Analysis of brain patterns using temporal measures

• US 20080091118A1
• Filed: 07/06/2007
• Published: 04/17/2008
• Est. Priority Date: 07/06/2006
• Status: Active Grant

• Current Assignee: Regents of The University of Minnesota (University of Minnesota)
• Original Assignee: Regents of The University of Minnesota (University of Minnesota)
• Inventors: Georgopoulos, Apostolos
• Granted Patent: US 9,101,276 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/245 specially adapted for magnetoencephalographic [MEG] signals
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/375 using biofeedback
• CPC Class Codes: A61B 5/4064 Evaluating the brain A61B5/031, A61B5/369, A61B5/14553 take precedence
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/4088 Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A set of brain data representing a time series of neurophysiologic activity acquired by spatially distributed sensors arranged to detect neural signaling of a brain (such as by the use of magnetoencephalography) is obtained. The set of brain data is processed to obtain a dynamic brain model based on a set of statistically-independent temporal measures, such as partial cross correlations, among groupings of different time series within the set of brain data. The dynamic brain model represents interactions between neural populations of the brain occurring close in time, such as with zero lag, for example. The dynamic brain model can be analyzed to obtain the neurophysiologic assessment of the brain. Data processing techniques may be used to assess structural or neurochemical brain pathologies.

Claims

1. A system for analyzing and classifying neurophysiologic activity of a subject, the system comprising:

a data input that receives a set of subject data representing a time series of neurophysiologic activity acquired by a multiplicity of spatially distributed sensors arranged to detect neural signaling in the subject during an eyes-open idle state;

a data store that stores a plurality of templates classified according to various brain conditions, wherein each of the templates represents selected subsets of statistically-independent temporal measures among neural populations measured from at least one other subject known to present a given brain condition;

a processor communicatively coupled to the data input and to the data store, and programmed to;

process the set of subject data to obtain a dynamic model that represents temporal measures among neural populations in the subject; and

compare at least a portion of the dynamic model with the plurality of templates to produce a classification of neurophysiologic activity of the subject when the dynamic model corresponds with at least one of the plurality of templates.

2. The system of claim 1, wherein the processor is programmed to process the set of subject data to produce a prewhitened time series having a characteristic of stationarity of mean, variance, and autocorrelation.

3. The system of claim 2, wherein the processor is programmed to apply an autoregressive integrative moving average-based algorithm to produce the prewhitened time series.

4. The system of claim 1, wherein the processor is programmed to compute partial cross correlations of the set of subject data to produce estimates of strength and sign of signaling between groups of the multiplicity of sensors representing interactions of neural populations.

5. The system of claim 4, wherein the groups of the multiplicity of sensors are pairs of the multiplicity of sensors, and wherein the partial cross correlations are computed to produce estimates of strength and sign of direct signaling between at least a subset of the pairs of the multiplicity of sensors.

6. The system of claim 4, wherein the partial cross correlations are computed to produce estimates of strength and sign of direct short-term signaling between the pairs of the multiplicity of sensors occurring within time windows of about 50 milliseconds.

7. The system of claim 4, wherein the partial cross correlations are computed to produce estimates of strength and sign of direct and substantially simultaneous signaling between the pairs of the multiplicity of sensors.

8. The system of claim 4, wherein the processor is further programmed to analyze the partial cross correlations for covariance with respect to at least one parameter selected from the group consisting of:

age, race, and neuropsychological capacities, or any combination thereof.

9. The system of claim 4, wherein the partial cross correlations include at least one type selected from the group consisting of:

(a) positive partial cross correlations, and (b) negative partial cross correlations.

10. The system of claim 4, wherein the processor is further programmed to perform linear discriminant analysis on the partial cross correlations to produce a set of classification functions that are used for producing the classification.

11. The system of claim 10, wherein the processor is programmed to determine a relevant subset of the partial cross correlations that consists of certain partial cross correlations relevant for performing the classification.

12. The system of claim 11, wherein the processor is programmed to perform the linear discriminant analysis utilizing a leave-one-out algorithm wherein 100% classification is required for any one of the partial cross correlations to be deemed relevant.

