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Loading second-level connectivity results

March 12, 2019, 7:58 pm
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Hello CONNers,

I have 2 rsfMRI groups (second level covariate in CONN pipeline) I want to view 2nd-level rsfMRI results just to see basic brain activations at rest (non-connectivity results) so that I can note down the ROI coordinates for group differences (group_A > group_B and group_B > group_A). I particularly aim to define them as new ROIs under CONN setup and then finally compute ROI-to-ROI connectivity calculations for these new ROIs. The predefined set of ROIs (e.g four DMN ROIs in CONN) don't seem to show group difference connectivity patterns even though seed to voxel analysis appears to show some patterns here and there. My questions are;

1- How can I view the group level resting-state fMRI activations in CONN/SPM? (e.g. group_A, group_B, group_A > group_B and group_B > group_A)
2- Where are the second level SPM.Mat files located?
3- Is there any tool that I can use to name the ROI based on ROI coordinates?
4- Any suggestions on best way to do all this?

Thanks & Regards,
Dilip
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Is it possible to use NBS in graph theoretical analysis in Conn?

March 13, 2019, 9:37 am
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Hi, 

I am using the NBS tool in the Roi to ROI analysis, and so far it is being really useful in a small sample size in which a lot of ROIs are used. I wonder if there is any way to employ this method of correction in the graph theoretical analysis tool.

Thanks in advance,
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RE: CONN compatibility with BIDS and fmriprep output

March 13, 2019, 3:29 pm
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Hi Karolina,

What did you do to recreate the ART outlier detection process outside of CONN? I am also using fmriprep to preprocessing and CONN for resting state connectivity analysis. 

Alfonso, would it be invalid to run ART on the output of fmriprep (which is co-registered to the nonlinear Asymmetric 2009 MNI space?)

Looking forward to increase ease of moving between fmriprep and CONN as well!

Best,
- Harris
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RE: Error importing results to CONN project

March 14, 2019, 1:52 am
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Hi Alfonso,

do you have any idea what could be the reason for this error?

Thanks,
Boris
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HPC Parallelization Options Not Showing Up in CONN18b

March 15, 2019, 12:03 pm
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Hi,

We are just starting to use CONN18b and are having trouble getting our saved SLURM Configuration to show up when we try to do different steps, such as Preprocessing and Done on the setup page. I've attached a picture. We recreated the SLURM settings so it's definitely there under the HCP options -> Configurations.

Thanks!
Jamie
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Trying to obtain a Connectivity Matrix

March 15, 2019, 1:32 pm
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Hi,

On the 'ROI-to-ROI explore gui', I need to select 'network of source ROIs' but the only option that shows up to me is 'Targets are source ROIs only'. And I can't select the following steps to try to display a ROI-to-ROI connectivity matrix.

Have someone had this problem before? Can someone help?

Thanks!
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Parametric modulator

March 18, 2019, 4:58 am
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Dear experts,

I am new to this forum and have not used the CONN toolbox before. I would like to run a PPI analysis with a parametric modulator, a triel-by-trial rating. I got two questions related to this:

1. Is it possible to run such an analysis using CONN?
2. At which analysis step should I include the parametric modulator. Should I run a standard parametric analysis  in SPM using pmod and then extract the time series from the parametric regressor to use it in the PPI?


Thanks a lot in advance.

Best wishes,
Carmen
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preprocessing error in CONN requiring manual realignment

March 18, 2019, 9:43 am
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Is there any documentation for how to do manual preprocessing of the data? I used the pipeline, and that worked for the majority of my participants, however after various QA measures it's clear that something went wrong for several of my participants (I'm working with older brains so this might be part of the issue). For example, when assessing the single slice for all subjects montage for the functional data the brains are not showing the same slice across subjects (in fact some look coronal while others look transverse), as well as issues with gray and white matter segmentation. 

Thanks so much!
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Exporting CONN 3D model with blobs e.g. to make a GIF

March 19, 2019, 6:53 am
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Dear Alfonso and others, 

First of all, thank you very much for the great tool that CONN is - so intuitive and versatile!

Among the functions I appreciate a lot about the latest versions, is the 3D rendering that is possible. Now, with the way that we publish continuously changing, I was wondering whether there is a way to extract the 3D model, with semi-transparent surfaces, my regions of FC and my ROI on it from the viewer or directly from MATLAB? In an ideal case, I would like to make a rotatable 3D rendering for use outside of CONN - be that in an actual viewer or just to make a GIF of it. 

