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Pairwise time-series correlations in a mask

September 26, 2020, 3:39 am
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Hi CONN experts,

I would like to compute the average of all pairwise correlations between the time-series of all the voxels in a specfic mask for each subject - is it something I can get using the GUI? or does it needs to be implemented directly in MATLAB?

Thanks!
Tamir
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Adding subjects

September 27, 2020, 10:39 am
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Dear Experts,
I want to add 2 subjects in the middle of all subjects, as 51 and 52 in 100 subjects. I tried to add but it adds at the end. Please suggest me how to add on the middle of all subjects. 

Thanks
Ramesh
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conn problem

September 27, 2020, 11:34 am
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Hello
I have this problem while I work with anothers data
ERROR DESCRIPTION:

Error using conn_menu>conn_spm_read_vols (line 1091)
Error reading file /Applications/conn/utils/surf/referenceT1.nii. File may have been modified or relocated. Please load file again
Error in conn_menu (line 666)
[temp,volhdr]=conn_spm_read_vols(title);
Error in conn (line 2450)
conn_menu('updateimage',CONN_h.menus.m_setup_00{5},vol);
CONN19.c
SPM12 + DAiSS DEM FieldMap MEEGtools
Matlab v.2016a
project: CONN19.c
storage: 334.8Gb available
spm @ /Applications/spm12
conn @ /Applications/conn
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Problem with assessing variance in Group Data

September 28, 2020, 1:27 pm
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Hi Alfonso,

I'm trying to assess variance between patient pre-op and post-op data. What I'm trying to do is do a subtraction at the first level - where only the patient's pre-op and post-op data is subtracted to get each patient's difference in connectivity after surgery.

It is my understanding that the first-level analysis will look at all the patient's connectivity for each of their session scans. This results in outputting beta maps for pre-op and post op seperately for each subject, scan and ROI. These are then grouped based on pre-op or post-op in the second-level analysis and then subtracted between sessions (1 -1) as a group. 

We are now assuming that the connectivity differences in the patient group before and after surgery may not be as homogenous as controls and would like to assess them based on each individual subject's differences due to the surgery rather than grouping all the pre-op together and then subtracting the combined post-op. Is there any way to do this in CONN?

I also thought about importing 1st level maps that subtract each pre- and post-op data (using the connectivity maps from CONN) using a fixed-effect analysis (since CONN doesn't do fixed effects) and then import them into the second-level analysis. However, I can't seem to find a way to import the first-level maps, since I can't perform a seed-to-voxel analysis in my other software. Is there a way to import 1st level connectivity maps to be analyzed at the 2nd-level in Conn?

Any suggestions would be greatly appreciated. Thanks for always replying so quickly.

Sincerely,
Jacinta
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RE: between/within subject analysis in CONN

September 28, 2020, 4:41 pm
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[color=#000000]Dear Alex,[/color]

[color=#000000]Yes, exactly, your description looks perfectly correct and you will be able to run both analyses, comparing "pre" data across subjects (using all subjects from the 4 studies, e.g. comparing patients and controls) as well as within-subject contrast such as "pre-post" (only for those subjects where both conditions are available, i.e. studies 1-3). In [i]Setup.Conditions [/i]remember to set the missing-data option to "allow missing-data" so that CONN does not complain that the "post" condition is not available in all subjects.[/color]

[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by Alexandra Muratore:[/i][quote]Dear Alfonso,

I am planning to run a rs-connectivity analysis combining patients and healthy controls from several studies. Participants from 3 of the 4 studies have data from both Time 1 and Time 2. Scans taken during Time 1 and Time 2 each have 2 resting state runs. Because patients are underweight at Time 1, I want to use a session-specific anatomical to match to their functionals. The participants from the 4th study have data collected from only 1 Time Point, with only 1 run each. As of now, I have the data set up so that participants from the first 3 studies are listed as having "4" sessions under Setup.Basic. In the Setup.Anatomical tab, I have assigned their Time 1 structural image to both Session 1 and Session 2 (which I'll be using as run 1 and run 2 taken during Time 1) and their Time 2 structural image to both Session 3 and 4 (which I'll be using as run 1 and 2 taken during Time 2). For participants from the 4th study, I'm giving them only 1 session, and assigning their 1 anatomical and 1 functional to this session. I'm planning to set up "Pre" and "Post" conditions and assigning Time 1 data to "Pre" and Time 2 data to "Post" (with the exception of participants who only have one time point, who's data will just go into "Pre"). I then plan to set up 2nd level covariates to establish groups of patients versus controls. Two questions have come up:

1. Is this the right way to set up the data? 
2. If so, will I be able to use this project to conduct analyses looking at both a) between-subject data (patients vs controls) for all subjects with Time 1 data, and 2) between (patient vs control) and within-subject (time 1 vs time 2) changes for just individuals who have data from multiple time points?

Thank you in advance!
Alex[/quote]
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RE: Automatically extract timeseries for ROIs (denoised data)

September 28, 2020, 4:55 pm
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[color=#000000]Hi Julia,[/color]

[color=#000000]The data is concatenated across all sessions, and the field conditionweights{1} points to which samples among all those 3080 should be associated with each individual condition. In any way, it is perhaps simpler to use the files ROI_Subject*_Condition000.mat to extract jointly all condition-specific timeseries using something like:[/color]

[color=#000000]  load('ROI_Subject120_Condition000.mat');[/color]
  y={};  
  for ncondition = 1:numel(conditionsweights)
[color=#000000]     y(ncondition,:) = cellfun(@(x)x(conditionsweights{ncondition}{1}>0,:),data,'uni',0); [/color]
  end

where the variable y will then contain a conditions-by-ROIs cell array of corresponding BOLD timeseries

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

When I check the data variable (timeseries) in the data folder file, e.g. ROI_Subject001_Condition001 it has 385 timepoints as expected (I recorded 8 resting-state sessions with 385 timepoints each).
But ROI_Subject001_Condition001 file in the Results/Preprocessing directory has 3080 timepoints (i.e. 8x385).
Why is that? And what does it mean? For me, every session is one condition (that spans the entire session). How can I extract the timecourse for this specific session/condition (001) only?

Thanks a lot in advance!
Cheers,
Julia[/quote]
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RE: Quadratic model

September 28, 2020, 5:01 pm
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[color=#000000]Yes, exactly[/color]
[color=#000000]Best[/color]
[color=#000000]Alfonso
[/color][i]Originally posted by Hugo Morandini:[/i][quote]Thank you very much Alfonso.

Just regarding the definition of the contrast, to look at the non-linear effect of age controlling for gender, would it be:

constant, age, age2, gender: [0 0 1 0] ?

Kind regards,

Hugo[/quote]
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RE: warning: no constant term modeled

September 28, 2020, 5:09 pm
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[color=#000000]Hello Leyla,[/color]

[color=#000000]The warning message seems to be caused by four subjects who are not labeled as part of GroupA nor GroupB (i.e. both GroupA and GroupB variables contain 0 values for these subjects), yet they do have some non-zero value in the Trait_groupA and/or Trait_groupB variables, which is inconsistent with them not being part of neither of these groups. To fix this, either enter 1 in the corresponding GroupA or GroupB variable for those four subjects to indicate which group they belong to (if you want to include them in this analysis), or enter in the GroupA or GroupB variables a value of NaN for those four subjects (if you want to remove them from this analysis; alternatively entering 0 values in the Trait_groupA and Trait_groupB variables for these subjects will also have the effect of removing these four subjects from this anlaysis)[/color]

[color=#000000]Best[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by leylabrucar:[/i][quote]Hello everyone,

I am wanting to test whether the association between trait and functional connectivity is different between my two groups and have set up the group comparison like so:
"GroupA", "GroupB", "Trait_groupA", and "Trait_groupB" second-level covariates, contrast [0 0 1 -1]

However, during my second level analyses, I have been receiving an error that says "Warning: possibly incorrect model: no constant term modeled (suggestion: add AllSubjects term to selected subject-effects list)."

I have attached a screenshot of this below.
Any suggestions as to how to fix this error?

