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RE: Error During Pre-Processing Steps

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Hi Jenny,
Hope you've figured a solution out already, but in case you haven't (and in case future Mac users encounter this problem), I encountered a similar error due to Catalina's security settings, which are set to not allow any apps to run unless they're from the app store or an 'identified developer'. Usually you would get a pop-up with the permission error but there's a chance that you didn't depending on your computer's settings. Luckily, though there's no way to allow all SPM files through System Preferences, there [i]is [/i]a way to get your computer's firewall to ignore specific applications through the command line. Try opening up Terminal and running [b]spctl --add Downloads/spm12/* [/b]to get your computer to allow SPM - you also have to run this command with spm12/*/* etc all the way to spm12/*/*/*/*/*/* to make sure you get all of the subdirectories. 

You also want to make sure that SPM is in your Matlab path - instructions for this are [url=https://sites.google.com/view/conn/resources/installation]here[/url].

Depending on the size of your scans, the error could also be due to lack of space on your hard drive - for me, a ~600MB uncompressed nifti file results in as much as 7GB of CONN preprocessing/denoising output files, so unless you're only running one or two sessions or shorter fMRI scans, 18GB is likely not going to be enough. I'd recommend trying to run just one single scan through CONN to see if you can get it to work, as this shouldn't be too large for the space on your computer. 

Hope this helps!

Best,
Natalie

niftiDATA vs. d* output files - which to use for analyses?

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Hi all,
I'm a bit confused regarding the preprocessed & denoised output files, and was hoping someone might be able to clarify which is best to use for subsequent analyses? There are a number of posts on this topic, but there appear to be two different answers - some say that the denoised data is the niftiDATA*.nii file (e.g. [url=message.php?msg_id=18633]here[/url], [url=message.php?msg_id=11244]here[/url], and [url=forum.php?thread_id=10805&forum_id=1144]here[/url]), while others (e.g. [url=forum.php?thread_id=10962&forum_id=1144]here[/url] and [url=forum.php?thread_id=11298&forum_id=1144]here[/url]) point to the d*.nii file as the denoised output file. Though both are referred to as being denoised preprocessing output, the two images (at least for my data, where I have dwu* files because I skipped STC & smoothing) look quite different - I'm linking screenshots here for reference in case they're not supposed to look like this [[url=https://drive.google.com/file/d/1IvrcF5ANsrevmgTqE7sUdIi3dROyNJDQ/view?usp=sharing]niftiDATA[/url]] [[url=https://drive.google.com/file/d/18b_d-qzGcmK6ESH7sE0Ssr_2fmgIIMw2/view?usp=sharing]dwu[/url]].

Because of this, I was wondering what the differences between these two files are, and which one is best to use for subsequent analyses (or pros and cons of either one)? I'm planning on using CONN 19b for preprocessing and denoising, then doing the rest of processing/analysis with other tools.

Thank you!
Natalie

RE: How can I generate a ROI-configuration file based on ICA results?

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Thank you so much for your help!

RE: fMRIPrep import

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Hi,

I'm having the same problem with CONN not recognizing the GM, WM and CSF ROI files from fmriprep. Structural and functional images are imported without a problem.

Any solution yet?

Best wishes

Julian

fMRIprep to CONN: Different resolutions of ROI templates and BOLD data

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Hello, 

I am working with a resting state data, preprocessed in fMRIprep. The anatomical data is in MNI152 space (1mm). Functional data is in it's original resolution after resampling on the MNI space.

The data is from multiple sites and the resolution varies between them (as well as TR).

To use CONN for ROI-to-ROI analysis, as well as between-groups comparisons with a ROI template that corresponds to MNI152Lin6Asym space, would you advice to resample the template to different sizes depending on the data? The anatomical data and ROI template are in the same space.