Any help or hints from all of you are much appreciated.
All the best,
Lys
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Can I use ROI-level covariates?

March 19, 2019, 8:36 am
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Hi Alfonso,

I use patient-specific ROIs (stroke lesions), and I want to compare ROIs from patients with good versus those with poor outcome. Patients can have multiple lesions/ROIs, however (e.g. if their stroke affected different parts of the same vascular territory). Is there a way to indicate that different ROIs come from the same patient, e.g. in the between-sources contrast? The reason for this is that ROI-networks from one patient will not be independent, and I think I should account for this to work with the correct degrees of freedom.

Thank you!
Eugenio
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Making graphs with connectivity values and clinical correlations

March 19, 2019, 8:47 am
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Dear Alfonso and all

Hope my message finds you well, I recently have been investigating the impact of pain on connectivity in a cohort of patients using multiple linear regression models in CONN (i.e. 0 0 0 1 , 1 being pain as interest)

Thankfully they also show significant correlations with connectivity, however I wish to plot these values in a graph (for visual presentation) so I can make it more intuitive for the reader, thus my question is:

How do I do this in CONN (if it is possible)? and if it is not possible do I simply extract the connectivity values from the contrast I made in the GUI and simply do a scatterplot with the clinical data ? (for example connectivity strength on the Y axis and clinical data on the X axis)

I have tried this before and it does not show a nice correlation in the graph, thus I was wondering do I need to normalise the clinical data first as it does in the GLM model? 

If anyone has experienced this please let me know and I look forward to your feedback and help!

Many thanks for your time

Vincent
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Differences in group size--compare within-group variance?

March 19, 2019, 6:25 pm
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Hello Alfonso and others,
I've run a few second-level ROI-to-ROI analyses (mostly 2-sample t-tests) involving comparisons between different combinations of four participant groups with different sizes (n = 9, 10, 17, and 17). A reviewer has asked us to address the possibility that some results might be due to differences in sample size, rather than (or in addition to) true differences in connectivity.

For example, when we compare groups 1 and 4 (n=9 vs. n=17), there are about 15 significantly different connections, but when we compare groups 3 and 4  (n=17 vs. n=17), there are about 35 significant differences. We would like to gain some insight into whether there are fewer differences between groups 1 and 4 than 3 and 4 because there is less power in the first analysis due to group 1 (n=9) being smaller than group 3 (n=17). I was thinking it might be helpful to compare the variance in the correlation coefficients for each group (since those are the data in each of the t-tests in my analyses), as this would at least let me see if we're in violation of the assumption of homogeneity of variance, but my network has more than 700 connections so it's not really feasible to try to compute those values manually. Is there some way to efficiently obtain the variances (maybe a distribution of variances across each group's network) from the CONN results just to get an idea of how things compare between groups? Or do you have any suggestions for a better/alternative way to account for sample size differences? 

Any suggestions or guidance would be very much appreciated.

Thank you,
Jeff
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Calculation of FDR-corrected p-values

March 20, 2019, 8:35 am
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Hi there,

I'm trying to calculate FDR-corrected p-values from the p field in my ROI.mat file. The p field in this file contains p-values which are equivalent on each side of the diagonal, as expected. However when I compute the FDR-corrected values for some reason the corrected p-values differ on each side of the diagonal (I've attached a screenshot).  

I'm using p_FDR=conn_fdr(p_uncorrected) to compute the FDR-corrected p-values.

If anybody has any thoughts on the values might be different on each side of the diagonal I would appreciate it!

Alex
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Export t-values from seed-to-voxel results

March 20, 2019, 10:23 am
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Hi everyone,

I've run a seed-to-voxel analysis and now I'm trying to export t-values. Is there a way to export them directly from conn results explorer without opening SPM for each result? 

Thank you immensely for the help,
Camilla
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Many repeated scans per subject, design question

March 20, 2019, 12:46 pm
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Hi,
I have a dataset of 8 subjects, who were each scanned on between 13 and 50 occasions. I am interested in the impact of a couple of DAYILY measures that vary between the different scan days.
At the first level, I will enter things like motion correction time series for each scan, and, if it were one subject, I would enter my DAILY variables at the second level. But how should this be done for multiple subjects?

