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petty
BIAC Staff
USA
453 Posts |
 Posted - Mar 30 2012 : 2:07:27 PM
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Hey Guys, I added something to the final step of the resting_pipeline.
There's now a generation of a graphml format file, which contains the zr_values and r_values from all the connections, as well as the time course and spacial location of the ROIs' centroids.
To make that happen there are a couple of new flags.
You may need them if not using the default AAL label file.
--corrtext is a tab delimited text file with the ROI value ( 1,2...n ) and the Name of the region:
1 DLPFC_R
2 DLPFC_L
3 FFG_R
4 FFG_L
examples can be seen in the text files located here:
/usr/local/packages/biacpython/data/
--refacpoint is the anterior commissure point in the brain in X,Y,Z coordinates. This is for a conversion and storage of the ROI centroid
If you are using the default, then you don't have to worry about those.
This graphml creation happens in the final step of the pipeline ... so if you wanted to give it a try, then the easiest thing to do is re-run the final stage.
This file is the kind of thing used to create networkx gaph theory graphs and i'm also working on a viewer function so that you can easily view results as a connectome, while being able to actually see your region names and timecourses.
more info can be seen on the wiki:
http://wiki.biac.duke.edu/biac:analysis:resting_pipeline#step_7
for now to call the newer version its just:
/usr/local/packages/biacpython/bin/resting_pipeline-beta.py
There will be a follow-up post about the viewer |
Edited by - petty on Mar 30 2012 5:16:54 PM |
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petty
BIAC Staff
USA
453 Posts |
Posted - Mar 30 2012 : 2:39:56 PM
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Update, if you want to try the viewer i made a copy here:
/usr/local/packages/biacpython/bin/beta_viewer.py
If you have any questions, suggestions or comments please let me know.
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morey
BIAC Faculty
USA
25 Posts |
Posted - May 22 2012 : 4:06:33 PM
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| I wonder if you would consider adding the scrubbing technique that is advocated by Power et al 2012; NeuroImage (Wash U group). We have implmented the scripts in my lab and can provide them for inclusion in the pipeline. I personally think the jury is still out on the precise effects of scrubbing and how exactly this ought to be accomplished. More broadly it's unclear how motion effects the results. However, when comparing groups of subjects, journal reviewers are insisting on this step. Thus for no other reason we should consider making it a step (or option) in the pipeline. |
Rajendra Morey MD |
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syam.gadde
BIAC Staff
USA
421 Posts |
Posted - May 22 2012 : 4:13:20 PM
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| I have implemented scrubbing of a sort as an option in a testing version of the resting pipeline, but using a slightly more sophisticated metric. It would be pretty easy to add Power-scrubbing as another option. It would be helpful to see what your labs' scripts are doing in case there are differences... |
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morey
BIAC Faculty
USA
25 Posts |
Posted - May 22 2012 : 4:16:09 PM
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| We'll send the "Power-scrubbing" scripts to you Syam |
Rajendra Morey MD |
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petty
BIAC Staff
USA
453 Posts |
Posted - May 22 2012 : 4:19:19 PM
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As of today the beta version with the graphml output is the released version:
to now to call the older version its just, but hopefully that won't be needed:
/usr/local/packages/biacpython/bin/resting_pipeline-prev.py
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syam.gadde
BIAC Staff
USA
421 Posts |
Posted - Jul 05 2012 : 5:01:15 PM
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The "scrubbing" methods described above have been implemented. Two thresholds are available:
- motion: using the 3 rotation and 3 translation parameters from mcflirt, this calculates, per pair of adjoining volumes, the maximum displacement due to rotation and translation of all voxels on the 50mm sphere around the center of rotation, The threshold (in mm) for this is set with --motionthreshold=THRESH.
- DVARS: using the method described in the Power et al. paper, you can specify a DVARS threshold using --dvarsthreshold=THRESH. You can specify it in BOLD units or as a percentage (with '%') of mean global brain signal.
In both cases, you can throw out neighboring points using --dvarsnumneighbors=NUM or --motionnumneighbors=NUM. You can use none, either or both scrubbing methods. If specified, the scrubbing only occurs as the last step before correlation, so the full data is used for any other step that would typically need it, like white matter/CSF/motion regression. The scrubbed data is written out to a scrubbed_*.nii.gz file, and the list of scrubbed volumes (and DVARS values, if requested) are written to text files.
See the --help message for more details. Feel free to post with any questions. |
Edited by - syam.gadde on Jul 06 2012 09:06:47 AM |
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petty
BIAC Staff
USA
453 Posts |
Posted - Jul 09 2012 : 3:40:31 PM
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the pipeline now take a T1 anatomical.
If a T1 is provided, then its reoriented and skull stripped. During normalization the functional is registered to the T1, which is in-turn normalized to the standard template. This transformation is applied to the functional. This follows the pattern that would be used in a normal feat analysis.
