Methods for physiological monitoring training, exercise and regulation

• US 7,567,693 B2
• Filed: 11/08/2005
• Issued: 07/28/2009
• Est. Priority Date: 01/30/2001
• Status: Expired due to Fees

• Current Assignee: Decharms R. Christopher
• Original Assignee: Decharms R. Christopher
• Inventors: deCharms, R. Christopher
• Primary Examiner(s): AZARIAN, SEYED H
• Application Number:
  US11/270,064
• Publication Number:
  US 20060078183A1
• Time in Patent Office: 1,358 Days
• Field of Search: 382/128, 382/129, 382/130, 382/131, 382/132, 382/133, 382/154, 382/155, 382/170, 382/171, 382/172, 382/203, 382/254, 382/260, 382/274, 382/285, 382/305, 382/100, 600/410, 600/417, 600/544, 600/558, 424/9.2, 514/214.01, 378/21, 378/4
• US Class Current: 382/128
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7285 for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: A61B 5/7425 Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image geometric transforms for image registration G06T3/14; enhancement using image fusion G06T5/50; algorithms for registration of images G06T7/30
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 6/541 involving acquisition triggered by a physiological signal
• CPC Class Codes: A61B 8/0808 for diagnosis of the brain
• CPC Class Codes: A61B 8/543 involving acquisition triggered by a physiological signal

Abstract

Computer executable software and device for guiding brain activity training comprising: logic which takes data corresponding to activity measurements of one or more internal voxels of a brain and determines one or more members of the group consisting of: a) what next stimulus to communicate to the subject, b) what next behavior to instruct the subject to perform, c) when a subject is to be exposed to a next stimulus, d) when the subject is to perform a next behavior, e) one or more activity metrics computed from the measured activity, f) a spatial pattern computed from the measured activity, g) a location of a region of interest computed from the measured activity, h) performance targets that a subject is to achieve computed from the measured activity, i) a performance measure of a subject'"'"'s success computed from the measured activity, j) a subject'"'"'s position relative to an activity measurement instrument; and logic for communicating information based on the determinations to the subject in substantially real time relative to when the activity is measured.

Claims

1. A method comprising:

(a) taking fMRI activity measurements of at least one localized region of a brain of a subject who is inside a fMRI scanner;

(b) communicating a representation of said activity measurements to said subject using a computer executable logic; and

(c) training said subject while inside said fMRI scanner to use said representation of said activity measurements to control said activity, wherein said representation is communicated in the form of presenting a display of said subject'"'"'s level of success in modulating said activity in said brain to said subject.

2. The method of claim 1 wherein said representation is communicated in substantially real time.

3. The method of claim 1 wherein said at least one localized region is internal relative to the surface of said brain.

4. The method of claim 1 comprising taking said fMRI activity measurements of a plurality of different internal localized regions of interest.

5. The method of claim 1 wherein said representation is communicated via a graphical user interface.

6. The method of claim 1 wherein said representation is communicated in a form selected from the group consisting of physiological images of said subject'"'"'s brain, matched anatomical images at the same level of section, 3-D reconstructions of either anatomy or physiological activation patterns, and both difference activity level images and statistical maps.

7. The method of claim 1 wherein said representation is communicated using virtual reality stimuli.

8. The method of claim 1 wherein modulating said activity modulates a sensation of experienced pain by said subject.

9. The method of claim 1 wherein said subject has depression.

10. The method of claim 1 wherein said subject has anxiety disorder.

11. The method of claim 1 wherein said subject has attention deficit hyperactivity disorder.

12. The method of claim 1 wherein said subject has brain injury.

13. The method of claim 1 wherein said subject has had a stroke.

14. The method of claim 1 wherein said subject has epilepsy.

15. The method of claim 1 wherein modulating said activity modulates the release of a neuromodulator.

16. The method of claim 15 wherein said neuromodulator is selected from dopamine, acetylcholine and serotonin, an endogenous opiate, adrenaline, and norepinephrine.

17. The method of claim 1 wherein said fMRI activity measurements include real time motion correction.

18. The method of claim 1 wherein said fMRI measurements are spatially registered to measurements made across days.

19. The method of claim 1 wherein said fMRI activity measurements include a spatial pattern comparison metric that compares a spatial pattern of activity in said at least one localized region with another spatial pattern.

20. The method of claim 1 wherein said subject further communicates with an operator by audio and/or video communication.

21. The method of claim 1 wherein a computer interface is provided that allows said subject to input information.

22. The method of claim 1 further comprising repeating said method across multiple training sessions.

23. The method of claim 1 wherein step (a) further comprises collecting volume data;

and, pre-processing said volume data by at least one process selected from the group consisting of spatial smoothing, temporal filtering, slice time correction, transformation into standard coordinates, resampling of data, motion correction of data, and regression filtering.

24. The method of claim 1 wherein said subject suffers from pain.

25. The method of claim 1 wherein said subject suffers from substance abuse.

26. The method of claim 1 wherein said fMRI activity measurements are taken from an anatomically-defined region of interest.

27. The method of claim 1 wherein said fMRI activity measurements are taken from a physiologically-defined region of interest.

28. The method of claim 1 wherein said subject suffers from schizophrenia.

29. The method of claim 1 wherein said subject suffers from memory loss.

30. A method comprising:

(a) taking fMRI activity measurements of at least one localized region of a brain of a subject who is inside a fMRI scanner, wherein said fMRI activity measurement include real time motion correction;

(b) communicating information about changes in said level of said activity to said subject using a computer executable logic; and

(c) training said subject while inside said fMRI scanner to use said information about changes in said level of said activity to control said activity.

31. The method of claim 30 wherein said fMRI activity measurements are averaged from a plurality of fMRI image volumes.

32. The method of claim 30 comprising taking said fMRI activity measurements of a plurality of different internal localized regions of interest.

33. The method of claim 30 wherein said fMRI activity measurements include a comparison of activity metrics from different regions of interest.

34. The method of claim 30 wherein said fMRI activity measurements include a comparison of activity metrics from the same region of interest during different time points.