13. The system of claim 11, wherein the processor is programmed to perform the linear discriminant analysis utilizing a genetic algorithm.

14. The system of claim 4, wherein the processor is further programmed to apply a thresholding function to the estimates of strength and sign of signaling between the groups of the multiplicity of sensors to produce the dynamic model.

15. The system of claim 14, wherein the classification of the neurophysiologic activity of the subject includes application of a classification function corresponding to a template of the plurality of templates to the estimates of strength and sign of signaling between the groups of the multiplicity of sensors.

16. The system of claim 1, wherein the processor is programmed to store the dynamic model in the data store in association with at least one record representing subject treatment administration history.

17. The system of claim 1, wherein the classification of the neurophysiologic activity of the subject includes an indicator of degree of the correspondence to the at least one of the plurality of templates.

18. The system of claim 1, wherein each of the templates represents only statistically significant temporal measures among neural populations relevant to the given brain condition.

19. The system of claim 1, wherein each of the templates represents a combination of selected subsets of temporal measures among neural populations of a plurality of different subjects.

20. The system of claim 1, wherein the classification of the neurophysiologic activity of the subject represents a first correspondence of the dynamic model with a first template of the plurality of templates and a second correspondence of the dynamic model with a second template of the plurality of templates that is different from the first template.

21. The system of claim 1, wherein the dynamic model is based on a dynamic neural network representing groupings of synchronous neural signaling detected by the multiplicity of sensors.

22. The system of claim 21, wherein the groupings of simultaneous neural signaling include pairwise groupings of neural signaling defined by synaptic activity in different brain locations occurring within about 1 ms.

23. The system of claim 1, wherein the processor is programmed to obtain the classification by comparing a subset of information selected from the dynamic model with at least one template of the plurality of templates.

24. The system of claim 1, wherein the processor is programmed to selectively update at least one of the plurality of templates based on the classification.

25. The system of claim 1, wherein the plurality of templates include templates that each correspond to at least one type of neural activity selected from the group consisting of:

a normal condition, Alzheimer'"'"'s Disease, pre-dementia syndrome, mild cognitive impairment, schizophrenia, Sjö

gren'"'"'s Syndrome, alcoholism, alcohol impairment, fetal alcohol syndrome, multiple sclerosis, Parkinson'"'"'s Disease, bipolar disorder, traumatic brain injury, depression, autoimmune disorder, a neurodegenerative disorder, pain, a disease affecting the central nervous system, or any combination thereof.

26. The system of claim 1, wherein the data input is communicatively coupled to a magnetoencephalogram (MEG) instrument having the multiplicity of spatially-distributed sensors.

27. The system of claim 26, wherein the sensors include superconducting quantum interference device (SQUID) sensors.

28. The system of claim 1, wherein the set of subject data includes data at a temporal resolution of about 1 ms.

29. The system of claim 1, wherein the eyes open idle state is achieved by instructions to the subject to perform an eye fixation task.

30. The system of claim 29, wherein the first set of subject data includes less than or equal to about one minute of eyes-open idle state activity.

31. The system of claim 1, wherein the data input includes a network communication device.

32. The system of claim 1, wherein the data store includes at least one database.

33. The system of claim 1, wherein the processor is programmed to process the set of subject data to remove at least one type of artifact selected from the group consisting of:

an eye blink artifact, a cardiac artifact, a skeletal muscle artifact or any combination thereof.

34. A system for analyzing neurophysiologic activity of a subject, the system comprising:

a data input that receives a set of subject data representing a time series of neurophysiologic activity acquired by each of a multiplicity of spatially distributed sensors arranged to detect neural signaling in the subject; and

a processor communicatively coupled to the data input and programmed to;

process the set of subject data to obtain a dynamic brain model that represents statistically-independent temporal measures among neural populations in the subject'"'"'s brain; and

analyze the dynamic brain model to estimate a neurophysiologic condition of the subject.