Also - I was wondering if it is possible to export the design matrix to a .txt file?
Thank you!
Leyla[/quote]
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RE: Output space: Anatomical scan

September 28, 2020, 8:12 pm
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Just wanted to re-circulate this. Thanks in advance for your time and help!
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RE: gPPI deconvolution work-around for event-related data

September 28, 2020, 9:13 pm
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Hi there! Just wanted to bump this since its gotten shifted down a bit. Any advice is appreciated!
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help with batch and fmriprep

September 28, 2020, 9:59 pm
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Dear users, 

I'm using CONN and fmriprep and so far I can successfully load fmriprep outputs into CONN using the GUI. Does anyone know how to import fmriprep outputs (including confounds.tsv) using batch commands? 

Thank you. 
Ana
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RE: setting different acquisition orders

September 29, 2020, 8:02 am
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[color=#000000]Dear Alex,[/color]

[color=#000000]A couple of different ways would be:[/color]

[color=#000000]Option 1) preprocess different groups separately; when running preprocessing from the GUI, uncheck the '[b]All Subjects[/b]' button and select those subjects with the same acquisition parameters and preprocess only those subjects, then repeat for each different study or group[/color]

[color=#000000]Option 2) use BIDS format to specify subject-specific acquisition parameters; if the data from your different studies has been converted from DICOM to NIFTI using dcm2niix or some similar alternative you may already have .json files (one .json file associated with each .nii file) describing the acquisition parameters of your functional data. If that is the case, then, when preprocessing in CONN simply select the acquisition order labeled '[b]BIDS (from functional .json metadata)[/b]' and CONN will read that info automatically so you can run all subjects simultaneously despite the differences in acquisition parameters across subjects and/or sessions[/color]

[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by Alexandra Muratore:[/i][quote]Dear Alfonso,

I'm trying to combine participants from multiple studies, one of which is multi band and the rest of which have different scan-specific acquisitions (i.e., Siemens vs Philips interleaved). How can I do this using the GUI?

Thank you!
Alex[/quote]
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RE: Longitudinal doubts

September 29, 2020, 8:20 am
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[color=#000000]Hi Ines,[/color]

[color=#000000]In a pre/post design (and assuming that in your second-level analysis you select both pre and post and specify a [-1 1] between-conditions contrast) the group-level analysis will include as dependent variable the differences in connectivity between the pre- and post- scans. In your results explorer view you may then select the 'plot effects' button to see, within each significant cluster, barplots displaying the average connectivity with the seed within each of the pre- and post- scans separately, which can be helpful when interpreting the group-level analysis results. [/color]

And regarding differences in MRI session parameters, yes, if there are any other potentially-confounding sources of differences in connectivity between pre- and post- scans (i.e. other than the "intervention" which you would like to attribute as the plausible cause of any significant effects), it is a good idea to explicitly encode those differences as a new second-level covariate and enter that explicitly as a control covariate in your second-level analyses in order to statistically control for those effects. 

[color=#000000]Hope this helps[/color]
[color=#000000]Alfonso[/color]
[i]Originally posted by Ines Del Cerro:[/i][quote]Hi,

I would like to add some comments to my previous post:

Dear CONN users,

I'm performing a longitudinal analysis with two temporal points (pre - post). I was wondering if CONN introduces any kind of covariate or regression variable at any point in order to correct possible differences caused by the MRI session (e.g. calibration of the scanner), or if it would be correct to use some covariate to take it into account.

We have also having some problems for the interpretation of our results. In a pre/post seed-to-voxel rsfMRI analysis, does the results correspond to the difference of the connectivity between both temporal points? Is that correct?

Thanks again,

Inés[/quote]
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CONN freezing

September 29, 2020, 4:23 pm
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Hi,

I have an issue about running CONN. I am using Scientific Linux 6, Matlab R2019a, SPM12 and CONN 19c, which I access via SPM's toolboxes. I tried to use CONN for 3 subjects, I have uploaded properly structural and resting data, including the right parameters. I have chosen the default processing pipeline, but it is frozen in the stage where I have to introduce the smoothing parameter. It is not doing anything, and I do not receive any error message. The space is enough. Do you have any suggestions?
Previously I have used CONN 17f and later 18b, but I did not have any problems for a while, afterwards 18b started freezing but in the end of results part.
Thank you very much in advance!


