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Error after Denoising

June 15, 2021, 6:26 am
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Dear CONN experts,
I am trying to run an analysis with 45 subjects and two sessions. After running denoising I got the following error description:

ERROR DESCRIPTION:

Unrecognized field name "X1".
Error in conn (line 7347)
[CONN_h.menus.m_analyses.X,CONN_h.menus.m_analyses.select,names]=conn_designmatrix(CONN_x.Analyses(ianalysis).regressors,CONN_h.menus.m_analyses.X1,[],{nregressors,nview});
Error in conn_menumanager (line 135)
feval(CONN_MM.MENU{n0}.callback2{n1}{1},CONN_MM.MENU{n0}.callback2{n1}{2:end});
CONN20.b
SPM12 + DAiSS DEM FieldMap MEEGtools
Matlab v.2021a
project: CONN20.b
storage: 97.4Gb available


If anyone could help I would be more than grateful.
Best,
Alain
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Mixed effects model for repeated subjects?

June 15, 2021, 9:34 am
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Hello all. I have a fairly simple resting-state functional connectivity analysis that I am doing, where the contrast at the first-level analysis is just a type of stimulation being off or on, let's call these OFF and ON, which alternates several times over the course of each scan. There are several types of this stimulation, which is the group distinction, let's call them A, B, C, D, etc., that I use for second-level group analysis. There are 100 scans in total, comprising the total of A, B, C, etc.

My problem is that I have far fewer than 100 subjects, because many of them contributed multiple scans. Some only contributed one, some may have contributed two to A and one to B, some may have contributed three to C but none to any other condition, etc. How many scans and which conditions they were vary widely subject to subject.

So this sounds like it needs a mixed effects analysis, with subject coded as a random effect, correct? For simplicity in setting up the CONN analysis, I just have each scan as a new "subject" and each "subject" only has one "session". But to do this correctly, would I simply code each scan from the same subject as a new session for that subject, and keep everything else the same? Is it ok that the number of sessions is very unequal from subject to subject, and many (most) have not participated in all conditions A, B, C, etc.? I have all this batch scripted and would like to keep it that way, and I think I see how to do this from the documentation. Thanks,

Karl
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ROI-to-ROI connectivity analyses with additional atlases

June 15, 2021, 2:37 pm
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Dear CONN experts,
I am doing a ROI to ROI connectivity analysis in CONN (v2020b).
Along with the "anatomical" AAL parcellation, I would like to use other "connectivity-based" brain parcellations (Schaefer and Brainnetome atlases, in particular).
However, I could only find versions of them in the "FSL-MNI" space (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal) or the "HCP-MNI" space (https://atlas.brainnetome.org/), which, as far as I know, are slightly different from the version of the MNI space implemented in SPM12 (and CONN).

I am aware that differences are very minor, but I was thinking if this discrepancy may be relevant for my purpose (i.e. to obtain full ROI-to-ROI connectivity matrices with different atlases). If yes, are you aware of versions of these atlases in the SPM-MNI space (or of any other meaningful method to overcome this issue - e.g. registering the atlas to SPM-MNI?)?

Also, does the imported atlas ROIs have to be at 1mm-isotropic resolution (like the CONN built-in atlases) or are other resolutions (e.g. 2mm or 1.25mm isotropic) equally acceptable?

Thanks in advance for your help,
Giuseppe
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RE: confounds in preprocessing: when to include main session- or task- effects ?

June 16, 2021, 8:30 am
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quick follow-up to my question:

Alfonso had already explained it in detail in the post below

https://www.nitrc.org/forum/forum.php?thread_id=12454&forum_id=1144

sorry for creating redundancy
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Continuous subject-level covariate of interest

June 16, 2021, 2:45 pm
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Hi,

I collected skin conductance (SC) data during fMRI, and I would like to use the SC as a regressor of interest (at the individual level) in an analysis that tries to identify edges that correlate with the SC time series (at the group level). To facilitate things, I binned each subject's SC time series into TR-long bins, such that the BOLD and SC signal have the same number of time points. 
Is this something that I can do in CONN? Maybe through the Conditions tab? As in, treat the SC time series as a condition in a task?

