Brain stimulation models, systems, devices, and methods

• US 7,904,134 B2
• Filed: 02/19/2008
• Issued: 03/08/2011
• Est. Priority Date: 07/07/2004
• Status: Active Grant

• Current Assignee: Cleveland Clinic Foundation
• Original Assignee: Cleveland Clinic Foundation
• Inventors: Butson, Christopher R., McIntyre, Cameron C., Henderson, Jaimie M., Hall, John D.
• Primary Examiner(s): Casler; Brian
• Assistant Examiner(s): MEHTA, PARIKHA SOLANKI
• Application Number:
  US12/070,521
• Publication Number:
  US 20080154341A1
• Time in Patent Office: 1,113 Days
• Field of Search: 600/407, 600/408, 600/416, 600/377, 600/378, 600/544, 607/2, 607/45, 607/115, 607/116, 607/148, 382/128, 382/131, 703/2
• US Class Current: 600/407
• CPC Class Codes: A61B 2090/364 Correlation of different images or relation of image positions in respect to the body
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 90/37 Surgical systems with images on a monitor during operation
• CPC Class Codes: A61N 1/0529 Electrodes for brain stimulation
• CPC Class Codes: A61N 1/0531 Brain cortex electrodes
• CPC Class Codes: A61N 1/0534 Electrodes for deep brain stimulation
• CPC Class Codes: A61N 1/0539 Anchoring of brain electrode systems, e.g. within burr hole
• CPC Class Codes: A61N 1/36082 Cognitive or psychiatric applications, e.g. dementia or Alzheimer's disease
• CPC Class Codes: A61N 1/36135 using physiological parameters
• CPC Class Codes: A61N 1/36146 specified by the stimulation parameters
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. A model computes a volume of influence region for a simulated electrical stimulation using certain stimulation parameters, such as amplitude, pulsewidth, frequency, pulse morphology, electrode contact selection or location, return path electrode selection, pulse polarity, etc. The model uses a non-uniform tissue conductivity. This accurately represents brain tissue, which has highly directionally conductive neuron pathways yielding a non-homogeneous and anisotropic tissue medium. In one example, the non-uniform tissue conductivity is obtained from diffusion tensor imaging (DTI) data. In one example, a second difference of an electric potential distribution is used to define a volume of activation (VOA) or similar volume of influence. In another example, a neuron or axon model is used to calculate the volume of influence without computing the second difference of the electric potential distribution.

Claims

1. A computer-assisted method for determining a volume of influence by an electrode inserted in neural tissue in a patient, comprising:

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;

solving the mathematical model to calculate a second derivative or an approximation of a second derivative of an electrical potential produced by the electrode under a set of electrode stimulation conditions within the neural tissue;

determining a predicted volume of influence using the second derivative or approximation of the second derivative of the electrical potential; and

displaying the volume of influence on a display screen.

2. The method of claim 1, 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.

3. The method of claim 2, wherein the threshold value is obtained by applying the set of electrode stimulation conditions to pre-determined threshold criteria.

4. The method of claim 3, wherein the pre-determined threshold criteria are determined from applying different electrode stimulation conditions to one or more neuron models that represent a neuron activation mechanism in the neural tissue.

5. The method of claim 2, 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 the activation of neurons in the neuron model.

6. The method of claim 1, wherein the electrode stimulation conditions include one or more of electrode stimulation parameters, electrode position, or electrode configuration.

7. The method of claim 1, wherein the mathematical model is a finite element analysis (FEA) model of the electrode inside the neural tissue.

8. The method of claim 1, wherein the mathematical model is solved by calculating a second difference of the electrical potential along one or more directions.

9. The method of claim 1, further comprising:

having a target volume of tissue to be influenced; and

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 tissue to be influenced.

10. The method of claim 9, wherein the electrode simulation conditions include one or more of electrode position, electrode configuration, or electrode stimulation parameters.

11. The method of claim 9, further comprising selecting a particular electrode stimulation condition based on the comparison of the multiple test volumes of influence to the target volume of tissue to be influenced.

12. The method of claim 9, wherein the step of comparing the multiple test volumes of influence to the target volume of tissue to be influenced comprises:

calculating a score that comprises an amount of test volume of influence that overlaps with the target volume of tissue to be influenced.

13. The method of claim 12, wherein the score further comprises an amount of test volume of influence that is outside the target volume of tissue to be influenced.

14. The method of claim 13, wherein a weighting for the amount of test volume of influence that overlaps with the target volume of tissue to be influenced is different from a weighting for the amount of test volume of influence that is outside the target volume of tissue to be influenced.

15. The method of claim 13, wherein the score further comprises an amount of target volume of tissue to be influenced that does not overlap with the test volume of influence.