Thank you,
Natasza

Seed to whole Brain analysis

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Hello Experts,

I'm new to the conn interface.
I am working on Resting state data and performing seed to whole brain analysis. 
I have created rois using Marsbar. However, I can't see those rois in the 2nd level analysis. Though I have run all the processes after importing those rois in the setup tab. 
I only want to look at the functional changes at those rois.
Could you let me know how can I do that?

Thanks!

functional_label

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Hi all,

I am writing a script for my resting-state data preprocessing based on the NYU script. So far everything works well on my first trial using the default pipeline but I am now dropping the STC as we have a short TR=1.5s (multiband 3). I know that this TR is probably borderline candidate for correction (with 15-20% signal drop in worst cases when uncorrected) but I was advised that the signal I gain through STC will probably be negligible once the functional data is smoothed. So I proceed by spelling out my preproc step in the setup.preprocessing.steps line.

The problem is that I am confused by the functional_label step. Do I understand correctly that the step serves to label the functional data "original data", "mni-space data" and "smoothed data" in the default pipeline? How do I assign the value if that's the case, the default value [datestr(now)] does not look like the labels above. Also, part of the confusion is that I don't understand what "secondary data" means in the glossary.

Can anyone explain? I know that the batch will run without functional_label specified, the previous CONN default pipeline didn't seem to have it when I checked past manuals. Still, I don't want to miss out on what might be a super useful step just because I don't understand it. 

Thanks in advance,
Steve

RE: Help Clarifying What QA_MaxMotion Values are CONN

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Bumping - checking in again if anyone can help.<div id="gtx-trans" style="position: absolute; left: 197px; top: 21px;">

Controlling for motion differences between two groups in a sample

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Hello CONN Forum and Alfonso,

Apologies if this has been asked before (couldn't find this topic after a thorough search).

In my ROI-ROI analysis, I have two groups in the sample - an autism diagnosis group and a control group. After removing cases with excessive outlier volumes (>50% outlier volumes), these two groups still have a significant difference (t-test) in both outlier volumes and mean motion (CONN's composite motion). I've seen only one way to control for this difference, which is Saad et al., 2013's global correlation regressor. What are some possible/recommended approaches in handling this difference in mean motion? Looking forward to everyone's answer, thank you in advance!

Sincerely,
Billy

RE: out of storage?

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[i]Originally posted by Isabelle Frosch[/i]

[quote]Just following up here. This has interfered with our work, we can't run any new second level analyses. For more context, we have about 50 subjects, 4 conditions, and are running a basic seed-to-voxel analysis. There is plenty of room to save new results on my external drive, but the CONN project has run out of room.[/quote][quote]Can you let us know what we can do to free up storage within the CONN project?
[/quote][quote]
[/quote][quote]Thank you![/quote][quote]Isabelle
[/quote][quote]
[/quote][quote]
[/quote][quote]Hi Alfonso,

I'm having issues with storage—

Without opening a specific project, conn says there is 0% storage. When I open a project, this doesn't change, and I can't run analyses or access completed analyses.

I am running conn locally on my computer and save 2 projects to an external drive that has 2 TB.

Any ideas?

Thanks!
Isabelle[/quote]

RE: reporting a bug in CONN

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Dear Alfonso,
Thank you very much for your reply. Now I know where the problem is. Atlas.txt was modified (I do not know why). I am sorry, and now the problem was solved.
Thank you again!
Xinyuan

RE: New CONN update and the matrix in the ROI to ROI \How to visualise the matrix with ROI names

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[color=#000000]Hi,[/color]

[color=#000000]I've given this a try and it does not work. I've downloaded + decompressed the supplied file and copied it's contents into the CONN folder within the MATLAB path. The results explorer changes but there is no "Matrix Display." There is a GLM display button only. I am using CONNv19c in matlab 2020a. Screenshot attached[/color]



[i]Originally posted by Alfonso Nieto-Castanon:[/i][quote][color=#000000]Hi Aser,[/color]