Thanks!
Rany
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RE: Question on second level ROI-ROI FNC omnibus test

June 16, 2021, 9:56 pm
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[color=#000000]Bumping this post up![/color]

[color=#000000]Additionally, is there a way to perform a two sample t-test assuming unequal variance (i.e., Welch's test)?[/color]


[color=#000000]Regards[/color]
[color=#000000]Pravesh
[/color]

[i]Originally posted by Pravesh Parekh:[/i][quote]Dear Dr. Alfonso,

Hope you are doing well! I have a few questions regarding the omnibus tests being performed as part of the ROI-ROI FNC analyses.

[b]Background[/b]:

- Conn 20.b, SPM12 7771, MATLAB R2016a
- 39 healthy subjects, 57 patients
- task fMRI where I am currently looking at one condition which is of interest
- analyses performed in the same space as functional (which are normalized to MNI space with 3mm isotropic voxel size)
- HRF-weighted correlation based ROI-ROI connectivity using Schaefer 7 networks 100 parcels atlas
- second level ROI-ROI FNC (with standard settings)
- specifying custom grouping file where the parcels are grouped as per the 7 networks

[b]What I think is being done during FNC[/b]:
- since there are 7 ROI groups, therefore there are nchoosek(7,2) = 21 between network clusters and 7 within network clusters = 28 clusters (which is what I see in the GUI with clusters going from 1-28)
- For each of the 28 clusters, perform a multivariate GLM where Y = all pairwise connections between ROIs within a cluster, X = ones(n,1) [in case of one sample test, where n is the sample size] OR X = [[ones(N1,1); zeros(N2,1)], [zeros(N1,1); ones(N2,1)]] for a between group comparison, where N1 and N2 are sample sizes and the contrast will be specified as [1, -1]
- The omnibus test is followed by a series of t-tests for each pair of connections within the cluster

[b]Questions[/b]:
Is it correct to say that for each cluster, the omnibus test is "are there any pairs of connections within this cluster for which the mean connectivity is not zero" (for a within group test) OR "are there any pairs of connections within this cluster where the mean connectivity difference between the two groups is not zero" (for between group test)?

As a specific example, say my first cluster consists is the DMN; there are 24 ROIs in the DMN group. Therefore, there would be 276 pairs of connections within this cluster. For within group comparison for healthy subjects, the GUI shows me a F(2,37) = 696.19. If I stack up my connectivity values as a 39 x 276 DMN_HS matrix, then can the F statistics can be obtained as: [h,F,p,dof,statsname]=conn_glm(ones(39,1),DMN_HS)? Similarly, the between group omnibus degrees of freedom are (3,92).

Perhaps I am misunderstanding the omnibus tests being done here? Could you provide some clarification/insights on the omnibus test being performed and how the degrees of freedom are being calculated above? Thank you very much for your time and help.


Warm Regards
Pravesh[/quote]
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Excluding ROIs for Certain Subjects

June 17, 2021, 10:17 am
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Hi all,

I'm trying to run second level ROI-to-ROI analyses with the GUI. Some of my subjects have low signal in certain ROIs due to drop out, and although CONN has calculated first level FC values for these ROIs, I would like to exclude these problematic ROIs on a case-by-case basis in the second level analysis. Is it possible to do this in CONN without excluding the entire subject? I tried searching the forum, and didn't see this discussed yet!

Thank you in advance for the help!
Jenna
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Loading results from a complete study / using 3 conditions?

June 17, 2021, 11:22 am
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Hello,

I'm trying to load the results from a published paper that used CONN to look at how they ran their analysis (then hopefully follow their analysis steps with my own data). They have their raw fMRI and task onset data available open source and then all their resultsROI_Subject001_Condition001.mat files for their 2 conditions and 37 subjects. They don't have a conn_*.mat project file available I can load/look at. I wanted to know if there's a way I could load those 74 first level result resultsROI_Subject0**_Condition00*.mat files into CONN to view results in the GUI and their experimental set up without starting from the raw files and attempting to recreate the whole thing on my own. I also ran into a similar issue when my lab drive ran out of memory and my conn_*.mat project file got corrupted and I couldn't figure out how to load everything I had already run into a new project and ended up redoing it.