16. The method of claim 12, wherein the score further comprises an amount of test volume of influence that overlaps with regions of neural tissue that induce side effects.

17. The method of claim 9, wherein the target volume of tissue to be influenced is based on an anatomical structure of the neural tissue.

18. The method of claim 17, wherein the anatomical structure is a subthalamic nucleus or a portion of a subthalamic nucleus.

19. The method of claim 9, wherein the target volume of tissue to be influenced is indicated for a treatment of Parkinson'"'"'s disease.

20. The method of claim 1, wherein the mathematical model further comprises a representation of an encapsulation tissue around the electrode.

21. The method of claim 1, wherein the neural tissue is represented as having a constant tissue conductivity.

22. The method of claim 1, wherein the tissue conductivity characteristic is electrical conductivity, anisotropy, or non-homogeneity of the neural tissue.

23. The method of claim 1, further comprising displaying the predicted volume of influence on a display screen.

24. A non-transitory computer-readable storage medium comprising instructions for:

configuring a mathematical model of an electrode inserted inside neural tissue of a patient, wherein the mathematical model comprises a parameter representing a tissue conductivity characteristic of the neural tissue;

solving the mathematical model to calculate a second derivative or an approximation of a second derivative of an electrical potential produced by the electrode under a set of electrode stimulation conditions within the neural tissue; and

determining a predicted volume of influence using the second derivative or approximation of the second derivative of the electrical potential.

25. The computer-readable storage medium of claim 24, 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.

26. The computer-readable storage medium of claim 25, further comprising instructions for:

solving the mathematical model to calculate an electrical field produced by the electrode;

establishing 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 the activation of neurons in the neuron model.

27. The computer-readable storage medium of claim 25, wherein the threshold value is obtained by applying the set of electrode stimulation conditions to pre-determined threshold criteria.

28. The computer-readable storage medium of claim 27, wherein the pre-determined threshold criteria are determined from applying different electrode stimulation conditions to one or more neuron models that represent a neuron activation mechanism in the neural tissue.

29. The computer-readable storage medium of claim 24, wherein the electrode stimulation conditions include one or more of electrode stimulation parameters, electrode position, or electrode configuration.

30. The computer-readable storage medium of claim 24, wherein the mathematical model is a finite element analysis (FEA) model of the electrode inside the neural tissue.

31. The computer-readable storage medium of claim 24, wherein the mathematical model is solved by calculating a second difference of the electrical potential along one or more directions.

32. The computer-readable storage medium of claim 24, further comprising instructions for:

receiving a target volume of tissue to be influenced;

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 tissue to be influenced.

33. The computer-readable storage medium of claim 32, further comprising instructions for selecting a particular electrode stimulation condition based on the comparison of the multiple test volumes of influence to the target volume of tissue to be influenced.

34. The computer-readable storage medium of claim 32, wherein the step of comparing the multiple test volumes of influence to the target volume of tissue to be influenced comprises:

calculating a score that comprises an amount of test volume of influence that overlaps with the target volume of tissue to be influenced.

35. The computer-readable storage medium of claim 34, wherein the score further comprises an amount of test volume of influence that is outside the target volume of tissue to be influenced.

36. The computer-readable storage medium of claim 35, wherein a weighting for the amount of test volume of influence that overlaps with the target volume of tissue to be influenced is different from a weighting for the amount of test volume of influence that is outside the target volume of tissue to be influenced.

37. The computer-readable storage medium of claim 35, wherein the score further comprises an amount of target volume of tissue to be influenced that does not overlap with the test volume of influence.

38. The computer-readable storage medium of claim 34, wherein the score further comprises an amount of test volume of influence that overlaps with regions of neural tissue that induce side effects.

39. The computer-readable storage medium of claim 32, wherein the target volume of tissue to be influenced is based on an anatomical structure of the neural tissue.

40. The computer-readable storage medium of claim 39, wherein the anatomical structure is a subthalamic nucleus or a portion of a subthalamic nucleus.

41. The computer-readable storage medium of claim 32, wherein the target volume of tissue to be influenced is indicated for a treatment of Parkinson'"'"'s disease.

42. The computer-readable storage medium of claim 32, wherein the electrode simulation conditions include one or more of electrode position, electrode configuration, or electrode stimulation parameters.

43. The computer-readable storage medium of claim 24, wherein the mathematical model further comprises a representation of an encapsulation tissue around the electrode.

44. The computer-readable storage medium of claim 43, wherein the encapsulation tissue is represented as a region around the electrode having a lower conductivity than a portion of the neural tissue that is not part of the encapsulation tissue.