Best regards,
Anca
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P1432 parcellation

September 30, 2020, 8:03 am
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Hi there I am currently analysing resting-state fMRI data and have decided to use the P1432 parcellation provided by CONN. However the atlas.txt file only shows the coordinates but has no labels for the corresponding ROIs. May I know where can I find the labels/region names for the P1432 parcellation.

Sorry I am quite new to CONN and brain data processing as a whole so hopefully my question made sense.
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ReHo Vs. LCORR differences and How to compute in CONN 19.

September 30, 2020, 11:43 am
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Hello Alfonso and fellow Conn users,

I want to know the difference between ReHo and LCORR.

ReHo was available in Conn 18 and as I understand it was the average connectivity between a voxel and its contiguous neighbor voxels.

LCORR is available in Conn 19 and is the average connectivity between a voxel and its neighbor voxels in a neighborhood of a specific size (default 25mm)

Is my understanding correct? and can ReHo be calculated in Conn 19 by setting the neighborhood size in LCORR to equal the voxel size? (for example 2 for 2mm isotropic voxels)

A clarification would be appreciated. And what are the difference in what the metrics represent?

Respectfully,
Mohammad
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RE: CAT12 Surface-based ROIs as seeds in CONN analyses?

October 1, 2020, 4:28 am
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Hello everyone,

I am also intrested in exporting the surface area in Cat12 as my ROI data. But I think it will not be possible in CONN now. As in COON, I think some of the preprocessing steps including volume-based, so extracting the ROI from the surface will propablely cause dimension problems.

What I am planning to do is to pick the peak coordinates of my surface results to build a 6-mm sphere ROI data using MarsBar. But I don't know if this was suitable?
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Can I load the first-level results to run the second-level analysis in SPM fMRI gui

October 1, 2020, 5:22 am
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Dear experts,

So sorry for troubling you with such stupid questions. Now I have preprocessed my resting state fMRI data in CONN toolbox.  Then I used the right caudate as seed to run the seed-based resting state functional connectivity analysis. Then I got the first level results. Now I want to to the second level analysis in SPM fMRI gui. So I choose the beta images from the first level analysis, and do I also need to import the second covariates in CONN as covariates in SPM?

Looking forward for your reply.

Best,

Hua
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Some subjects have discarded first volumes in raw data, some dont!!!!?

October 1, 2020, 9:00 am
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Dear collegues,

in my sample (n=616) there are about one third of the subjects having 232 volumes as the first 5 are deleted already. The rest still have 237 volumes.

I preprocessed the data with a batch functio and told it to "delete" the first 5 volumes...now i noticed some subjects lack those 5 first volumes - so here CONN will turn down volume 6-10 ...

Anybody an idea how to solve this problem? Can I somehow tell the batch not to discard the first volumes for certain subjects???


THANKS!!!
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ROI-to-ROI menu error.

October 1, 2020, 1:19 pm
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Dear Conn Toolbox community, 

thank you for your being so consistently helpful. I am trying to perform an ROI-to-ROI analysis in the Conn interface. Whenever I so much as try to open the ROI-to-ROI menu, however, it immediately throws an error: 

ERROR DESCRIPTION:

Matrix dimensions must agree.
Error in conn_process (line 4685)
nsubjects=find(any(X(:,nsubjecteffects)~=0,2)&~any(isnan(X(:,nsubjecteffects)),2)&~any(any(all(isnan(y),2),3),4));
Error in conn_process (line 57)
case 'results_roi', [varargout{1:nargout}]=conn_process(17,varargin{:});
Error in conn (line 9255)
CONN_h.menus.m_results.roiresults=conn_process('results_ROI',CONN_x.Results.xX.nsources,CONN_x.Results.xX.csources);
Error in conn_menumanager (line 134)
feval(CONN_MM.MENU{n0}.callback2{n1}{1},CONN_MM.MENU{n0}.callback2{n1}{2:end});
CONN18.b
SPM12 + DAiSS DEM FieldMap MEEGtools
Matlab v.2018b
project: CONN18.b

Due to the variable names in the error lines I assumed it might have had something to do with there somehow being a mismatch in number of files in my functional and structural setup, but I determined that not to be true. It will also throw this error whenever I try 'results explorer' or 'graph theory' from that same menu. Would there happen to be someone who can tell me what could be causing this issue? 

Kind regards, 
Romke Hannema
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