[color=#000000]In the attached patch I have added a new '[b]matrix display[/b]' button to the ROI-to-ROI results explorer window that will give you a bit more flexibility and options for displaying your results in terms of ROI-to-ROI connectivity matrices (this patch is for release 19c, to install it first unzip the attached file and then copy all of the resulting files to your conn distribution folder overwriting the files with the same name there)[/color]

Let me know if that works ok and/or any comments/suggestions for improvement

Best
Alfonso

[i]Originally posted by Aser A:[/i][quote]Hi all

So for the new CONN update in the second level ROI 2 ROI analysis. You can explore the results. When way of showing the result is display the connectivity using a matrix instead of the connected lines.. This is so great in terms of viewing the results.
However I have this problem: I cannot figure out the relation between the regions of the locations and the names of the regions inside the matrix.
CONN does not name the x and y of the matrix and each line by the region name.
So how can I display the region names to be meaningful and make the matrix easy to read?
Thanks

I posted this before but could not find an answer to it.[/quote][/quote]

unequal number of subjects

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I have 27 controls and 26 patients, so 53 total. When I get to the results of the second level analysis, I see n=52 - I assume one of my patients was excluded from the analysis. Is there any way to run the analysis with an unequal number of subjects? thanks

Bug to display QA plots on CONN 18.b

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Dear Alfonso,

Since last week I have had some issues to display the QA plots of my data after the preprocessing. I never had these issues before.
At first, I thought that the preprocessing was incorrect so I have rerun it (without any problem) but I still end up with a problem to display the QA plots (even though I can create the QA plots).
I get this message: 
'Error in conn_qaplotsexplore/conn_qaplotsexplore_update (line 735)
dlg.dataD=sqrt(convn(convn(sum(dlg.dataD.^2,3),conn_hanning(3)/2,'same'),conn_hanning(3)'/2,'same')./max(eps,.01*mean(temp(:))+temp));
Error while evaluating UIControl Callback.'

I saw other posts with different issues on different CONN versions since the update to CONN 19.c. Does it have anything to do with the CONN version I am using? Ideally I would like to keep the CONN 18.b version.

Any help/ thought would be greatly appreciated.

Thank you.

Kind regards,
Hugo

Adding a new subject in the middle of the sample after preprocessing

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Dear Conn users, 
I would like to ask ,how to add a new subject in the middle of the sample. After preprocessing, I accidentally deleted one subject from the primary dataset, but  unfortunately I have had my smoothed data as secondary dataset and therefore all data after that deleted subject are in fact assigned to +1 subject comparing to original data. This makes a mess in the naming of subject´s files in the sourcedata file and in GUI. I have 400 subjects in 2 sessions  in my sample  I do not want to reassign all data (functional, structural,first level covariates,ROIs WM,GM, CSF) manually or with batch. Is there any chance how to add that deleted subject back in the middle of the sample or how can I solve such a problem? Thank You. Boris

interpreting regression results

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Dear Alfonso,
First of all, thank you so much for your continued help.
I ran a regression analysis: allsubjects, behavioral [0 1], rest[1] seeds/sources Amygdala l [1], the attached screenshot is the regression result. May I ask how to interpret? I was wondering if I can only conclude: negative association between left amygdala-right superior parietal lobule and behavioral score. Or if I can also conclude: higher behavioral score is associated with lower functional connectivity? In orther words, from the regression results, do we know functional connectiviy is "high" or "low"? or do we only know "positive" "negative" association?
Many thanks!
Xinyuan

RE: Regression and bandpass filtering in fMRI

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Hi Alfonso,

Thank you so much for your reply.

That make sense. In fact, after doing RegBP I did not have any problem. But, considering that bandpassing at these very low frequency ranges ("0.002 - 0.01" and "0.012 - 0.028") removes the majority of the independent components of the signal, will the resulting output has any statistical validity? 

Thanks in advance.

