volume of influence by an electrode inserted in neural tissue
This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, a target volume of activation (VOA) can be received, a test VOA can be simulated, and at least one of a target electrode location or parameter can be provide using a relationship between the target VOA and the test VOA.
1. A method for displaying a volume of influence by an electrode inserted in neural tissue of a patient, the method comprising:
calculating a predicted volume of influence, wherein the predicted volume of influence is a prediction of the amount of neural tissue that will be activated by the electrode:
displaying a target volume of influence and the predicted volume of influence on a display screen; and
configuring a mathematical model of (a) the electrode inserted inside the neural tissue and (b) a representation of an encapsulation tissue around the electrode.
2. The method of claim 1 , wherein the mathematical model comprises a parameter representing a tissue conductivity characteristic of the neural tissue.
3. The method of claim 2 , wherein the mathematical model is a finite element analysis (FEA) model of the electrode inside the neural tissue.
4. The method of claim 2 , wherein the tissue conductivity characteristic is electrical conductivity of the neural tissue.
5. A method for displaying a volume of influence by an electrode inserted in neural tissue of a patient, the method comprising:
calculating a predicted volume of influence, wherein the predicted volume of influence is a prediction of the amount of neural tissue that will be activated by the electrode;
displaying a target volume of influence and the predicted volume of influence on a display screen; and
configuring a mathematical model of the electrode inserted inside the neural tissue;
wherein the mathematical model comprises:
a parameter representing a tissue conductivity characteristic of the neural tissue; and
a representation of an encapsulation tissue around the electrode.
6. The method of claim 5 , further comprising displaying anatomical features on the display screen.
7. The method of claim 5 , further comprising displaying a representation of the electrode on the display screen.
8. The method of claim 5 , wherein the neural tissue is represented as having a constant tissue conductivity.
9. A non-transitory computer-readable storage medium comprising instructions for:
calculating a predicted volume of influence, wherein the predicted volume of influence is a prediction of the amount of neural tissue that will be activated by an electrode;
displaying a target volume of influence and the predicted volume of influence on a display screen; and
configuring a mathematical model of (a) the electrode inserted inside the neural tissue and (b) a representation of an encapsulation tissue around the electrode.
10. The not transitory computer-readable storage medium of claim 9 , wherein the mathematical model comprises a parameter representing a tissue conductivity characteristic of the neural tissue.
11. The non-transitory computer-readable storage medium of claim 10 , wherein the mathematical model is a finite element analysis (FEA) model of the electrode inside the neural tissue.
12. The non-transitory computer-readable storage medium of claim 10 , wherein the tissue conductivity characteristic is electrical conductivity of the neural tissue.
13. A computer system having a display screen, the computer system programmed to perform steps that comprise:
calculating a predicted volume of influence, wherein the predicted volume of influence is a prediction of the amount of neural tissue that will be activated by an electrode;
displaying a target volume of influence and the predicted volume of influence on a display screen; and
configuring a mathematical model of (a) the electrode inserted inside the neural tissue and (b) a representation of an encapsulation tissue around the electrode.
14. The computer system of claim 13 , wherein the mathematical model comprises a parameter representing a tissue conductivity characteristic of the neural tissue.
15. The computer system of claim 14 , wherein the mathematical model is a finite element analysis (FEA) model of the electrode inside the neural tissue.
16. The computer system of claim 14 , wherein the tissue conductivity characteristic is electrical conductivity of the neural tissue.
17. The computer system of claim 13 , wherein the steps further comprise displaying anatomical features on the display screen.
18. The computer system of claim 13 , wherein the steps further comprise displaying a representation of the electrode on the display screen.
19. A method for displaying a volume of influence by an electrode inserted in neural tissue of a patient, comprising:
calculating a predicted volume of influence, wherein the predicted volume of influence is a prediction of the amount of neural tissue that will be activated by the electrode;
displaying a target volume of influence and the predicted volume of influence on a display screen; and
configuring a mathematical model of the electrode inserted inside the neural tissue; wherein:
the mathematical model comprises a parameter representing a tissue conductivity characteristic of the neural tissue; and
calculating the predicted volume of influence comprises:
solving the mathematical model to calculate a second derivative or an approximation of the second derivative of an electrical potential produced by the electrode under a set of electrode stimulation conditions within the neural tissue; and
determining the predicted volume of influence using the second derivative or approximation of the second derivative of the electrical potential.
20. The method of claim 19 , wherein the step of determining the predicted volume of influence comprises comparing the second derivative or approximation of the second derivative to a threshold value that represents activation of neurons in the neural tissue.
21. The method of claim 20 , wherein the threshold value is obtained from an application of the set of electrode stimulation conditions to pre-determined threshold criteria.
22. The method of claim 21 , wherein the pre-determined threshold criteria are determined from an application of different electrode stimulation conditions to one or more neuron models that represent a neuron activation mechanism in the neural tissue.
23. The method of claim 21 , further comprising:
solving the mathematical model to calculate an electrical field produced by the electrode;
having a neuron model to represent a neuron activation mechanism in the neural tissue;
placing the neuron model in the calculated electrical field; and
determining the threshold value for the second derivative or approximation of the second derivative of the electrical potential for activation of neurons in the neuron model.
24. The method of claim 19 , wherein the electrode stimulation conditions include one or more of electrode stimulation parameters, electrode position, or electrode configuration.
25. The method of claim 19 , wherein the mathematical model is solved by calculating a second difference of the electrical potential along one or more directions.
26. The method of claim 19 , further comprising:
repeatedly solving the mathematical model using different electrode stimulation conditions to obtain multiple predicted volumes of influence as test volumes of influence; and
comparing the multiple test volumes of influence to the target volume of influence.
27. The method of claim 26 , wherein the electrode simulation conditions include one or more of electrode position, electrode configuration, or electrode stimulation parameters.
28. The method of claim 26 , further comprising selecting a particular electrode stimulation condition based on the comparison of the multiple test volumes of influence to the target volume of influence.