Can I enter 8 "DAILY" variables at the second level, where each subject gets her numbers, and the rest are coded as zeros?
Or should I model the effect of DAILY in each subject in a separate GLM, and then do a third level test in SPM using the resulting con*.img maps? And if so, how do I weight for the number of scans each subject underwent?

Best
Clas
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RE: Changing data's location

March 20, 2019, 6:34 pm
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I was wanting to know this as well. I couldn't get the conn scripts to work right. I found a different way by experimenting and is simple as heck. The reason I needed to change path names, is I updated my mac operating system. Never do that unless you really need to. My external hard drive is in NTFS, and whatever came with the hard drive didn't work on the new OS. After finding a free program, it would change the name of my hard drive to the programs own version of it. iBoysoft. 

Anyway, if you use conn, you also have spm. There is a function in spm called spm_changepath.m which I have used before for spm. In this example, I was just changing every file path that had my "hard drives" name with what this new plugin to write in NTFS was creating. This assumes you changed directory to your conn base folder:

spm_changepath('myconnproject.mat', 'Benson', 'iboysoft_ntfs_disk2s2_')

Being kind of like /Volumes/Benson/Imaging_data/....... to /Volumes/iboysoft_ntfs_disk2s2_/Imaging_data/.......

It might yell at you the first time saying it can't find some .....dmat file when you first open it. I think conn is assuming you want to merge files. Just close the error. It will work from there. I just wanted to change the ROI's used at the first level step, and worked perfect all the way through getting 2nd level results.

Many Blessings,
Benson
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Multiple functional files per subject on batch script

March 20, 2019, 9:02 pm
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Dear all, 

I'm trying to script a conn batch to preprocess data with the following characteristics :
214 subjects
134 functional files (.nii) per subject
1 structural file per subject
1 session per subject


I am wondering if it is possible to load all the functional files (134) via command line, or if only one functional file per subject is expected.


Here's a portion of my script for only 2 subjects:

DATApath=pwd;
nsubjects=2;
clear FUNCTIONAL_FILE* STRUCTURAL_FILE*;
subs=dir(regexprep(DATApath,'%s.*$','*'));
subs=subs([subs.isdir]>0);
subs={subs.name};
subs=subs(cellfun(@(s)all(s>='0'&s<='9'),subs));
if isempty(nsubjects), nsubjects=numel(subs);
else subs=subs(1:nsubjects);
end

for n=1:numel(subs)
<span style="white-space: pre;"> </span>fprintf('Locating subject %s files\n',subs{n});

<span style="white-space: pre;"> </span>% files=conn_dir(fullfile(cwd,SubjectFolder{nsub},SessionSubFolder{nses},'functional*.img')); <- this is the structure I'm following

<span style="white-space: pre;"> </span>t1=conn_dir(fullfile(DATApath,subs{n},'structural','structural*.nii'));
<span style="white-space: pre;"> </span>f1=conn_dir(fullfile(DATApath,subs{n},'emotion','emotion*.nii')); % here's where I get an error saying "Pathname is too long"
<span style="white-space: pre;"> </span>if isempty(dir(t1)), error('file %s not found',t1); end
<span style="white-space: pre;"> </span>if isempty(dir(f1)), error('file %s not found',f1); end

<span style="white-space: pre;"> </span>STRUCTURAL_FILE{n,1}=t1;
<span style="white-space: pre;"> </span>FUNCTIONAL_FILE{n,1}=f1;
end

nsessions=1;
fprintf('%d subjects, %d sessions\n', nsubjects, nsessions);

{...}

clear batch;
batch.filename=fullfile(TARGETpath, 'conn_emotion.mat');

batch.Setup.functionals=repmat({{}},[nsubjects,1]); % Point to functional volumes for each subject/session
for nsub=1:nsubjects,for nses=1:nsessions, batch.Setup.functionals{nsub}{nses}{1}=FUNCTIONAL_FILE{nsub,nses}; end; end;

batch.Setup.structurals=STRUCTURAL_FILE; % Point to anatomical volumes for each subject

{...}

Thank you very much!
Emiliana
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Problems batch importing l2covariates

March 21, 2019, 9:51 am
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Hello All,

I am having trouble importing second level covariates. My batch script runs fine and all the data and conditions and first level covariates will load, but not the second level covariates. 

