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Type of fieldmap

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Hi,

I have a type of fieldmap that is different from normal phase / magnitude files. It is one blip image exactly the same  size of my EPI files. I was wondering how I can use it in the preprocessing step of Conn. It gives an error when I choose any of the options. Please find attcahed the file.

I appreciate your advice,
Haleh

RE: How CONN set up ROI as default?

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Hi,

Im struggling with this issue too, has it been solved?

Thank you.

RE: duplicate connections exporting table

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HI Alfonso and group?
Has this been previously addressed?
Has any one else run int this issue?

RE: CONN error during Preprocessing

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Thanks for the help! That seems to clear up the issue we were encountering.

RE: Problem with second-level analysis main CONN GUI & result explorer display , when using different template and atlas files.

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Thank you so much Alfonso.

One other minor issue, My template, atlas, and mask are all co-registered and are in the same space. But i noticed that they have an LPI orientation While the outcome functional images are in the RPI orientation (which is the same for the default conn template and atlas). Does this affect the analysis in any way? Does conn properly handle the orientation? And in what orientation should the T1 and atlas be when I use them for the display of the second level results?
Hope this is handled by conn and that it won't be an issue.
Many thanks,
Mohammad Kawas.

RE: SVD did not converge error is still happening

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Dear Alfonso,

Thank you very much for your reply. I updated to MATLAB 2018b and indeed the issue was fixed, no more SVD convergence error. Thank you for your advice, I am not sure I still have MATLAB 2018a lying around but if I do I will try that and tell you if it works.

Best regards,
Stephen

RE: problem in running the demonising

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Thanks Alfonso. My apologies for coming back late .

I was again in Boston for ASNR and could not get to the forum. 

Best regards
Joga

CONN Freesurfer space analysis

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Hello,

I have two questions about surface-based analysis in Freesurfer space (Freesurfer structural images, same subject-space functional images), as I need to transform functional images after preprocessing (denoising/filtering/acompcor) from surface to volume for additional analysis (I guess they are surface-based after these steps).

Specifically:
1) Where can I find these functional maps I'm looking for?

2) Do you know how I can map these files from surface to volume?

Best,
Lorenzo

2nd level voxel-to-voxel ICA interpretation

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Hello, 
I am running 2nd level analysis on ICA components (CEN, DMN) to compare groups or correlate connectivity with an outcome such as performance. While results of seed-to-voxel analysis on that level seem straightforward, I am not sure about interpretation of the results from voxel-to-voxel analysis. So if I get significant positive as well as negative clusters for a given contrast for a specific ICA map how is the interpretation in terms of connectivity? Let's say I am correlating performance with connectivity in the ICA-based CEN: positive clusters refer to positive association between connecitivity in (?) the CEN and performance? And negative clusters refer to regions in (or with?) the CEN that show detrimental connectivity on performance?

Thanks for any hint! 
Cheers,
Anton

RE: Calculate framewise displacement Jenkinson

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Dear Alfonso, dear all,

I came across this thread and ran the patch, which worked great - I got the FD Power values I was looking for. Thanks so much for this.

However, after running the patch, only the FD variable is listed in the 1st level Covariates; all other covariates are gone (e.g. realignment, scrubbing, QC timeseries). Has anyone noticed this before?

Is there a way to import the missing variables back into the 1st level Covariates, to avoid having to rerun the preprocessing steps? Which are the files that would need to be imported?

Thanks so much for any help you can provide!

Best wishes,

Madeleine


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

[color=#000000]Please try the attached patch using the syntax:[/color]

   conn_convertl12l1covariate FD_jenkinson

That will create in your CONN project a new 'QC_FDjenkinson' covariate with the Framewise Displacement timeseries (using Jenkinson definition and FSL's defaults, instead of CONN's). See "help conn_convertl12l1covariate" for additional options (the script allows you to use different Sphere radius or center definitions in Jenkinson's FD formulation, use CONN's or Power's formulation instead, etc.) This script is still under development so please let me know if you run into any issues

Best
Alfonso
[i]Originally posted by Julian Dronse:[/i][quote]Dear Alfonso, dear all,

is there an easy way to compute FD Jenkinson using the first level realignment covariate for use as a 2nd level covariate in CONN?

Thank you!
Best wishes

Julian[/quote][/quote]

ROI-to-ROI averaging 2 ROI's

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Hi everyone, 
Wondering if there's a way to take the average timeseries of 2 ROI's in the ROI-to-ROI 2nd level analysis mode? (for instance, average timeseries of left and right insula). I can easily do this in the seed-to-voxel mode, but not ROI-to-ROI. When I select both ROI's, it simply runs an analysis on both separately. 
Thanks!

connectivity matrices, SCM in ART and denoising error help

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Hello Alfonso,

Thank you for the wonderful workshop in Boston, it was really informative and helpful!

