Python Template Matching Script Test

The goal is to have a method for matching group functional networks to the most similar individual network, represented by sharing the most overlap in activation, and lack of activation.
To test the accuracy of this method, I have done the following:

1) Completed individual ICA runs for the NYU10 set, to be matched with group networks from the same group.
2) Tested two methods (1,2) to match individual networks to group networks
3) Created results report script to visualize results
4) Would like to talk about two methods, and best way to create a "gold standard" to evaluate script performance and methods.


Group Functional Networks

1                                    2                                       3
4                                    5                                       6
7                                    8                                       9
10                                   11                                      12
13                                   14                                      15
16                                   17                                      18
19                                   20                                      21
22                                   23                                      24
25                                   26                                      27
28                                   29                                      30


Results Reports

Method 1

1) Identify voxels in template
2) Convert to MNI coordinates
3) Look for activation in these voxels of each component to match.  When we find one:

•  add 1 to voxel_in_roi count
• take absolute value of activation, add to activation_in_roi
  

4) Set these voxels = 0 and look for remaining activation (activation in component image outside of template) .  When we find one:

• add 1 to voxel_out_roi count
• take absolute value of activation, add to activation_out_roi
  

5) Subtract average activation per voxel outside of template from average activation per voxel inside template.
6) Rank these scores in descending order to get best --> worst matches

ICA: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Method 2

1) Identify voxels in template
2) Convert to MNI coordinates
3) Look for activation in these voxels of each component to match.  When we find one:

•  add 1 to voxel_in_roi count
• Add activation to activation_in_roi
  

4) Set these voxels = 0 and look for remaining activation (activation in component image outside of template) .  When we find one:

• add 1 to voxel_out_roi count
• Add activation to activation_out_roi
  

5) Subtract average avtivation per voxel outside of template from average activation per voxel inside template.
6) Take absolute value of this difference to get ranking score, in descending order is best --> worst matches

ICA: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

** Note that there are 30 components for the group map, and only 25 for each individual, which was a mistake on my part.  I am not sure if this will influence the matching.