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longitudinal gPPI

October 15, 2020, 9:35 am
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Dear Alfonso,

I'm trying to run a gPPI analysis for 2 groups of patients (drug and placebo group) who scanned two-times (pre/post), each time point has 2 runs and 4 conditions. briefly, it should be a 2(group)-by-2(time point)-by-4(conditions) design.

I preprocessed the pretreatment and posttreatment data with CONN separatly, within which i defined 2 sessions per subject for 2 runs and added the corresponding functional and anatomical images. Everything works well, i got the first level beta map for each patient during each time point and each condition. 

Then i realized that i could not preform the second-level analysis (group-by-time interaction, main effect analysis) with CONN, since i processed the 2 time point data separately.  I then used SPM 12 to do the second-level analysis; i performed a 2(group)-by-2(time point) full factorial design using the beta maps from condition 2 (the most interested condition) derived from CONN to examine the treatment effect on functional connectivity. Finally, i got significant interaction effect and main effec of group after FWE correction.

I wanted to know if it was correct to preprocess the 2 time point data separately and then perform the second level analysis with SPM like i did above? If i was wrong, what should i do?

Best wishes,
Lu
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RE: 7T HCP dataset--1000s of ART outliers?

October 15, 2020, 1:03 pm
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Dear Alfonso,

I have taken your suggestion and added a constant to all functional volumes.

As a test, I entered the used the conn batch script to test this on n=4 before my full sample (n=137).

The QA_NORM_functional .jpg looks blown out (brain looks all-white, see attached image), is this because of how CONN scales the image intensity for these plots and adding a constant of 10000 has blown it out of it's normal scale so it just looks white but the underlying data are still intact? 

Other QA metrics look ok.

Please find the attached file containing my .m script and QA plots.

If the distribution of the r-connectivity values looks okay and I, for example, can see the DMN when I seed the PCC, is it safe to assume the functional registration QA plots are just "blown out" because of the constant I added?

Thank you!
Sarah
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RE: Concatenated files set up error mismatch between first level covariate dimensions and scans

October 15, 2020, 1:49 pm
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Hi Alfonso,
could you please help (see below)?
Thanks,
Best,
Amy
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"Subject does not have scan" error but subject has scan

October 16, 2020, 10:16 am
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Hello,

I made a few changes in SetUp and updated my project but received the following error:

ERROR DESCRIPTION:

ERROR: Subject 14 does not have any scan associated with condition Session2. If this is expected/correct please select 'Allow missing data' in Setup.Conditions to avoid this error message
CONN18.b
SPM12 + DEM FieldMap MEEGtools marsbar
Matlab v.2019b
project: CONN19.b
storage: 4111.5Gb available
spm @ /tmp/yassapublic/Users/SarahKark/software/spm12
conn @ /tmp/yassapublic/Users/SarahKark/software/conn

However, Subject 14 has a functional scan for session 2 and session-invariant structural.

I am not sure how to move forward.

Thank you!
Sarah Kark
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Step 5 - Importing ROI error - Mismatched number of scans and covariate datapoints

October 17, 2020, 7:15 pm
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Hi all, 

I'm a new CONN user, and I'm running into the following error when trying to complete denoising on a sample of 33 participants. 

Any pointers would be greatly appreciated. 

***ERROR***

ERROR DESCRIPTION:

Error using rex>rex_do (line 766)
mismatched number of scans and covariate datapoints ([4 1] vs. [180 13])
Error in rex (line 192)
[params.ROIdata,params.ROInames,params.ROIinfo.basis,params.ROIinfo.voxels,params.ROIinfo.files,params.ROIinfo.select,params.ROIinfo.trans]=rex_do(params,1);
Error in conn_rex (line 8)
[varargout{1:nargout}]=rex(varargin{:});
Error in conn_process (line 927)
else [data{nroi1},namesroi{nroi},params]=conn_rex(Vsourcethis,Vmask{nroi}{min(nses,nsesstemp)},'summary_measure','eigenvariate','dims',CONN_x.Setup.rois.dimensions{nroi},'conjunction_mask',mask,'level',level,'scaling',scalinglevel,'select_clusters',0,'covariates',entercovariates,'fsanatomical',fsanatomical,'output_type',outputtype,'output_rex',filenamerex,'output_folder',filepath);
Error in conn_process (line 16)
case 'setup', conn_disp(['CONN: RUNNING SETUP STEP']); conn_process([0:4,4.5,5]);
Error in conn (line 5495)
else conn_process(processname); ispending=false;
Error in conn_menumanager (line 120)
feval(CONN_MM.MENU{n0}.callback{n1}{1},CONN_MM.MENU{n0}.callback{n1}{2:end});
CONN19.c
SPM12 + DAiSS DEM FieldMap MEEGtools
Matlab v.2020a
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Trouble with slice timing correction

October 19, 2020, 1:22 am
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Hello forum,

When I try to preprocess my data, I clicked on interleaved (Siemens) for the slice timing correction in the GUI. However, when the preprocessin runs, I get the following errors:

Item 'Slice order'. Field 'val' : Item must be an array of natural numbers.
Item 'Reference Slice'. Field 'val' : Item must be an array of natural numbers.

Does anyone know how to resolve this?



Thomas van Aller
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Computing mean V2V measures within the DMN

October 19, 2020, 9:07 am
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Dear Dr. Alfonso, Conn experts,

I have computed ALFF and fALFF for a set of subjects (two groups). Now, I am interested in performing a regression analysis between the mean ALFF/fALFF within the DMN and some clinical scores that I have (I only have clinical scores for one group). What could be a good way to do this?

My proposed solution is to get the BETA_Subject*_Condition001_Measure001_Component001.nii files for the selected subjects from my firstlevel results folder and then calculate the mean (say) ALFF by using a DMN mask from Conn's networks atlas; then I can use this mean value for my regression analysis.

Is this solution valid and is there perhaps a more straightforward way of doing this?

Apologies if this question has been previously answered. I am using Conn 18b


Thank you
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problem with file prepend--c0c0T1w_restore.1.60.nii

October 19, 2020, 3:19 pm
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Hello,

I initiated batch jobs using the human connectome script and started running them last Thursday. I obtained some outputs and then they stalled, so I reinitiated today but now instead of c0T1w_restore.1.60.nii, for example, I get c0c0T1w_restore.1.60.nii (no the double c0). Similarly I am getting c1c0T1w_restore.1.60.nii vs. c1T1w_restore.1.60.nii. If I am launching batches with overwrite=Yes, why does CONN seem to working from files that were generated when I initiated pre-processing and not starting from the root file T1w_restore.1.60.nii?

Thank you!
Sarah
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RE: longitudinal gPPI

October 20, 2020, 7:08 am
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Does anybody has any suggesstion about longitudinal gPPI with CONN?
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two shorter functional runs per subject - how to transform these two in one longer scan

October 20, 2020, 4:45 pm
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Hello, I am new to Conn, I am trying to do a resting fMRI seed based connectivity study, where I want to compare pre and post to see if there is a difference after an intervention. The problem is that every subject, in every session got two runs ( so I have two runs pre and two runs post). Instead of making a long session for each subject, when the scan was done, they made two separate shorter scans pre intervention and post intervention. When I simply added two runs per session, I got this error message