Best regards,
Karel

denoising after normalization

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Hi Alfonso and other experts
I'm starting to work with rsfmri images in the CONN toolbox. I have very little experience in rsfmri analysis with CONN and in general (I'm a first year predoctoral student), so I apologize in advance if my question is absurd. In the past I worked with DPARSFA and applied the nuisance covariates regression before normalization (because some authors advise this) while in CONN I could observe the denoising is done after normalization and smoothing. Is there any data that justifies this order?

best regards and thank you very much in advance
benxamin

correlation analysis

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Dear Expert,

I kindly request you to have a look in my design and give your suggestion whether I am going in the right direction or any correction is necessary.
I have 3 groups and 3 nuisance co-variate (age, gender & education levels). First questionnaire (Q) was given for each group, and 2nd questionnaire (Q2) was given only for group 3. I entered the data (n=28/group) in the same way that I have given in example below.

Group (A)   Group (B)<span style="white-space: pre;"> </span>Group (C)<span style="white-space: pre;"> </span>age<span style="white-space: pre;"> </span>gender<span style="white-space: pre;"> </span>edu-level  AQ<span style="white-space: pre;"> </span>BQ<span style="white-space: pre;"> </span>CQ<span style="white-space: pre;"> </span>CQ2<span style="white-space: pre;"> </span>
1 <span style="white-space: pre;"> </span>0 <span style="white-space: pre;"> </span>0 <span style="white-space: pre;"> </span>36<span style="white-space: pre;"> </span>0   <span style="white-space: pre;"> </span><span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>122<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0
1 <span style="white-space: pre;"> </span>0 <span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>45<span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>2<span style="white-space: pre;"> </span>123<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0
0 <span style="white-space: pre;"> </span>1 <span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>50<span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>134<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0
0 <span style="white-space: pre;"> </span>1 <span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>40<span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>139<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0
0 <span style="white-space: pre;"> </span>0 <span style="white-space: pre;"> </span>1 <span style="white-space: pre;"> </span>35<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>2<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>134<span style="white-space: pre;"> </span>8
0 <span style="white-space: pre;"> </span>0 <span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>48<span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>1<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>0<span style="white-space: pre;"> </span>145<span style="white-space: pre;"> </span>7


The contrast for hypothesis testing

For compare groups

A>B  <span style="white-space: pre;"> </span>1 -1 0 0 0 0 

For regression analysis
Positive corl of group A with AQ <span style="white-space: pre;"> </span>0 0 0 0 0 0 1 0 0 0 
Negative corl of group A with AQ <span style="white-space: pre;"> </span>0 0 0 0 0 0 -1 0 0 0

For regression analysis of Q2 with group C.
Positive corl of group C with CQ2 <span style="white-space: pre;"> </span>0 0 0 0 0 0 0 0 0 1
Negative corl of group A with CQ2<span style="white-space: pre;"> </span> <span style="white-space: pre;"> </span>0 0 0 0 0 0 0 0 0 -1 


Thanks in advance
Ramesh

errors with SPM preprocessed files

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Dear Alfonso and CONN users,

I've been trying to get Conn working for a dataset I've been working on, using imported SPM files as a starting point. I have 46 participants, and so far I have gotten everything through the Denoising step to work for almost all of them, but there are a couple of cases that give me errors during Conn's preprocessing (when pressing 'Done' on the Setup page).

One gives the following error:
SOME ERRORS FOUND!: Please revise the Setup information
ERROR DESCRIPTION:

ERROR: Subject 3 Session 1 condition Corr contains onset times beyond the end of the scanning session
ERROR: Subject 3 Session 1 condition Prime contains onset times beyond the end of the scanning session
ERROR: Subject 3 Session 1 condition ISI contains onset times beyond the end of the scanning session
ERROR: Subject 3 Session 1 condition Resp contains onset times beyond the end of the scanning session
CONN19.c
SPM12 + DEM FieldMap MEEGtools

The other gives me the error 'File too small'.

Using SPM there is no problem with the 1st level analyses of these participants, and I don't really know how to check where it's going wrong for either of them within the Conn pipeline. Has anyone had any experience with this, or have any suggestions for what to check?

Thanks in advance.

James
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