Automated, objective method of assessing tinnitus condition
Described herein are systems and methods for determining or quantifying tinnitus conditions within a patient, including determining if a patient has some form of tinnitus as well as categorizing or determining the severity of a tinnitus condition, if present. The described systems and methods are also useful in evaluating the efficacy a tinnitus treatment by measuring the degree of reduction of tinnitus symptoms in a patient. The described systems and methods also provide a personalized profile for each specific patient, allowing for more effective treatment options as various forms or causes of tinnitus become apparent.
1. A method for determining a tinnitus condition of a patient comprising:
providing a functional magnetic resonance imaging (fMRI) device;
imaging said patient with said fMRI device thereby generating a fMRI map of at least a portion of a brain of said patient;
identifying a plurality of voxels in said fMRI map corresponding to regions of the at least a portion of said brain of said patient;
analyzing said plurality of voxels, thereby determining the tinnitus condition of said patient;
wherein the analyzing step comprises identifying a functional connection between voxels corresponding to a first region of said brain and voxels corresponding to a second region of said brain; and
wherein the first region is an amygdala region and the second region is a precuneus region of said brain of said patient.
2. The method of claim 1 , wherein said imaging step is performed in a resting state of said patient.
3. The method of claim 1 , wherein said tinnitus condition is the presence of or absence of tinnitus in said patient.
4. The method of claim 1 , wherein said tinnitus condition is a stage of progression of tinnitus in said patient.
5. The method of claim 1 , wherein said tinnitus condition is a type or severity of tinnitus of said patient.
6. The method of claim 1 , wherein said patient is currently undergoing a treatment for tinnitus and said tinnitus condition is a measure of efficacy of said treatment.
7. The method of claim 1 , wherein said step of identifying a plurality of voxels identifies a number of voxels selected from the range of 10 to 40 voxels.
8. The method of claim 1 , wherein said step of identifying a plurality of voxels identifies a number of voxels greater than or equal to 15 voxels.
9. The method of claim 1 , wherein said imaging step is performed over a predetermined period of time and wherein each of said plurality of voxels includes a time component.
10. The method of claim 9 , wherein said step of analyzing said plurality of voxels includes analyzing said plurality of voxels in the time domain.
11. The method of claim 9 , wherein said step of analyzing said plurality of voxels further comprises invariant analysis with respect to paramertrization of activity in said voxels with respect to time.
12. The method of claim 1 , wherein said imaging step generates said fMRI map as a time series of blood oxygen levels corresponding to a time period of greater than or equal to 5 minutes.
13. The method of claim 12 , wherein said step of analyzing said plurality of voxels analyzes each voxel over a time interval of less than or equal to 10 seconds.
14. A method for determining a tinnitus condition of a patient comprising:
providing a functional magnetic resonance imaging (fMRI) device;
imaging said patient with said fMRI device thereby generating a fMRI map of at least a portion of a brain of said patient;
identifying a plurality of voxels in said fMRI map corresponding to regions of the at least a portion of said brain of said patient;
analyzing said plurality of voxels, thereby determining the tinnitus condition of said patient, wherein the analyzing step comprises:
iteratively integrating at least a portion of a time series corresponding to each of said plurality of voxels thereby generating a plurality of irreducible trajectories;
generating a lead matrix comprised of a plurality of signed areas wherein determination of sign is informed by the direction of traversal of said irreducible trajectories; and
identifying a functional connection between voxels corresponding to a first region of said brain and voxels corresponding to a second region of said brain.
15. The method of claim 14 , wherein said step of analyzing said plurality of voxels utilizes a chain of offsets model.
16. The method of claim 1 , wherein said step of analyzing said plurality of voxels further comprises a step of reducing noise in said fMRI map.
17. The method of claim 1 , wherein said step of analyzing said plurality of voxels further comprises comparing said plurality of voxels to a library of voxel data in order to determine said tinnitus condition.
18. The method of claim 1 , wherein said step of analyzing said plurality of voxels further comprises comparing said one or more functional connections to a library of connection data in order to determine said tinnitus condition.
19. The method of claim 18 , wherein said comparing step is performed by a processor utilizing machine learning.
20. The method of claim 1 , wherein said fMRI map corresponds to a portion of said brain of said patient.
21. The method of claim 1 , wherein said fMRI map corresponds to substantially all of said brain of said patient.
22. The method of claim 1 , wherein said fMRI map is a three dimensional representation of said patient's brain over time.
23. The method of claim 1 , wherein the analyzing step comprises identifying an additional functional connection between voxels corresponding to a third region of said brain and voxels corresponding to a fourth region of said brain.