ERROR: Subject 1 Session 1 first-level covariate realignment mismatched dimensions (260 rows, while functional data has 2 scans; the number of rows of a first-level covariate should equal the number of scans for this subject/session)
ERROR: Subject 1 Session 1 first-level covariate QC_timeseries mismatched dimensions (260 rows, while functional data has 2 scans; the number of rows of a first-level covariate should equal the number of scans for this subject/session)
ERROR: Subject 1 Session 1 first-level covariate scrubbing mismatched dimensions (260 rows, while functional data has 2 scans; the number of rows of a first-level covariate should equal the number of scans for this subject/session)
ERROR: Subject 1 Session 2 first-level covariate realignment mismatched dimensions (260 rows, while functional data has 2 scans; the number of rows of a first-level covariate should equal the number of scans for this subject/session)
ERROR: Subject 1 Session 2 first-level covariate QC_timeseries mismatched dimensions (260 rows, while functional data has 2 scans; the number of rows of a first-level covariate should equal the number of scans for this subject/session)
ERROR: Subject 1 Session 2 first-level covariate scrubbing mismatched dimensions (260 rows, while functional data has 2 scans; the number of rows of a first-level covariate should equal the number of scans for this subject/session)
ERROR: Subject 1 does not have any condition associated with data from session 2. If this is expected/correct please select 'Allow missing data' in Setup.Conditions to avoid this error message

I think I needed a way of combining this two runs pre intervention and then combine this two runs post intervention, but I have no idea how to do it. Or have someone else other idea?

thank you,
Marco Echevarria
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Batch Denoising in single-subject space

October 21, 2020, 6:01 am
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Hi Alfonso

I am trying to do a batch to pre-process resting state data in single-subject functional space. The pre-processing should include some spatial pre-processing steps and some denoising steps.
I have written the following code, but it seems to me that denoising is not done in the single-subject functional space; voxel dimensions coincide, but the output file niftiDATA_Subject001_Condition000.nii is shifted, compared to the smoothed rs fMRI image (output of spatial pre-processing step)
Is it related with segmentation of WM and CSF on T1 and aCompCor?
Thanks for any help 
Paola


BATCH.Setup.isnew=1;

BATCH.filename=fullfile(wd,s1);
BATCH.Setup.functionals{1}{1}=fullfile(wd,s,'resting','resting.nii');
BATCH.Setup.structurals{1}{1}=fullfile(wd,s,'mpr.nii');
BATCH.Setup.RT=1.567
BATCH.Setup.preprocessing.FWHM=6;
BATCH.Setup.conditions.names={'rest'};
BATCH.Setup.conditions.onsets{1}{1}{1}=[0];
BATCH.Setup.conditions.durations{1}{1}{1}=[inf];
BATCH.Setup.analyses=[1,2,3,4];
BATCH.Setup.outputfiles=[0,1,0,0,0,0];
BATCH.Setup.overwrite='Yes';
BATCH.Setup.voxelresolution=3;
BATCH.Setup.preprocessing.steps={'functional_realign&unwarp','functional_center','functional_art','structural_center','functional_coregister_affine','structural_segment','functional_smooth'};
BATCH.Setup.done=1;
BATCH.Denoising.filter=[0.01, 0.1];
BATCH.Denoising.despike=0;
BATCH.Denoising.detrend=1;
BATCH.Denoising.regbp=1;
BATCH.Denoising.done=1;
BATCH.Denoising.overwrite='Yes';
BATCH.Preprocessing.done=1;
conn_batch(BATCH);
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Strange FC and QC_MeanMotion plot

October 21, 2020, 10:18 am
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Hello all,

I am working with a small dataset (n=17) with a few noisy participants, but they are an incredibly special population so I am working to keep as many as possible.

I used liberal scrubbing thresholds and ended up with a bizarre looking FC and QC-MeanMotion de-noising plot--with an almost tent-like distribution with extreme -1 and +1 values. The distribution of r-conn values for each participant look okay.

I am curious about anyone's thought about the attached plot and if it would still be valid to move forward.

Many thanks!
Sarah
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Harvard-Oxford atlas in CONN matches input structural and functional data in MNI space?