I have few questions that I need some help with:[list=1][*]As we discussed, there was one reviewer who wanted connectivity matrices (like the one in the file attached) for the ROI-to-ROI analyses, can you let me know the matlab code on how to get these connectivity matrices?[*]I am getting the following error everytime I run the denoising step. It stops at a specific subject during Step 5/6- preprocessing voxel to voxel covariance. I have checked the space on the USB drive and there is about 1.3 TB on it and my Mac has 16GB of RAM (so I don't think it's an issue of space). Do you know what it is about?[/list]Error using fseek
Invalid file identifier. Use fopen to generate a valid file identifier.
Error in conn_get_slice (line 16)
fseek(handle,4*sum(V.size.Nv(1:slice-1))*V.size.Nt,-1);
Error in conn_process (line 1682)
[y,idx]=conn_get_slice(Y,slice);
Error in conn_process (line 30)
case {'preprocessing_gui','denoising_gui'}, conn_disp(['CONN: RUNNING DENOISING STEP']); conn_process([1.5,2,6:9],varargin{:});
Error in conn (line 5907)
else conn_process('denoising_gui'); ispending=false;
Error in conn_menumanager (line 120)
feval(CONN_MM.MENU{n0}.callback{n1}{1},CONN_MM.MENU{n0}.callback{n1}{2:end});

       3. I have a question regarding the ART toolbox. Is there a way to analyze between-group differences in stimulus-correlated motion (SCM)? I only know how to look for stimulus-correlated motion at individual level, but how do I analyze it at group level and compare the 2 groups on the amount of SCM? I posted my question in the ART toolbox forum but didn't get any response. Hopefully, you will know the answer.

Thanks again!

RE: Freesurfer's Brainmask preprocessing

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Hi Alfonso,
Thank you for your answer and the attached patch. Sorry for the late reply. I tried the patch today and there are some weird results. The normalization has worked well for the structural but not with the ROI nor with the functionnal (see images attached). I also attach the pipeline I used.
Any idea of what's going on and how to fix it?
Thank you,
Olivier Roy

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

[color=#000000]Those are interesting questions. Briefly:[/color]

[color=#000000](1) that should be fine (but see (3) below)[/color]
(2) yes (and it will switch the Gray/White/CSF ROIs to point to those new tissue class masks)

(3) none of the existing pipelines will apply the normalization spatial-transformation encoded in the y_*.nii files generated during normalization to your externally-defined Gray/White/CSF ROIs (the "Segmentation&Normalization" steps will create new Gray/White/CSF ROI files as part of the segmentation step, while the "Normalization" step only normalizes the functional or structural data leaving your Gray/White/CSF ROIs unchanged; see below for some alternative options)

(4) that should work perfectly fine but your Gray/White/CSF ROIs will remain unchanged (in subject-space) so you would need to manually (e.g. using SPM gui or batch commands) transform those files to MNI-space using the y_*.nii files generated during the normalization step. 

Because (4) is somewhat time-consuming (and I imagine there would be other scenarios where users may want to use externally-defined tissue masks) I am also attaching a patch that includes a new "Structural Normalization with user-defined Gray/White/CSF masks" and "Functional Indirect Normalization with user-defined Gray/White/CSF masks" steps. These work just the same as the original "Structural Normalization" and "Functional Indirect Normalization" steps, respectively, but they will also keep any user-defined Gray/White/CSF ROIs (e.g. those that were imported from FreeSurfer) in the same space as your structural data. Internally they will simply apply in both cases the same non-linear transformations that is applied to the structural data to your Gray/White/CSF ROIs as well. Note that the assumption in both of these cases is that your externally-defined Gray/White/CSF masks will be in the same space as your structural data (which makes sense when importing FreeSurfer data). Let me know if you run into any issues with this patch (this patch is for release 18b, to install it simply copy the attached file to your conn distribution folder overwriting the file with the same name there)

Hope this helps
Alfonso

[i]Originally posted by Olivier Roy:[/i][quote]Hello,
Because I had trouble with skull-stripping with my raw T1 images (some skull often left in the posterior aspect of the brain), I decided to use the brainmask.mgz from Freesurfer which was properly skull-stripped. By using the brainmask, I also allowed Conn to import the segmentation files from Freesurfer. I then ran a modified version of the second volume-based preprocessing pipeline in Conn (see attached image): in brief, I just removed the "functional Creation of voxel-displacement map (VDM) for distortion correction" and replaced the realignment step with "functional Realignment & unwarp (subject motion estimation and correction)", effectively getting rid of the distortion correction part.

