See inlline replies below.

On Aug 31, 2015, at 6:37 AM, Matthew George Liptrot <[email protected]> 
wrote:

> Hi Donna,
> 
> Thanks for the info – I keep learning new functionality in wb_command! :-) 

Me, too!  Handy tool it is, and it keeps getting handier...

> However, when I run that on the CIFTI connectivity file I don’t get such 
> results (see below). 
> Are there some hidden switches to get such an output? Or maybe those metrics 
> are only given for functional data?

Sorry, you're right:  I was using it on functional data stats.  My bad.

I like Tim Coalson's suggestion:

wb_command -cifti-reduce matthew.dconn.nii MEAN matthew_mean.dscalar.nii
wb_command -file-information matthew_mean.dscalar.nii

The second command's output should give you a Inf/NaN column.  Check the 
-cifti-merge-dense inputs for NaNs the same way.

It sounded to me like you already checked the upstream inputs for NaN's, but 
using wb_command this way to reduce the dconn file to a summary stat for the 
purpose of making it easy to see how many NaN's there are in the file gives you 
a second opinion on the inputs' NaN status.

> FYI, I’m using wb_command v1.1.1 on Linux.
> 
> Cheers,
> 
> M@
> 
> $ wb_command -file-information Conn3.dconn.nii
> Name:                           Conn3.dconn.nii
> Type:                           Connectivity - Dense
> Structure:                      CortexLeft CortexRight 
> Data Size:                      33.33 Gigabytes
> Maps to Surface:                true
> Maps to Volume:                 true
> Maps with LabelTable:           false
> Maps with Palette:              true
> All Map Palettes Equal:         true
> Map Interval Units:             NIFTI_UNITS_UNKNOWN
> Number of Rows:                 91282
> Number of Columns:              91282
> Volume Dim[0]:                  91
> Volume Dim[1]:                  109
> Volume Dim[2]:                  91
> Palette Type:                   File (One for all maps)
> CIFTI Dim[0]:                   91282
> CIFTI Dim[1]:                   91282
> ALONG_ROW map type:             BRAIN_MODELS
>     Has Volume Data:            true
>     Volume Dims:                91,109,91
>     Volume Space:               -2,0,0,90;0,2,0,-126;0,0,2,-72
>     CortexLeft:                 29696 out of 32492 vertices
>     CortexRight:                29716 out of 32492 vertices
>     AccumbensLeft:              135 voxels
>     AccumbensRight:             140 voxels
>     AmygdalaLeft:               315 voxels
>     AmygdalaRight:              332 voxels
>     BrainStem:                  3472 voxels
>     CaudateLeft:                728 voxels
>     CaudateRight:               755 voxels
>     CerebellumLeft:             8709 voxels
>     CerebellumRight:            9144 voxels
>     DiencephalonVentralLeft:    706 voxels
>     DiencephalonVentralRight:   712 voxels
>     HippocampusLeft:            764 voxels
>     HippocampusRight:           795 voxels
>     PallidumLeft:               297 voxels
>     PallidumRight:              260 voxels
>     PutamenLeft:                1060 voxels
>     PutamenRight:               1010 voxels
>     ThalamusLeft:               1288 voxels
>     ThalamusRight:              1248 voxels
> ALONG_COLUMN map type:          BRAIN_MODELS
>     Has Volume Data:            true
>     Volume Dims:                91,109,91
>     Volume Space:               -2,0,0,90;0,2,0,-126;0,0,2,-72
>     CortexLeft:                 29696 out of 32492 vertices
>     CortexRight:                29716 out of 32492 vertices
>     AccumbensLeft:              135 voxels
>     AccumbensRight:             140 voxels
>     AmygdalaLeft:               315 voxels
>     AmygdalaRight:              332 voxels
>     BrainStem:                  3472 voxels
>     CaudateLeft:                728 voxels
>     CaudateRight:               755 voxels
>     CerebellumLeft:             8709 voxels
>     CerebellumRight:            9144 voxels
>     DiencephalonVentralLeft:    706 voxels
>     DiencephalonVentralRight:   712 voxels
>     HippocampusLeft:            764 voxels
>     HippocampusRight:           795 voxels
>     PallidumLeft:               297 voxels
>     PallidumRight:              260 voxels
>     PutamenLeft:                1060 voxels
>     PutamenRight:               1010 voxels
>     ThalamusLeft:               1288 voxels
>     ThalamusRight:              1248 voxels
> 
> 
> 
> 
> On 25/8/15 15:48 , "Donna Dierker" <[email protected]> wrote:
> 
> wb_command -file-information gives you stats like this:
> 
> Map     Minimum     Maximum     Mean   Sample Dev   % Positive   %Negative   
> Inf/NaN   Map Name
> 
> 
> On Aug 25, 2015, at 6:32 AM, Matthew George Liptrot 
> <[email protected]> wrote:
> 
> Hi Stam,
> Yep, workbench opens them fine. If I load in the dconn file, a surface and 
> the greyordinates.dscalar file, then I can see the connectivity maps as I 
> click different parts of the surface.
> Is there a better way to check if the values are reasonable? E.g. Would the 
> histograms report if NaNs are present?
> Good to hear about the FSL release! I guess though that this is a wb_command 
> bug (or a bug in my use of it! :-)
> Cheers,
> M@
> On 24/8/15 14:40 , "Stamatios Sotiropoulos" 
> <[email protected]> wrote:
> Hi Matthew
> No, they are not meant to be there. Can you open and display the dconn file 
> in workbench?
> FYI, there will be a new FSL release sometime this or the next week. It will 
> have some bug fixes in probtrackx2 and some new features. It may be the case 
> that this problem disappears with the new version.
> Cheers
> Stam
> On 24 Aug 2015, at 08:24, Matthew George Liptrot <[email protected]> 
> wrote:
> Hi all,
> We’ve been using the HCP DWI data to generate dense connectomes in ‘matrix3’ 
> format. Following previous advice, we are doing this in multiple stages:
> • Generate 3 separate targets (left surface, right surface, subcortical 
> voxels)
> • For each, run probtrackx2 (seed = white matter voxels, target as above) 
> multiple times using the ‘-rseed’ option (1000 streams per voxel as a 
> robustness / computational trade-off).
> • The multiple probtrackx2 runs are combined with FSL’s fdt_matrix_merge. 
> This gives us 3 FSL  .dot files.
> • Convert each of the three .dot files to .dconn using <wb_command 
> -probtrackx-dot-convert>
> • Merge the three .dconn files using <wb_command  -cifti-merge-dense> to 
> obtain the final 92K x 92K dconn matrix.
> The problem is that we get both NaN’s and zeros in the final, merged dconn 
> matrix. Presumably we should not have any NaN’s, as the numbers should just 
> represent count statistics?
> We looked into the outputs at different stages, and it seems that the NaN’s 
> only appear after Step 5.
> So our question is: are the NaN’s meant to be there, and if so, what do they 
> represent? If not, is this a bug or a mistake on our part?
> Many thanks for any help!
> M@
> --
> Matthew George Liptrot
> Department of Computer Science
> University of Copenhagen
> &
> Section for Cognitive Systems
> Department of Applied Mathematics and Computer Science
> Technical University of Denmark
> http://about.me/matthewliptrot
> _______________________________________________
> HCP-Users mailing list
> [email protected]
> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
> 
> 
> 
> 
> 
> 
> -- 
> Matthew George Liptrot
> 
> Department of Computer Science
> University of Copenhagen
> & 
> Section for Cognitive Systems
> Department of Applied Mathematics and Computer Science
> Technical University of Denmark
> 
> http://about.me/matthewliptrot
> 


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