Methods and apparatus for using brain imaging to predict performance

1. A computerized system for predicting performance, the system comprising:

at least one computer processor; and

at least one computer-readable medium encoded with a plurality of instructions that, when executed by the at least one computer processor, perform a method of predicting performance of an individual, the method comprising:

receiving brain imaging data for the individual, wherein the brain imaging data comprises structural brain data;

determining first values for at least one characteristic of the structural brain data within regions of interest defined for a population of individuals having different performance levels; and

predicting based, at least in part, on the first values, a performance potential of the individual.

2. The computerized system of claim 1 , wherein the structural brain data includes first structural data characterizing white matter structure and second structural data characterizing gray matter structure.

3. The computerized system of claim 2 , wherein the first structural data characterizing white matter structure comprises diffusion tensor imaging data.

4. The computerized system of claim 1 , wherein the brain imaging data comprises physiological brain data characterizing dynamics of the brain of the individual, and wherein the method further comprises:

determining second values for at least one characteristic of the physiological brain data within the regions of interest; and

predicting, based on the second values, a current performance level of the individual.

5. The computerized system of claim 4 , wherein the method further comprises:

determining based, at least in part, on the predicted performance potential and the predicted current performance level, a performance deficit; and

outputting a training recommendation determined based, at least in part, on one or more characteristics of the performance deficit.

6. The computerized system of claim 4 , wherein the physiological brain data includes at least two types of physiological brain data selected from the group consisting of resting state functional connectivity data, cerebral blood flow data, and cerebral vascular reactivity data.

7. The computerized system of claim 1 , wherein the brain data includes at least two types of brain data, and wherein the method further comprises:

creating, a multi-channel voxel map, wherein the multi-channel voxel map associates voxels in each of the regions of interest with multiple values, wherein a first value of the multiple values corresponds to a value determined for a first type of brain data of the at least two types of brain data and a second value of the multiple values corresponds to a value determined for a second type of brain data of the at least two types of brain data.

8. The computerized system of claim 7 , wherein the at least two types of brain data includes a first type of structural brain data and a first type of physiological brain data.

9. The computerized system of claim 7 , wherein the at least two types of brain data includes a first type of structural brain data and a second type of structural brain data.

10. The computerized system of claim 1 , wherein the brain imaging data is data acquired using magnetic resonance imaging.

11. The computerized system of claim 10 , wherein the brain imaging data comprises diffusion tensor imaging data.

12. The computerized system of claim 10 , further comprising:

a magnetic resonance imaging system in communication with the at least one computer processor, wherein the magnetic resonance imaging system is configured to acquire the brain imaging data.

13. A computer-implemented method for predicting performance, the method comprising:

receiving brain imaging data for an individual, wherein the brain imaging data comprises structural brain data;

determining first values for at least one characteristic of the structural brain data within regions of interest defined for a population of individuals having different performance levels; and predicting, based on the first values, a performance potential of the individual.

14. The computer-implemented method of claim 13 , wherein the structural brain data includes first structural data characterizing white matter structure and second structural data characterizing gray matter structure.

15. The computer-implemented method of claim 14 , wherein the first structural data characterizing white matter structure comprises diffusion tensor imaging data.

16. The computer-implemented method of claim 13 , wherein the brain imaging data comprises physiological brain data characterizing dynamics of the brain of the individual, and wherein the method further comprises:

determining second values for at least one characteristic of the physiological brain data within the regions of interest; and

predicting based on the second values, a current performance level of the individual.

17. The computer-implemented method of claim 16 , wherein the method further comprises:

determining based, at least in part, on the predicted performance potential and the predicted current performance level, a performance deficit; and

outputting a training recommendation determined based, at least in part, on one or more characteristics of the performance deficit.

18. The computer-implemented method of claim 16 , wherein the physiological brain data includes at least two types of physiological brain data selected from the group consisting of resting state functional connectivity data, cerebral blood flow data, and cerebral vascular reactivity data.

19. The computer-implemented method of claim 1 , wherein the brain data includes at least two types of brain data, and wherein the method further comprises:

creating, a multi-channel voxel map, wherein the multi-channel voxel map associates voxels in each of the regions of interest with multiple values, wherein a first value of the multiple values corresponds to a value determined for a first type of brain data of the at least two types of brain data and a second value of the multiple values corresponds to a value determined for a second type of brain data of the at least two types of brain data.

20. The computer-implemented method of claim 19 , wherein the at least two types of brain data includes a first type of structural brain data and a first type of physiological brain data.