35. The system of claim 34, wherein the statistically-independent temporal measures include a set of partial cross correlations among groupings of different time series within the set of subject data representing generally synchronous interactions between neural populations in the subject'"'"'s brain.

36. The system of claim 34, wherein the processor is programmed to compare at least a portion of the dynamic brain model with a different model of neural activity such that a result of the comparison is indicative of a neurophysiologic condition of the subject.

37. The system of claim 36, wherein the dynamic brain model is a first dynamic brain model of the subject based on the set of subject data taken at a first time, and wherein the different model of neural activity is based on a second dynamic brain model of the subject obtained from a different set of subject data taken at a second time that is different from the first time;

and

wherein the neurophysiologic condition of the subject that the processor is programmed to analyze represents a potential change in neurophysiology of the subject between the first time and the second time.

38. The system of claim 37, wherein the result of the comparison is indicative of a degree of the potential change in neurophysiology of the subject.

39. The system of claim 36, wherein the different model of neural activity corresponds to a dynamic brain model of at least one different subject;

and

wherein the neurophysiologic condition of the subject that the processor is programmed to analyze represents a potential difference in neurophysiology between the subject and the at least one different subject.

40. The system of claim 39, wherein the comparison is indicative of a degree of the potential difference in neurophysiology between the subject and the at least one subject different from the subject.

41. The system of claim 36, wherein the different model of neural activity is a neurophysiologic condition template that represents selected subsets of dynamic brain models based on sets of partial cross correlations among groupings of different time series within each of a plurality of sets of subject data corresponding to a plurality of subjects known to present a given brain condition;

and

wherein the neurophysiologic condition of the subject that the processor is programmed to analyze represents a degree with which the subject presents with the given brain condition.

42. The system of claim 36, wherein the different model of neural activity includes a plurality of different templates, including:

a first template that represents selected subsets of dynamic brain models based on sets of partial cross correlations among groupings of different time series within each of a plurality of sets of subject data corresponding to a first set of subjects known to present a first brain condition; and

a second template that represents selected subsets of dynamic brain models based on sets of partial cross correlations among groupings of different time series within each of a plurality of sets of subject data corresponding to a second set of subjects known to present a second brain condition different from the first brain condition;

wherein the result of the comparison is indicative of a degree with which the subject presents with the first brain condition and with the second brain condition.

43. The system of claim 34, wherein the neurophysiologic condition of the subject is selected from the group consisting of:

a normal condition, Alzheimer'"'"'s Disease, pre-dementia syndrome, mild cognitive impairment, schizophrenia, Sjö

gren'"'"'s Syndrome, alcoholism, alcohol impairment, fetal alcohol syndrome, multiple sclerosis, Parkinson'"'"'s Disease, bipolar disorder, traumatic brain injury, depression, autoimmune disorder, a neurodegenerative disorder, pain, a disease affecting the central nervous system, or any combination thereof.

44. The system of claim 34, wherein the groupings of different time series are pair-wise groupings of the time series corresponding to all pairs of the sensors, such that the statistically-independent temporal measures among neural populations in the subject'"'"'s brain are estimates of strength and sign of signaling between pairs of the multiplicity of sensors representing pairwise interactions among the neural populations.

45. The system of claim 34, further comprising:

a data store operatively coupled to the processor; and

wherein the processor is programmed to store the dynamic brain model in the data store in association with at least one data store entry representing administration of at least one therapy for the subject.

46. The system of claim 34, wherein the subject data includes data at a 1 ms temporal resolution.

47. The system of claim 34, wherein the subject data includes data representing neurophysiologic activity of the first subject during an open eye idle state.

48. The system of claim 47, wherein the subject data representing the time series of neurophysiologic activity includes less than or equal to about one minute of eye fixation activity.

49. The system of claim 34, wherein the data input is communicatively coupled to a magnetoencephalogram (MEG) instrument having the multiplicity of spatially-distributed sensors, selected from the group consisting of magnetometers, and axial gradiometers.

50. The system of claim 34, wherein the processor is programmed to process the subject data to produce a prewhitened time series having a characteristic of stationarity of mean, variance, and autocorrelation.