45. The computer-readable storage medium of claim 44, further comprising instructions for:

receiving clinical data relating to a patient'"'"'s response to stimulation of the patient'"'"'s neural tissue; and

adjusting the conductivity of the encapsulation tissue represented by the mathematical model based on the clinical data.

46. The computer-readable storage medium of claim 24, wherein the neural tissue is represented as having a constant tissue conductivity.

47. The computer-readable storage medium of claim 24, wherein the tissue conductivity characteristic is electrical conductivity, anisotropy, or non-homogeneity of the neural tissue.

48. The computer-readable storage medium of claim 24, further comprising instructions for displaying the predicted volume of influence on a display screen.

49. A computer system having a display screen, the computer system being programmed to perform steps that comprise:

configuring a mathematical model of an electrode inserted inside neural tissue of a patient, wherein the mathematical model comprises a parameter representing a tissue conductivity characteristic of the neural tissue;

solving the mathematical model to calculate a second derivative or an approximation of a second derivative of an electrical potential produced by the electrode under a set of electrode stimulation conditions within the neural tissue;

determining a predicted volume of influence using the second derivative or approximation of the second derivative of the electrical potential; and

displaying the volume of influence on the display screen.

50. The computer system of claim 49, 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.

51. The computer system of claim 50, wherein the threshold value is obtained by applying the set of electrode stimulation conditions to pre-determined threshold criteria.

52. The computer system of claim 51, wherein the pre-determined threshold criteria are determined from applying different electrode stimulation conditions to one or more neuron models that represent a neuron activation mechanism in the neural tissue.

53. The computer system of claim 50, wherein the computer system is programmed to perform steps that further comprise:

solving the mathematical model to calculate an electrical field produced by the electrode;

establishing 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 the activation of neurons in the neuron model.

54. The computer system of claim 49, wherein the electrode stimulation conditions include one or more of electrode stimulation parameters, electrode position, or electrode configuration.

55. The computer system of claim 49, wherein the mathematical model is a finite element analysis (FEA) model of the electrode inside the neural tissue.

56. The computer system of claim 49, wherein the mathematical model is solved by calculating a second difference of the electrical potential along one or more directions.

57. The computer system of claim 49, wherein the computer system is programmed to perform steps that further comprise:

receiving a target volume of tissue to be influenced;

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 tissue to be influenced.

58. The computer system of claim 57, wherein the electrode simulation conditions include one or more of electrode position, electrode configuration, or electrode stimulation parameters.

59. The computer system of claim 57, wherein the computer system is programmed to perform steps that further comprises:

selecting a particular electrode stimulation condition based on the comparison of the multiple test volumes of influence to the target volume of tissue to be influenced.

60. The computer system of claim 57, wherein the step of comparing the multiple test volumes of influence to the target volume of tissue to be influenced comprises:

calculating a score that comprises an amount of test volume of influence that overlaps with the target volume of tissue to be influenced.

61. The computer system of claim 60, wherein the score further comprises an amount of test volume of influence that is outside the target volume of tissue to be influenced.

62. The computer system of claim 61, wherein a weighting for the amount of test volume of influence that overlaps with the target volume of tissue to be influenced is different from a weighting for the amount of test volume of influence that is outside the target volume of tissue to be influenced.

63. The computer system of claim 61, wherein the score further comprises an amount of target volume of tissue to be influenced that does not overlap with the test volume of influence.

64. The computer system of claim 60, wherein the score further comprises an amount of test volume of influence that overlaps with regions of neural tissue that induce side effects.

65. The computer system of claim 57, wherein the target volume of tissue to be influenced is based on an anatomical structure of the neural tissue.

66. The computer system of claim 65, wherein the anatomical structure is a subthalamic nucleus or a portion of a subthalamic nucleus.

67. The computer system of claim 57, wherein the target volume of tissue to be influenced is indicated for a treatment of Parkinson'"'"'s disease.

68. The computer system of claim 49, wherein the mathematical model further comprises a representation of an encapsulation tissue around the electrode.

69. The computer system of claim 68, wherein the encapsulation tissue is represented as a region around the electrode having a lower conductivity than a portion of the neural tissue that is not part of the encapsulation tissue.

70. The computer system of claim 69, wherein the computer system is programmed to perform steps that further comprise:

receiving clinical data relating to a patient'"'"'s response to stimulation of the patient'"'"'s neural tissue; and

adjusting the conductivity of the encapsulation tissue represented by the mathematical model based on the clinical data.

71. The computer system of claim 49, wherein the neural tissue is represented as having a constant tissue conductivity.

72. The computer system of claim 49, wherein the tissue conductivity characteristic is electrical conductivity, anisotropy, or non-homogeneity of the neural tissue.