October 21, 2020, 7:24 pm
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Dear Alfonso and other CONN experts,
I have a simple question. I imported structural and functional MRI files that have been pre-processed by Fmriprep into CONN , all input structural and functional files are at MNI template space. But I noticed the default atlas in CONN is Harvard-Oxford atlas, not MNI atlas. I was wondering if I need to insert MNI atlas into CONN to match my input data, or the default Harvard-Oxford atlas could match the MNI sapce in my input data?
Thank you in advance!
Xinyuan
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How to get ROI BOLD signal(time series) after denoising

October 22, 2020, 3:12 am
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Hi! 

I'm always grateful for making such a great tool for conn team and NITRC.

I recently need to learn how to get each ROI of BOLD signal (time series) using conn.

In the interface, I think there will be information about earch ROI of BOLD signal somewhere, but I can't find.

I would like to know the process if I need additional steps to save this information. And I want to know where the information related to this is stored.

Thank you for your kind answer.
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RE: Step 5 - Importing ROI error - Mismatched number of scans and covariate datapoints

October 22, 2020, 9:02 am
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[color=#000000]Hi,[/color]

[color=#000000]I've just had the same error.[/color]

Could it relate to how ROIs are specified? For each ROI, there's an option to choose which dataset from which to 'Compute average timeseries' (this option is located on the right-hand side of the conn window). I noticed that this was set to 'secondary dataset #1 (fmap)'. By contrast, when I ran a previous analysis that did work, this was instead set to 'secondary dataset #1 (unsmoothed volumes)' - i.e. for the analysis that worked, this option pointed to the functional files rather than the fmap files!

I've now tried to switch to a different secondary dataset (containing functional files) and skipped straight to step 5. I've not had this error and it is processing, so for now at least it seems to be working!

Let me know if this works for you.

Madeleine

[i]Originally posted by b shar:[/i][quote]Hi all, 

I'm a new CONN user, and I'm running into the following error when trying to complete denoising on a sample of 33 participants. 

Any pointers would be greatly appreciated. 

***ERROR***

ERROR DESCRIPTION:

Error using rex>rex_do (line 766)
mismatched number of scans and covariate datapoints ([4 1] vs. [180 13])
Error in rex (line 192)
[params.ROIdata,params.ROInames,params.ROIinfo.basis,params.ROIinfo.voxels,params.ROIinfo.files,params.ROIinfo.select,params.ROIinfo.trans]=rex_do(params,1);
Error in conn_rex (line 8)
[varargout{1:nargout}]=rex(varargin{:});
Error in conn_process (line 927)
else [data{nroi1},namesroi{nroi},params]=conn_rex(Vsourcethis,Vmask{nroi}{min(nses,nsesstemp)},'summary_measure','eigenvariate','dims',CONN_x.Setup.rois.dimensions{nroi},'conjunction_mask',mask,'level',level,'scaling',scalinglevel,'select_clusters',0,'covariates',entercovariates,'fsanatomical',fsanatomical,'output_type',outputtype,'output_rex',filenamerex,'output_folder',filepath);
Error in conn_process (line 16)
case 'setup', conn_disp(['CONN: RUNNING SETUP STEP']); conn_process([0:4,4.5,5]);
Error in conn (line 5495)
else conn_process(processname); ispending=false;
Error in conn_menumanager (line 120)
feval(CONN_MM.MENU{n0}.callback{n1}{1},CONN_MM.MENU{n0}.callback{n1}{2:end});
CONN19.c
SPM12 + DAiSS DEM FieldMap MEEGtools
Matlab v.2020a[/quote]
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Conn Sub band frequency

October 22, 2020, 10:26 am
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Hi Conn Experts,
First of all thank you to the team for making such a wonderful toolbox. 
I have few queries regarding denoising and frequency decomposition. 