51. The system of claim 50, wherein the processor applies an autoregressive integrative moving average-based algorithm to produce the prewhitened time series.

52. The system of claim 34, wherein the processor is programmed to perform linear discriminant analysis on the set of statistically-independent temporal measures.

53. A system for analyzing neurophysiologic activity of a first subject, the system comprising:

a data input that receives sets of brain activity data corresponding to an eye fixation task, each set representing a time series of neurophysiologic activity acquired by a multiplicity of spatially distributed sensors arranged to detect neural signaling in a corresponding subject; and

a processor communicatively coupled to the data input, and programmed to;

process each set of brain activity data to produce a corresponding dynamic model of neural activity representing time-dependent coupling between neural populations of the first subject, including;

processing the brain activity data to produce a prewhitened time series having a characteristic of stationarity of mean, variance, and autocorrelation;

computing pairwise, partial cross correlations of the prewhitened time series to produce estimates of strength and sign of signaling between pairs of the multiplicity of sensors representing pairwise interactions of neural populations;

performing a classification of the partial cross correlations to produce a measure of correlation of the brain activity data to validated reference data corresponding to a plurality of different neurophysiologic conditions.

54. The system of claim 53, wherein the plurality of different neurophysiologic conditions includes at least two conditions selected from the group consisting of:

a normal condition, Alzheimer'"'"'s Disease, pre-dementia syndrome, mild cognitive impairment, schizophrenia, Sjö

gren'"'"'s Syndrome, alcoholism, alcohol impairment, fetal alcohol syndrome, multiple sclerosis, Parkinson'"'"'s Disease, bipolar disorder, traumatic brain injury, depression, an autoimmune disorder, a neurodegenerative disorder, pain, a disease affecting the central nervous system, or any combination thereof.

55. The system of claim 53, wherein the processor is programmed to apply an autoregressive integrative moving average-based algorithm to produce the prewhitened time series.

56. The system of claim 53, wherein the partial cross correlations are computed to produce estimates of strength and sign of direct short-term signaling between the pairs of the multiplicity of sensors occurring within time windows of about 50 milliseconds.

57. The system of claim 53, wherein the partial cross correlations are computed to produce estimates of strength and sign of substantially simultaneous signaling between the pairs of the multiplicity of sensors.

58. The system of claim 53, wherein the processor is further programmed to analyze for covariance at least one type of partial cross correlations selected from the group consisting of:

(a) positive partial cross correlations of the partial cross correlations of the prewhitened time series, and (b) negative partial cross correlations of the partial cross correlations of the prewhitened time series.

59. The system of claim 53, wherein the processor is further programmed to apply a thresholding function to the estimates of strength and sign of signaling between pairs of the multiplicity of sensors to produce the first dynamic model of neural activity.

60. The system of claim 53, wherein the processor is programmed to determine a relevant subset of the partial cross correlations that consists of certain partial cross correlations relevant for performing the classification.

61. The system of claim 60, wherein the processor is programmed to perform the linear discriminant analysis utilizing a leave-one-out algorithm wherein 100% classification is required for any one of the partial cross correlations to be deemed relevant.

62. The system of claim 60, wherein the processor is programmed to perform the linear discriminant analysis utilizing a genetic algorithm.

63. The system of claim 53, wherein the processor is programmed to develop a neurophysiologic template based on the classification.

64. A method for automatically classifying neurophysiologic brain activity of a subject, the method comprising:

receiving, by the data processing system, a set of subject data representing a time series of neurophysiologic activity acquired by a multiplicity of spatially distributed sensors arranged to detect neural signaling in a subject during an eyes-open idle state;

processing, by the data processing system, the set of subject data to obtain a dynamic model that represents statistically-independent temporal measures among neural populations in the subject;

maintaining, by the data processing system, a set of templates classified according to various brain conditions, wherein each of the templates represents selected subsets of statistically-independent temporal measures among neural populations measured from at least one other subject known to present a given brain condition;

comparing, by the data processing system, at least a portion of the dynamic model with the plurality of templates to produce a classification of neurophysiologic activity of the subject when the dynamic model corresponds with at least one of the plurality of templates.