Relatedly - I'm doing all of this to try to figure out how to best handle my 3 conditions in my analysis - for each participant I have blocks of rest (just watching the task screen), control math task, and stressful math task and had been running analyses with the control, experimental, and rest conditions included in the first level gPPI conditions to be modeled and then did a [-1 1] "difference experimental > control" for the between-conditions contrast in the second level analysis. I'm not sure if I should instead be doing the [0 1 0] and "effect of experimental" for second levels. For my main analysis seed-based, the [-1 1] contrast seemed to produce logical results, but for the group ICA analysis using the [-1 1] contrast eliminated all results and I feel like that is incorrect theoretically for ICA.

Thank you,
-Rachel
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ROI to ROI second level results in Results Explorer (Cluster vs Connection thresholding)

June 17, 2021, 1:18 pm
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Hi Conn Community, 

I've been working on a gPPI analysis in conn contrasting two different conditions for some time now, and have some potentially interesting results. I've come across something that I'm curious about and was hoping for some input. 

When I look at the ROI to ROI connections (I am looking at Schaefer parcellations in 3 networks specifically), I have found some sig. and interesting clusters using TFCE correction in the Results Explorer. The problem is that while all of the clusters are very significant, if I expand the statistics, most of the connections themselves are not (they are FDR corrected around p=0.05 and a bit higher in the results explorer). Are the sig. clusters themselves enough to draw conclusions or the connections also need to be sig. ? I thought TFCE in the results explorer would also correct the edges and show only sig. connections as well but if the table is correct then it does not. 

Please advise if possible and thanks again.

Best, 
Kylie I.
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Interpreting multiple regression

June 17, 2021, 1:42 pm
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Hello!

If I run a joint-regression using 3 covariate (X Y and Z) and find a significant interaction between right accumbens and the right angular gyrus would the following statement be correct.

"We performed a multiple regression analyses to explore the relationship between X, Y ,Z and and functional connectivity with the Accumbens r. Results revealed a significant interaction between X, Y,  and Z predicting dynamic resting state functional connectivity between the right accumbers and the right angular gyrus , F(#,##)=#.##, p=#.##."

Thank you for your time,

Matt G
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RE: Error using conn_importcondition (line 199)

June 18, 2021, 2:33 am
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Hi,

Just following up on my previous questions, would anyone be able to help? I am still unable to understand this error:

[i]Error using conn_importcondition (line 199)[/i]
[i]multiple rows for condition Correct_Go subject 1 session 1[/i]

For example (or please see the file attached to my previous message), this is a sample of the csv file from the first 4 onsets of the first 3 subjects:

condition_name subject_number session_number onsets durations
Correct_Go, 1, 1, 0.01, 0
Correct_Go, 1, 1, 2.4948, 0
Correct_Go, 1, 1, 4.981, 0
Failed_NoGo, 1, 1, 7.468, 0
Correct_Go, 2, 1, 0.01, 0
Correct_Go, 2, 1, 2.4948, 0
Correct_Go, 2, 1, 4.981, 0
Correct_NoGo, 2, 1, 7.468, 0
Correct_Go, 3, 1, 0.01, 0
Correct_Go, 3, 1, 2.4948, 0
Correct_Go, 3, 1, 4.981, 0
Correct_NoGo, 3, 1, 7.468, 0

Anything wrong with this format?

Many Thanks,
Arthur
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RE: v20b Preprocessing stuck at ART

June 18, 2021, 5:08 am
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[color=#000000]Hi,[/color]
I had the exact same issue and the patch fixed it.

Thank you so much!
Giuseppe


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

[color=#000000]Thanks for the detailed feedback, my guess is that the procedure may have got stuck trying to delete those files and likely waiting for user-confirmation as some of those may have been marked as read-only. [/color][color=#000000]Could you please [/color]try the attached patch and let me know if that seems to fix this issue? (this patch is for release 20b, to install it simply copy this file to your conn distribution folder overwriting the file with the same name there). 

Thanks again!
Alfonso
[color=#000000] 
[/color][i]Originally posted by Ryan Daley:[/i][quote]Hi Alfonso,

After receiving your response, I went to cancel a job that was running by hitting "ctl + c". This provided the following MATLAB error message:

"Operation terminated by user during [u][b]dos[/b][/u] ([u]line 67[/u])

Error using close
Interrupt while evaluating Figure CloseRequestFcn."