I have 10 covariates. I created L2COV which is a 1x98 (98 subjects) cell where each cell is a 1x10 cell array of the indicators (0 or 1 for that covariate)

l2covariate_names= {'CTS','HC','L Hand','R Hand','Group E','Group K','Group P','Pain Dominant','Paresthesia Dominant', 'Equal'};

batch.Setup.l2covariates.names=l2covariate_names;
batch.Setup.l2covariates.values=L2COV;
batch.Setup.l2covariates.descrip=repmat({''},1,n_l2cov);

I tried manually loading in a covariate, saving the .mat file and then looking at it in matlab. the .Setup.l2covariates structure looks identical to the way I set up my batch file.

So why doesn't CONN load them in? 

I've also been unsuccessful in writing a text file to import through the gui. How should the text file be formatted?

- Harris
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RE: Lesion Masks

March 21, 2019, 10:56 am
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Hello,
I want to know too. I have the same problem. 
Thanks
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RE: Differences in group size--compare within-group variance?

March 21, 2019, 5:50 pm
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[color=#000000]Hi Jeffrey,[/color]

[color=#000000]This is an interesting question, and the reviewer is correct to point out that evaluating the difference in the number of significant/supra-threshold connections between two different analyses does not allow you to properly infer whether the differences between the two pairs of groups involved are similar or not. In addition, the expectation of decreased power in this sort of unbalanced designs is somewhat independent of the heteroscedasticity assumption of the GLM model or the homogeneity of variances assumption in a two-sample t-test, It is simply due to the smaller-group's average connectivity estimate having larger unknowns / standard error due to its smaller sample-size (this difference in standard errors is expected even if the within-group standard deviations are exactly the same across the two groups). Because of this I believe that a more direct way to evaluate whether in your results the difference in the number of supra-threshold connections between the two analyses is due to differences in power/sensitivity vs. differences in effect-size would be to actually compare the effect-sizes of the between-group differences/comparisons in both analyses. [/color]

[color=#000000]There are, of course, a lot of different ways to go about exploring these effect-sizes. Since the two between-group comparisons in your case involve three groups (Group1vsGroup4, and Group3vs.Group4) one relatively straightforward approach would be to perform a conjunction of a [-1 1 0] contrast and a [-1 -1 2] contrast (when selecting Group1, Group3, and Group4 in this order). The first between-subjects contrast identifies those connections where Group1 and Group3 have different connectivity, and the second contrast identifies those connections where the difference between Group1 and Group4 is different from the difference between Group3 and Group4). Those connections that appear as significant (using two-sided tests) in both results will be the ones where you can confidently say that the strength of the Group1vsGroup4 difference in connectivity differs from the strength of the Group3vsGroup4 difference in connectivity. Then simply display those between-group differences in connectivity across these same connections in order to evaluate whether in fact the Group3vsGroup4 differences appear to be larger/stronger than the Group1vsGroup4 differences, which would support your original observation regarding the number of suprathreshold connections in each individual analysis but without the potential biases due to difference in power/sensitivity between those individual analyses. [/color]

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


[i]Originally posted by Jeffrey Johnson:[/i][quote]Hello Alfonso and others,
I've run a few second-level ROI-to-ROI analyses (mostly 2-sample t-tests) involving comparisons between different combinations of four participant groups with different sizes (n = 9, 10, 17, and 17). A reviewer has asked us to address the possibility that some results might be due to differences in sample size, rather than (or in addition to) true differences in connectivity.

For example, when we compare groups 1 and 4 (n=9 vs. n=17), there are about 15 significantly different connections, but when we compare groups 3 and 4  (n=17 vs. n=17), there are about 35 significant differences. We would like to gain some insight into whether there are fewer differences between groups 1 and 4 than 3 and 4 because there is less power in the first analysis due to group 1 (n=9) being smaller than group 3 (n=17). I was thinking it might be helpful to compare the variance in the correlation coefficients for each group (since those are the data in each of the t-tests in my analyses), as this would at least let me see if we're in violation of the assumption of homogeneity of variance, but my network has more than 700 connections so it's not really feasible to try to compute those values manually. Is there some way to efficiently obtain the variances (maybe a distribution of variances across each group's network) from the CONN results just to get an idea of how things compare between groups? Or do you have any suggestions for a better/alternative way to account for sample size differences? 

Any suggestions or guidance would be very much appreciated.

Thank you,
Jeff[/quote]
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