65. The method of claim 64, further comprising:

processing the set of subject data to produce a prewhitened time series having a characteristic of stationarity of mean, variance, and autocorrelation.

66. The method of claim 65, wherein the processing of the set of subject data to produce a prewhitened time series includes applying an autoregressive integrative moving average-based algorithm to produce the prewhitened time series.

67. The method of claim 64, wherein the processing of the set of subject data to obtain a dynamic model includes computing partial cross correlations of the set of subject data to produce estimates of strength and sign of signaling between groups of the multiplicity of sensors representing interactions of neural populations.

68. The method of claim 67, wherein the computing partial cross correlations of the set of subject data includes producing estimates of strength and sign of signaling between sensor pairs of the multiplicity of sensors.

69. The method of claim 67, wherein the computing partial cross correlations of the set of subject data produces estimates of strength and sign of direct short-term signaling between the pairs of the multiplicity of sensors occurring within time windows of about 50 milliseconds.

70. The method of claim 67, wherein the computing partial cross correlations of the set of subject data produces estimates of strength and sign of direct and substantially simultaneous signaling between the pairs of the multiplicity of sensors.

71. The method of claim 67, further comprising:

analyzing the partial cross correlations for covariance with respect to at least one parameter selected from the group consisting of;

age, race, and neuropsychological capacities, or any combination thereof.

72. The method of claim 67, wherein the computing partial cross correlations of the set of subject data produces at least one partial cross correlation type selected from the group consisting of:

(a) positive partial cross correlations, and (b) negative partial cross correlations.

73. The method of claim 67, further comprising:

performing linear discriminant analysis on the partial cross correlations to produce a set of classification functions; and

utilizing the classification functions to produce the classification.

74. The method of claim 73, further comprising:

determining a relevant subset of the partial cross correlations that consists of certain partial cross correlations relevant for performing the classification.

75. The method of claim 74, wherein the performing of the linear discriminant analysis includes utilizing a leave-one-out algorithm wherein 100% classification is required for any one of the partial cross correlations to be deemed relevant.

76. The method of claim 74, wherein the performing of the linear discriminant analysis includes utilizing a genetic algorithm to select the subset of the partial cross correlations.

77. The method of claim 67, further comprising:

applying a thresholding function to the estimates of strength and sign of signaling between the groups of the multiplicity of sensors to produce the dynamic model.

78. The method of claim 67, wherein the comparing to produce the classification includes applying a classification function corresponding to a template of the plurality of templates to the estimates of strength and sign of signaling between the groups of the multiplicity of sensors.

79. The method of claim 64, further comprising:

associating, by the data processing system, the dynamic model with at least one record representing subject treatment administration history.

80. The method of claim 64, wherein the comparing at least the portion of the dynamic model with the plurality of templates produces an indicator of degree of correspondence to the at least one of the plurality of templates.

81. The method of claim 64, wherein each of the templates represents only statistically significant temporal measures among neural populations relevant to the given brain condition;

and

wherein the comparing at least the portion of the dynamic model with the plurality of templates includes comparing a different subset of the dynamic model with each template.

82. The method of claim 64, further comprising:

creating each of the templates wherein each template represents a combination of selected subsets of temporal measures among neural populations of a plurality of different subjects.

83. The method of claim 64, wherein the comparing at least the portion of the dynamic model with the plurality of templates produces the classification of the neurophysiologic activity of the subject such that it represents a first correspondence of the dynamic model with a first template of the plurality of templates and a second correspondence of the dynamic model with a second template of the plurality of templates that is different from the first template.

84. The method of claim 64, wherein the processing the set of subject data to obtain the dynamic model includes generating a dynamic neural network representing groupings of synchronous neural signaling detected by the multiplicity of sensors.

85. The method of claim 84, wherein the generating of the dynamic neural network includes representing groupings of simultaneous neural signaling that include pairwise groupings of neural signaling defined by synaptic activity in different brain locations occurring within about 1 ms.