My questions are:
1. Since I used the brainmask.mgz which is already skull-stripped, will the skull-stripping part of the "functional Indirect Segmentation & Normalization" step have altered the image too much with the second skull-stripping step? So that I lose GM or CSF information for instance?

2. From what I understand, the "functional Indirect Segmentation & Normalization" step also resegment the Grey/White/CSF and overwrite those from Freesurfer. Is that true?

3. I want to co-register functional and structural volumes and then normalize to MNI while also keeping (and normalizing) the Grey/White/CSF segmentation from Freesurfer. If instead of the step "functional Indirect Segmentation & Normalization" I use the step "functional Indirect Normalization" (which also coregister structural and functional), will it also normalize the Grey/White/CSF segmentation from Freesurfer?
<span style="white-space: pre;"> </span>- I am asking this question because the "[u]functional Indirect Segmentation & Normalization[/u]" step gives as output the skull-stripped normalized structural volume, [b]normalized Grey/White/CSF masks[/b] and normalized functional volumes (all in MNI space) [u]whereas[/u] the "functional Indirect Normalization" step gives the [b]same thing (also seems to skull-strips; normal?) [/b][u]less the normalized Grey/White/CSF masks[/u]. 

4. Finally, if the answer to question #1 is that the second skull-stripping is problematic. Can I replace the step "functional Indirect Segmentation & Normalization" which skull-strips with the "functional Direct Coregistration to structural without reslicing" and subsequently "functional Direct Normalization" which do not seem to skull-strip again. How should I arrange the steps in the sequence in this case? Also in this case, how could I make sure that the Grey/White/CSF masks from Freesurfer get normalized to MNI?

Thank you and sorry for the long post,
Olivier[/quote][/quote]

fprintf error when running batch script

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Hi Conn Forum,

I'm a new user and am encountering an error when attempting to run a conn batch script. I have data that was preprocessed using the HCP pipelines and I would like to use the conn_batch_humanconnectomeproject.m script to import the data into conn. I have made the appropriate modifications to the script so that it finds my files (e.g., changed the names and number of resting state scans). 

When I run the script, my files are found and read correctly - but then I quickly get this error?

%%%%%%%%%%%%%%%%%%%%%%%%

Error using fprintf
Invalid file identifier. Use fopen to generate a valid file identifier.

Error in conn_disp (line 162)
for n=1:numel(str), fprintf(fh,'%s\n',str{n}); end

Error in conn_process (line 27)
case 'setup_preprocessing', conn_disp(['CONN: RUNNING SETUP.PREPROCESSING STEP']); conn_setup_preproc(varargin{:});

Error in conn_batch (line 1099)
conn_process('setup_preprocessing',steps,'subjects',SUBJECTS,OPTIONS{:});

162 for n=1:numel(str), fprintf(fh,'%s\n',str{n}); end

%%%%%%%%%%%%%%%%%%%%%%%%

Using the real-time debugger in matlab, it appears that a file is attempting to be opened by fprintf. This file is taken from line 160-161 of the conn_disp.m:

%%%%%%%%%%%%%%%%%%%%%%%%

filename=fullfile(conn_prepend('',conn_projectmanager('projectfile'),''),'logfile.txt');
fh=fopen(filename,'at');

%%%%%%%%%%%%%%%%%%%%%%%%

It appears that conn is trying to open the log file, but this log file does not exist. I'm assuming that it should have been created in an earlier step that I may have neglected to complete. Any help that could be offered is appreciated!

-Matt

CONN: Missing ROIs within one File after Setup Step

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Dear Conn Community, dear Alfonso,

I'm quiet new to CONN and usually I could find all the answers to my questions within this forum - thanks for that!
However, this time I wonder if you could help me solving this problem:

I've uploaded several multiple ROI files in MNI space in the SETUP step. Each of theses files contains multiple ROIs ("Atlas" box is marked).
With the display ROI function, I can see that all ROIs are also uploaded (for example 37 ROIs within one ROI file) and properly coregistered.

But, after running the SETUP pipeline I run into two problems:
First, the individual ROIs of one file are not named by their name as I have named them in an additional txt file (same pattern as the default atlas and network ROIs), but all ROIs of one file have the same name with a number, e.g. "ROI.Cluster001, ROI.Cluster002"

Second, not all ROIs of one file are implemented. For example the file including 37 ROIs lists only 25 "Clusters". I don't know if ROI.Cluster001 represents the first entry of my .txt file. and I don't know how to check what went wrong.

Do you have any suggestions how I could proceed to solve these problems?