If no T1 is provided, then the functional is normalized directly to the standard template as before. |
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petty
BIAC Staff
USA
453 Posts |
Posted - Jan 08 2013 : 10:51:17 AM
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We ran into an issue with the pipeline and float32 data .. there was significant error with the mean and sum calculations due to the decimal precision and the fact that the functional data has a huge number of data-points
The pipeline has been updated to cast any incoming data as float64 to remove this error.
To clearly demonstrate what i mean:
#read in float32
data = nb.load('cn001_1_concat.nii.gz')
data.get_data_dtype()
dtype('float32')
d = data.get_data()
d.mean()
1075.9124773522676
#cast as float64
d = data.get_data().astype(np.float64)
d.mean()
3672.51162572165
#cast as int16
d = data.get_data().astype(np.int16)
d.mean()
3672.3072782209892
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Edited by - petty on Jan 08 2013 11:05:35 AM |
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petty
BIAC Staff
USA
453 Posts |
Posted - Jan 08 2013 : 11:18:56 AM
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to add to that update and only be more specific, the only place where it really would've caused an effect is here:
DVARS calculation ... if you were calculating by a percentage ( ie: --dvarsthreshold=0.5% ), then the mean calculation could've been off, likely causing a higher number volumes to be rejected |
Edited by - petty on Jan 08 2013 11:35:05 AM |
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ch186
New Member
20 Posts |
Posted - Jan 22 2013 : 4:27:06 PM
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I am getting a memory error on the DVARS step of the pipeline. Any idea what is causing this?
01/22/2013 04:05:59 PM calculating DVARS for: /mnt/BIAC/munin.dhe.duke.edu/Morey/Lab/TBIPTSD/scrubbing/12947/output_test/filt_12947_rest_st_mcfr_brain_norm_wmcsf.nii.gz
Traceback (most recent call last):
File "/usr/local/bin/resting_pipeline.py", line 1292, in <module>
pipeline = RestPipe()
File "/usr/local/bin/resting_pipeline.py", line 111, in __init__
self.step7()
File "/usr/local/bin/resting_pipeline.py", line 941, in step7
self.step7b()
File "/usr/local/bin/resting_pipeline.py", line 974, in step7b
timeseries = self.scrub_motion_volumes(timeseries)
File "/usr/local/bin/resting_pipeline.py", line 1112, in scrub_motion_volumes
work = work ** 2
File "/usr/lib/python2.6/site-packages/numpy-1.6.1-py2.6-linux-x86_64.egg/numpy/ma/core.py", line 3670, in __pow__
return power(self, other)
File "/usr/lib/python2.6/site-packages/numpy-1.6.1-py2.6-linux-x86_64.egg/numpy/ma/core.py", line 6012, in power
result = np.where(m, fa, umath.power(fa, fb)).view(basetype)
MemoryError |
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syam.gadde
BIAC Staff
USA
421 Posts |
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ch186
New Member
20 Posts |
Posted - Jan 22 2013 : 5:56:34 PM
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| I requested 6GB and still got the error. Using "qaact -j" the max was 3.55GB. |
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petty
BIAC Staff
USA
453 Posts |
Posted - Jan 22 2013 : 6:54:13 PM
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I ran this dataset interactively ( as you ), with 10G of ram. Not sure what your threshold was .. so you'll probably want to run it over. But it does just sound like more memory needed. Somethings don't report their usage correctly, so you can't always go by what was reported with qacct.
"01/22/2013 06:49:44 PM will "scrub" these volumes due to DVARS > 53.4295: [0, 1, 2, 3, 4, 155, 156, 157, 158, 159, 160, 161, 162] " |
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morey
BIAC Faculty
USA
25 Posts |
Posted - Mar 03 2014 : 4:12:46 PM
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My question relates to the correlation coefficients of functional connectivity in the resting state pipeline. For example each of the correlation coefficients in the 116 x 116 matrix represents the connectivity strength between nodes. Each subjects cross correlation matrix can be used to contrast between subject groups using randomise. The correlation coefficient can be positive or negative, but a negative correlation value also suggests a strong functional connection if it is close to -1.
When randomise permutes group assignment and computes the group means to generate a distribution of means, these means may not accurately reflect connectivity strengths because the negative and positive correlations would cancel out. One solution might be to take the absolute values of the correlation coefficients prior to permutation.
I noted that there is a normalized matrix but this also has negative values. I am concerned that permuting is not appropriate when numbers of opposite sign but the same magnitude signify comparable connectivity strengths. Is this a legitimate concern? |
Rajendra Morey MD |
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simon
Starting Member
USA
0 Posts |
Posted - Oct 16 2014 : 2:26:35 PM
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How do a use a different atlas than the default in step 7? i see 3 flags for the labels (corrlabel, corrtext, corrts), but not for the actual atlas itself.
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resting_pipeline-beta