86. The method of claim 64, further comprising:

updating at least one of the plurality of templates based on the classification.

87. The method of claim 64, wherein the maintaining of the set of templates includes maintaining templates that each correspond to at least one type of neural activity selected from the group consisting of:

a normal condition, Alzheimer'"'"'s Disease, pre-dementia syndrome, mild cognitive impairment, schizophrenia, Sjö

gren'"'"'s Syndrome, alcoholism, alcohol impairment, fetal alcohol syndrome, multiple sclerosis, Parkinson'"'"'s Disease, bipolar disorder, traumatic brain injury, depression, autoimmune disorder, a neurodegenerative disorder, pain, a disease affecting the central nervous system, or any combination thereof.

88. The method of claim 64, further comprising:

obtaining a magnetoencephalogram that includes the set of subject data.

89. The method of claim 88, wherein the obtaining of the magnetoencephalogram includes sampling data with a sampling interval of at most 1 ms.

90. A method for automatically obtaining a neurophysiologic assessment of structural or neurochemical brain pathologies utilizing a data processing system, the method comprising:

obtaining a set of brain data representing a time series of neurophysiologic activity acquired by each of a multiplicity of spatially distributed sensors arranged to detect neural signaling of a brain of a subject;

processing the set of brain data to obtain a dynamic brain model that represents statistically-independent temporal measures among neural populations in the subject'"'"'s brain; and

analyzing the dynamic brain model to obtain the neurophysiologic assessment of the brain.

91. The method of claim 90, wherein the processing of the set of brain data to obtain the statistically-independent temporal measures includes computing a set of partial cross correlations among groupings of different time series within the set of brain data, such that the dynamic brain model represents interactions between neural populations of the brain occurring close in time.

92. The method of claim 91, wherein the processing of the set of brain data includes obtaining the dynamic brain model based on the set of partial cross correlations for which the groupings of different time series are pair-wise groupings of the time series corresponding to all pairs of the sensors, such that the set of partial cross correlations are estimates of strength and sign of signaling between pairs of the multiplicity of sensors representing pairwise interactions among the neural populations.

93. The method of claim 90, wherein the analyzing of the dynamic brain model includes:

comparing at least a portion of the dynamic brain model with at least one different model of neural activity such that a result of the comparison is indicative of a neurophysiologic condition of the brain.

94. The method of claim 93, wherein the set of brain data is taken at a first time;

and

wherein the method further comprises;

obtaining another set of brain data at a second time that is different from the first time;

obtaining the at least one different model of neural activity based on processing of the other set of brain data, wherein the other set of brain data corresponds to the same brain; and

wherein analyzing of the dynamic brain model includes detecting a potential change in neurophysiology of the brain between the first time and the second time based on the comparison.

95. The method of claim 94, wherein analyzing of the dynamic brain model produces a result that is indicative of a degree of the potential change in neurophysiology of the brain.

96. The method of claim 93, further comprising:

obtaining another set of brain data from at least one other brain;

obtaining the at least one different model of neural activity based on processing of the other set of brain data; and

wherein the analyzing of the dynamic brain model includes detecting a potential difference in neurophysiology between the brain and the at least one other brain.

97. The method of claim 96, wherein analyzing of the dynamic brain model produces a result that is indicative of a degree of the potential difference in neurophysiology between the brain and the at least one other brain.

98. The method of claim 93, further comprising:

creating a neurophysiologic condition template based on the at least one different model of neural activity, wherein selected subsets of dynamic brain models are represented based on sets of partial cross correlations among groupings of different time series within each of a plurality of sets of brain data corresponding to a given brain condition; and

wherein the analyzing of the dynamic brain model includes calculating a degree with which the brain corresponds to the given brain condition.

99. The method of claim 93, wherein the comparing of a least the portion of the dynamic brain model with the at least one different model includes comparing the dynamic brain model with a plurality of different templates, including:

a first template that represents selected subsets of dynamic brain models based on sets of partial cross correlations among groupings of different time series within each of a plurality of sets of brain data corresponding to a first brain condition; and

a second template that represents selected subsets of dynamic brain models based on sets of partial cross correlations among groupings of different time series within each of a plurality of sets of brain data corresponding to a second brain condition different from the first brain condition; and

wherein the result of the comparison is indicative of a degree with which the brain corresponds with the first brain condition and with the second brain condition.