The program then went on to perform Direct Segmentation & Normalization and successfully ran the rest of the preprocessing pipeline. All of the files were present that I would have expected in the output.  However, there were also 6 files in the output that I did not expect to see including the following:

art_mask_temporalfile20210409094858825.mat
art_mask_temporalfile202104090948588251.mat
art_mask_temporalfile202104090948588252.mat
art_mask_temporalfile202104090948588253.mat
art_mask_temporalfile202104090948588254.mat
art_mask_temporalfile202104090948588255.mat

I had never tried to close out a job this way before, because I had let the walltime expire on previous jobs.

That said, it seems odd that I would need to cancel a process in order for the pipeline to continue running.

Also, it doesn't appear as though a qloq was created for this project. I'm wondering if this is because I opened a node, then opened the Conn project, and then "ran locally" within the node.

Thanks,
Ryan[/quote][/quote]
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RE: ROI-to-ROI connectivity analyses with additional atlases

June 18, 2021, 6:27 am
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[color=#000000]Dear Giuseppe,[/color]

[color=#000000]In practice those differences are often considered sufficiently small not to be worrisome, perhaps mainly because there are many other potential sources of differences (e.g. depending on details of acquisition, details of your sample, etc.) that can be reasonably expected to produce similar or larger differences between the results of normalization in your sample from the results of normalization in the data used to generate the atlas. That said, one possible/reasonable approach to try to minimize those differences would be to normalize in SPM a reference anatomical image in the atlas space (e.g. MNI152 NLIN 6th generation for FSL?) and then apply the same normalization transformation to your atlas file (e.g. using nearest-neighbor resampling to avoid breaking the structure of the data labels) in order to bring it to the same MNI-space as your data (e.g from FSL-MNI to SPM-MNI).[/color]

[color=#000000]And regarding your question about resolution, atlases and ROI files can be saved in any resolution, as long as they are properly co-registered to the functional data (this information is stored in the nifti header files, encoding the transformation between voxel coordinates and "world" coordinates) the details of voxel-size, image bounding box, etc. of your files can be arbitrary.[/color]

[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso[/color]

[i]Originally posted by Giuseppe Pontillo:[/i][quote]Dear CONN experts,
I am doing a ROI to ROI connectivity analysis in CONN (v2020b).
Along with the "anatomical" AAL parcellation, I would like to use other "connectivity-based" brain parcellations (Schaefer and Brainnetome atlases, in particular).
However, I could only find versions of them in the "FSL-MNI" space (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal) or the "HCP-MNI" space (https://atlas.brainnetome.org/), which, as far as I know, are slightly different from the version of the MNI space implemented in SPM12 (and CONN).

I am aware that differences are very minor, but I was thinking if this discrepancy may be relevant for my purpose (i.e. to obtain full ROI-to-ROI connectivity matrices with different atlases). If yes, are you aware of versions of these atlases in the SPM-MNI space (or of any other meaningful method to overcome this issue - e.g. registering the atlas to SPM-MNI?)?

Also, does the imported atlas ROIs have to be at 1mm-isotropic resolution (like the CONN built-in atlases) or are other resolutions (e.g. 2mm or 1.25mm isotropic) equally acceptable?

Thanks in advance for your help,
Giuseppe[/quote]
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Subject-specific ROIs in CONN batch

June 18, 2021, 7:28 am
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Hi Alfonso!

I'm trying to use a CONN batch script for my analysis, but I'm not sure how to specify that my ROIs are subject-specific. I have attached the script I am using.

Thanks in advance!

Pavan
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RE: Continuous subject-level covariate of interest

June 17, 2021, 7:37 am
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[color=#000000]Thanks so much, Alfonso. This is incredibly helpful.[/color]
[color=#000000]I'll give it a try and follow up if I run into trouble.[/color]

[color=#000000]Rany[/color]

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

[color=#000000]Yes, definitely, in CONN this is called a "temporal modulation" analysis: simply enter your SC timeseries (for each subject&session) as a new first-level covariate in CONN, and then in the [i]first-level analysis [/i]tab define a new "temporal modulation" analysis and when prompted select your SC covariate as the interaction factor. That will compute for each subject and each connection the temporal association between connectivity strength and skin conductance, and of course then you can enter these measures into second-level analyses as usual. As other analyses in CONN you may use these temporal modulation analyses in the context of seed-to-voxel or ROI-to-ROI connectivity measures. [/color]

[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by Rany Abend:[/i][quote]Hi,

I collected skin conductance (SC) data during fMRI, and I would like to use the SC as a regressor of interest (at the individual level) in an analysis that tries to identify edges that correlate with the SC time series (at the group level). To facilitate things, I binned each subject's SC time series into TR-long bins, such that the BOLD and SC signal have the same number of time points. 
Is this something that I can do in CONN? Maybe through the Conditions tab? As in, treat the SC time series as a condition in a task?