Thanks in advance,
Alexandra

invalid results after denoising CONN

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Dear all,

For one of our studies we are getting multiband, multi-echo data.
Params/specifics are:
Siemens 7T Magnetom, Acc factor 2, 3 TEs, TR 1.6, two functional runs of each 36 minutes (condition A versus B)
Since we are focusing on the brainstem we are not getting whole brain data (a portion of parietal/occipital cortex is missing in the original data)

For a quick & dirty analysis I have isolated the second echo time data and processed the data as single echo (using the default preprocessing pipeline in CONN). Preprocessing appears to have worked out nicely, after the final step (smoothing) the data is still OK. However, after proceeding with the denoising step in CONN, processing results are clearly invalid as we are suddenly left with whole brain data. Just to see what it does to the results I ran a 1st level analysis within SPM before and after denoising. Oddly, after denoising we are only seeing significant differences in the part of the brain that is 'added' by CONN.

Can someone explain what is making denoising invalid in this case? How/why is the data manipulated to become "whole brain"?

Many thanks in advance.
Best regards,
Bram

Error at step 5/7 Importing ROI data in setup

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Hi,

I am trying to use CONN for some functional connectivity of resting state data. After following the project wizard and preprocessing my data using the standard pipeline, and after doing the setup and clicking done, at step 5 'Importing ROI data', I keep coming across this error:

Step 5/7: Importing ROI data
ERROR DESCRIPTION:

Error using conn_process (line 762)
Error extracting Grey Matter BOLD signal for subject 1 session 1
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 5262)
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});
CONN18.b
SPM12 + Anatomy DEM FieldMap MEEGtools
Matlab v.2018a
project: CONN18.b
storage: 27.3Gb available
spm @ /Users/prabhjotdhami/Documents/MATLAB/Add-Ons/spm12
conn @ /Users/prabhjotdhami/Documents/MATLAB/Add-Ons/conn

Any advice as to what to do?

Best,
Paul

set-up_miss use of spm for segment

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错误使用 spm (line 1231)
Unknown action string

出错 conn_setup_preproc (line 821)
CONN_x.SetupPreproc.log{end+1}={'timestamp',datestr(now),'ver_CONN',conn('ver'),'ver_SPM',spm('version'),'steps',...

出错 conn_process (line 268)
conn_setup_preproc('run_structural_segment','subjects',nsub);

出错 conn_process (line 16)
case 'setup', conn_disp(['CONN: RUNNING SETUP STEP']); conn_process([0:4,4.5,5]);

出错 conn (line 5262)
else conn_process(processname); ispending=false;

出错 conn_menumanager (line 120)
feval(CONN_MM.MENU{n0}.callback{n1}{1},CONN_MM.MENU{n0}.callback{n1}{2:end});

CONN18.b
SPM8 + Beamforming DEM FieldMap MEEGtools vbm8
Matlab v.2014a
project: CONN18.b
storage: 489.5Gb available

spm @ E:\tool\spm8
conn @ E:\tool\conn18b\conn

Decimal number in second lvl contrasts

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Dear conn colleagues,

In these moments we are finalizing quite complex experiment. In conn second level analysis we want used paired t-test show us results between conditions recorded in two different session.

1) In our spm matrix we used, among other things, contrasts containing values such as 0.25, etc. (see left part of image in attachment). Now we want used same contrast in connectivity tests by conn. I'm not shure if I can used between condition contrast something like these: [0.25 0.25 -1 0.25 0.25 -0.25 -0.25 1 -0.25 -0.25] - this contrast work fine and give me some results, but I'm not sure if they are relevant or not. My question is: if I'm interested in connectivity in contrast like these, can I enter conn values outside 1; 0; -1; is possible used values with decimal places as in case of spm? In the conn manual is not mentioned this possibility. And also, maybe is better used predefined conn contrast which offer me contrast like [0 0 -1 0 0 0 0 1 0 0].

2) Second question is about P value, in the conn results a few times happened to me that the global p-value (I had it set to 0.05) was higher (see red circle in attachment image), but in case of each region it was lower than global threshold (in picture green circle). Should I follow a global value or value for a region?

Thanks Peter

Correlation matrix for specific gPPI contrast

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Hi CONN experts, 

I'm trying to find the best way to get a whole brain roi-roi connectivity matrix with task effects modeled (ie gPPI) for post-hoc analysis. Currently I have set up my imaging files and input my task parameters as conditions and run first-level analysis with analysis type as : gPPI, ROI-to-ROI analyses only and analysis options: correlation (bivariate). My understanding is this produces a connectivity matrix for each subject in conn*/results/firstlevel/ANALYSIS_01/resultsROI_Condition*.mat which should reflect the time series of each roi in the analysis correlated to the time series of every other roi independent of any specific seed. Is this the correct interpretation? Finally, if I want to get the same connectivity matrix but for a specific contrast modeled at the second level (ie Condition 1 > Condition 2) is it possible to produce this using the conn toolbox and would this be a valid approach to examine the whole-brain connectome under specific task conditions?

Thank you,
Isabella
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