100. The method of claim 90, wherein the analyzing of the dynamic brain model to obtain the neurophysiologic assessment of the brain includes obtaining an assessment selected from the group consisting of:

a normal condition, Alzheimer'"'"'s Disease, pre-dementia syndrome, mild cognitive impairment, schizophrenia, Sjö

gren'"'"'s Syndrome, alcoholism, alcohol impairment, fetal alcohol syndrome, multiple sclerosis, Parkinson'"'"'s Disease, bipolar disorder, traumatic brain injury, depression, autoimmune disorder, a neurodegenerative disorder, pain, a disease affecting the central nervous system, or any combination thereof.

101. The method of claim 90, further comprising:

associating the dynamic brain model with at least one record representing administration of at least one therapy potentially affecting the brain.

102. The method of claim 90, wherein the obtaining of the brain data includes obtaining data sampled at a temporal resolution of about 1 ms while the brain is in an idling state while receiving a static visual stimulus.

103. The method of claim 90, further comprising:

processing the brain data to produce a prewhitened time series having a characteristic of stationarity of mean, variance, and autocorrelation.

104. The method of claim 103, further comprising:

applying an autoregressive integrative moving average-based algorithm to the brain data to produce the prewhitened time series.

105. The method of claim 90, further comprising:

performing linear discriminant analysis on the statistically-independent temporal measures among neural populations in the subject'"'"'s brain.

106. A method for facilitating automatically obtaining a neurophysiologic assessment of structural or neurochemical brain pathologies utilizing a data processing system, the method comprising:

providing instructions to;

obtain a set of brain data representing a time series of neurophysiologic activity acquired by each of a multiplicity of spatially distributed sensors arranged to detect neural signaling of a brain of a subject; and

provide the set of brain data to the data processing system; and

receiving a neurophysiologic assessment of the brain of the subject, the neurophysiologic assessment based upon processing the set of brain data with the data processing system that develops a dynamic brain model based on statistically-independent temporal measures among neural populations in the subject'"'"'s brain, wherein the dynamic brain model represents interactions between neural populations of the brain occurring close in time, and analyze the dynamic brain model to obtain the neurophysiologic assessment of the brain.

107. A method for facilitating automatically obtaining a neurophysiologic assessment of structural or neurochemical brain pathologies utilizing a data processing system, the method comprising:

providing instructions to obtain a set of brain data representing a time series of neurophysiologic activity acquired by each of a multiplicity of spatially distributed sensors arranged to detect neural signaling of a brain of a subject; and

using the data processing system to;

process the set of brain data to obtain a dynamic brain model based on statistically-independent temporal measures among neural populations in the subject'"'"'s brain, wherein the dynamic brain model represents interactions between neural populations of the brain occurring close in time; and

analyze the dynamic brain model to obtain the neurophysiologic assessment of the brain.

108. A computer-readable medium comprising instructions that are adapted to cause a computer system to:

receive a set of subject data representing a time series of neurophysiologic activity acquired by each of a multiplicity of spatially distributed sensors arranged to detect neural signaling in a brain of a subject;

process the set of subject data to obtain a dynamic brain model that represents statistically-independent temporal measures among neural populations in the brain of the subject; and

analyze the dynamic brain model to estimate a neurophysiologic condition of the subject.

109. A system for analyzing neurophysiologic activity of a subject, the system comprising:

a data source that obtains a set of subject data representing a time series of neurophysiologic activity acquired by each of a multiplicity of spatially distributed sensors arranged to detect neural signaling in the subject; and

a processor communicatively coupled to the data source and programmed to;

process the set of subject data to obtain a dynamic brain model that represents statistically-independent temporal measures among neural populations in the subject'"'"'s brain; and

analyze the dynamic brain model to estimate a neurophysiologic condition of the subject.