Thanks!
Rany[/quote][/quote]
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false positives correction and multiple comparisons

June 20, 2021, 5:35 am
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Good Day to you all. I am relatively new to Conn. We have submitted a longitudinal study with a small cohort (16 subjects) and analysed the effect of medication after one year using seed-based analysis. We used neurocognitive score as a covariate

We found very good results with improvement in the connectivity in several networks and there paralleled the neurocognitive improvement.
Our reviewers question
Q:1: A total of 16 seed regions were examined in the seed-to-voxel analyses, thus probable false-positives should be addressed/controlled and how conn tool box accounts the multiple comparisons.
Q: 2: Were participants instructed to keep their eyes closed or open (i.e., to focus on a white cross)? Due to the known impact of probable impact on FCs with eyes opened or closed, it is desired to include it as a covariate in second-level data analysis.
I assume that the false positives were already addressed by using FDR correction and statistical inference made at the cluster-level, utilising cluster-level threshold of p(FDR) < 0.05 after applying an uncorrected voxel-threshold of p < 0.001. The multiple comparisons in CONN are already built in the model or do we have to manually perform any steps?
when all the subjects have gone through the same protocol, is there a reason to use open eyes as covariate?
I would highly appreciate your inputs.
Siva
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Subject-specific ROIs in CONN batch

June 20, 2021, 2:49 pm
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Dear Alfonso & the CONN community,

I am using batch code to conduct my analysis - my ROIs are subject-specific, but I don't know how to specify this in the script. My current script is attached. Any guidance would be greatly appreciated!

Kind Regards,
Pavan
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RE: Errors saving in version 13.o

June 20, 2021, 3:12 pm
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Thanks Chaleece, for letting us know about the solution. It saved me from a lot of confusions.
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Error overwriting data

June 21, 2021, 1:39 am
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Hi conn users!

I am trying to overwrite some results of my project, but I keep getting this error: "Could not open file '....../rp_*.txt', no such file or directory". According to the path it shows for this txt file, the path is when the file was created in an external hard drive, but now, when I have copied this project to my laptop, it tries to find a file in a directory it does not exist for obvious reasons. 

Is there any way to solve this? 

Thank you in advance!!
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RE: Mixed effects model for repeated subjects?

June 22, 2021, 6:43 pm
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[color=#000000]Hi all, can I bump this? I am just wondering how to use the "Sessions" option in the case of multiple sessions per subject, and also in general a mixed effects model with unequal N, different numbers of sessions per subject, subjects participating in different conditions, etc. Thanks,[/color][i] [/i]

Karl

[i]Originally posted by Karl Lerud:[/i][quote]Hello all. I have a fairly simple resting-state functional connectivity analysis that I am doing, where the contrast at the first-level analysis is just a type of stimulation being off or on, let's call these OFF and ON, which alternates several times over the course of each scan. There are several types of this stimulation, which is the group distinction, let's call them A, B, C, D, etc., that I use for second-level group analysis. There are 100 scans in total, comprising the total of A, B, C, etc.

My problem is that I have far fewer than 100 subjects, because many of them contributed multiple scans. Some only contributed one, some may have contributed two to A and one to B, some may have contributed three to C but none to any other condition, etc. How many scans and which conditions they were vary widely subject to subject.

So this sounds like it needs a mixed effects analysis, with subject coded as a random effect, correct? For simplicity in setting up the CONN analysis, I just have each scan as a new "subject" and each "subject" only has one "session". But to do this correctly, would I simply code each scan from the same subject as a new session for that subject, and keep everything else the same? Is it ok that the number of sessions is very unequal from subject to subject, and many (most) have not participated in all conditions A, B, C, etc.? I have all this batch scripted and would like to keep it that way, and I think I see how to do this from the documentation. Thanks,

Karl[/quote]
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