I ran Conn analysis to check for any frequency dependent effects on  a particular patient group ( with filter bank n =4, which created four  additional frequency bands in the Condition set up After analyzing the results, I could see there are more newer and useful fmri activations  seen  in each of the four conditions created than that were  seen in  the original frequency band ( default frequency 0.008Hz - 0.09Hz). Can this this be seen as an advantage and reported as a better technique compared to  
Original full band frequency. ( no decomposition). Could the frequency decomposition technique of  discrete cosine transformation used in conn be attributed to this?. Is there any article which explains the use of sub band coding using DCT in conn which  could be cited?
 
I also ran analysis with filter bank n = 3 and 5. As the conditions increases, I have more number of unique activations which could not ne seen when the original rest condition frequency alone was applied. 
I would like to know if the frequency decomposition using dct is helping to extract more physiological features (fmri activations) 

Could Frequency decomposition into equal bands by dct be reported as a better approach in our study ? Or could this be some noise or other phenomena we are not aware of?
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Conn 19c : Specify number of permutations in ROI-to-ROI analyses

October 23, 2020, 2:16 am
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Dear Conn developpers,

First, thanks for your toolbox and new release, it's a great!

I would like to know if it is possible to specify in ROI-to-ROI analyses the number of permutations for TFCE and SPC methods (through the GUI, or batch command, or even by hard-coding the value in the m files). I am using Conn v19.c.

Thanks a lot for your help,

Best regards,

Lisa
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small volume correction at 2nd level

October 26, 2020, 7:58 am
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Hi Alfonso and team,

I'm trying to run a second-level analysis using a seed-based approach, but would like to restrict my seed-to-voxel analysis to a specific mask rather than whole-brain. Is there a way to do small volume correction in the most current version of the toolbox?

Thank you,
Alex
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Error at Denoising (version 19.c)

October 26, 2020, 8:01 am
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Hi!

I am analysis on data obtained during a motor task from 9 participants. The data had been analysed before (not with CONN) so I already have SPM.mat files for each participant and I just imported them by selecting the "Convert/Import from SPM design files" option in the "Tools" menu, specifying 9 participant placeholders, and then associting with them the respective SPM.mat files. 

However, although preprocessing was completed successfully, at denoising for participant 1, I get the following error:

SOME ERRORS FOUND!: Please revise the Setup information
ERROR: Subject 1 Session 2 condition 2_3_Session2 contains onset times beyond the end of the scanning session
ERROR: Subject 1 Session 2 condition 2_10_Session2 contains onset times beyond the end of the scanning session
E:\projects\tsvet\connfMRI\conn_project05.qlog\201026003449602\node.0001201026003449602.bat error
ERROR DESCRIPTION:

Error using conn_process (line 225)
ERROR: Subject 1 Session 2 condition 2_3_Session2 contains onset times beyond the end of the scanning session
ERROR: Subject 1 Session 2 condition 2_10_Session2 contains onset times beyond the end of the scanning session
Error in conn_process (line 16)
case 'setup', conn_disp(['CONN: RUNNING SETUP STEP']); conn_process([0:4,4.5,5]);
Error in conn_jobmanager (line 798)
conn_process(job(n).fcn,job(n).args{2:end});


I looked at these conditions in run 2 and indeed the onset times of these two specific trials trials were respectively 461.17 secs with duration of 0 secs, and 459.76 secs with a duration of 1.014 secs, which is not possible as run two was only 454 secs (227 volumes with TR of 2). 

I have come across with a thread of some people having similar issues (https://www.nitrc.org/forum/forum.php?thread_id=9670&forum_id=1144). There a patch was provided but for an older version. Is there a solution for this version? 

Also, I am conducting the denoising in parallel for all 9 participants, if this is important.

Thanks,
Ivan
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interpreting scrubbing output

October 26, 2020, 2:07 pm
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I'm trying to figure out a way to quantify # volumes "scrubbed" and compare them between groups. A couple of questions:

1. Can I find this information in the "scrubbing_art_regression" .mat files?
2. From what I've found so far, the outputs are 0s and 1s or a completely blank data matrix. What do each of these indicate?


Thank you
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