NITRC CONN : functional connectivity toolbox Forum: help

RE: CONN25.b fMRIPrep project: QC displays hang after Setup/Denoising tab despite ART covariates being created

Marco Echevarria — Sun, 14 Jun 2026 21:06:23 GMT

Hello, I would like to contribute by saying I could solve the problem of the QC dysplay simply by installing a new version of the matlab, I changed from matlab2021b to matlab @2026a, in case anyone has the same problem. Thanks, Marco Echevarria

RE: "Not Ready to start Denoising Step"

Luisa Hartmann — Sun, 14 Jun 2026 20:52:08 GMT

Hello,
 
this has been a few years ago, but I was just wondering if you've since found a solution to the problem? This happens to me with almost all subjects, and I do not know whats wrong. The preprocessing went normal.

RE: How to plot the result of each conditions in CONN?

Armin Toghi — Thu, 11 Jun 2026 11:48:30 GMT

Dear Alfonso,
Regarding the effect size plots I plot the figure using values in 'export effect-size' and then plot the individual data points inside each bar plot (from 'export raw data'). I attached the picture: it seems they dont match. I think there might be some adjustment for analysing the raw data, which doesnt simply represent the mean values of raw data for each condition. Could you please guide me on this. Is it wrong to plot the individual raw points on the current the effect size plot ?
 
Best,
Armin

CONN25.b fMRIPrep project: QC displays hang after Setup/Denoising tab despite ART covariates being created

Marco Echevarria — Wed, 10 Jun 2026 21:53:28 GMT

Dear Alfonso and CONN experts,
I am working on a CONN25.b project using fMRIPrep-preprocessed resting-state fMRI data. The dataset has 600 subjects and is stored on an external SSD. The project uses fMRIPrep outputs in MNI152NLin6Asym 2-mm space. Functional images were smoothed in CONN, and the subject-specific GM/WM/CSF ROIs were manually corrected to the corresponding fMRIPrep MNI-space probability maps. For WM/CSF extraction, the source functional data is set to the unsmoothed functional data.
I initially encountered the previously reported CONN25.b error in the Denoising & Quality Control window:
Index exceeds the number of array elements. Index must not exceed 0.
Error in conn (line 7570) if conn_existfile(fullfile(CONN_x.Preproc.qa.folders{3},'DataSensitivityScore.mat'))
Following the forum recommendation, I updated conn.m from the GitHub master branch. Later, to avoid a possible mixed installation, I installed the full CONN master branch in a separate folder and confirmed that MATLAB is using only that version:
which conn -all /Users/marcoechevarria/Downloads/conn_master/conn.m
which conn_qascores -all /Users/marcoechevarria/Downloads/conn_master/conn_qascores.m
which conn_process -all /Users/marcoechevarria/Downloads/conn_master/conn_process.m
which conn_display -all /Users/marcoechevarria/Downloads/conn_master/conn_display.m
The functional outlier detection / ART step appears to have completed successfully. In the Denoising tab, the confounds include:
White Matter (5P) CSF (5P) realignment (12P) scrubbing (63P) Effect of rest (2P)
The CONN_x covariates also suggest that ART/QC variables were created. For example, the first-level covariates include realignment, scrubbing, QC_timeseries, QC_framewise_displacement, QC_dvars, etc. The second-level covariates include:
QC_ValidScans QC_InvalidScans QC_ProportionValidScans QC_MaxMotion QC_MeanMotion QC_MaxGSchange QC_MeanGSchange QC_DOF QC_DOF_WelchSatterthwaite QC_PeakFC
The Data Quality / QC-FC display also computes successfully, although the score is poor:
Data Quality score: 52.1% QC-FC correlation analyses: n = 600
However, Data Validity and Data Sensitivity do not complete. The GUI repeatedly hangs at:
Creating QC displays. Please wait...
When checking:
global CONN_x numel(CONN_x.Preproc.qa.folders) CONN_x.Preproc.qa.folders'
I get:
ans = 3
ans = 3×1 cell array
<pre dir="ltr"><code dir="ltr">{0×0 double}
{'/Volumes/Extreme SSD/Analise/Projeto_1/results/qa/QA_GUIrequest_DataQuality_2026_06_09'}
{0×0 double}</code></pre>
So, only the Data Quality folder seems to have been created. Data Validity and Data Sensitivity remain empty/outdated.
I also checked for the covariates that I believe should be created by Data Validity:
global CONN_x names = CONN_x.Setup.l2covariates.names;
names(contains(names,'QC_StdFC','IgnoreCase',true) | ... contains(names,'QC_NORM','IgnoreCase',true) | ... contains(names,'QC_IqrFC','IgnoreCase',true) | ... contains(names,'QC_MeanFC','IgnoreCase',true))'
This returns an empty cell array.
I then tried to run Data Validity manually:
global CONN_x
folderDV = fullfile(CONN_x.folders.qa, ['QA_GUIrequest_DataValidity_manual_', datestr(now,'yyyy_mm_dd_HHMMSS')]);
scoreDV = conn_qascores('DataValidity', folderDV, 1.Setup.nsubjects);
but this again opens the “Creating QC displays. Please wait...” window and appears to hang indefinitely, with MATLAB using very little CPU.
A similar issue seems related to a recent forum post by Joshua Neal from May 2, 2026, where DataValidity/DataSensitivity/QC display errors occurred after the conn.m patch in a CONN25.b + fMRIPrep project.
My questions are:
<ol start="1" data-spread="true">
<li>Does the functional outlier detection / ART step appear to have completed correctly based on the presence of scrubbing, realignment, QC_ProportionValidScans, QC_InvalidScans, QC_MeanMotion, and QC_MeanGSchange?</li>
<li>Is DataValidity expected to create QC_IqrFC, QC_MeanFC, and QC_StdFC only after the denoising step has been fully run?</li>
<li>Should I proceed to run the Denoising step first and then recompute Data Validity / Data Quality / Data Sensitivity afterwards?</li>
<li>Is there a recommended way to compute DataValidity and DataSensitivity without using the GUI QC display generation, or to bypass the hanging QA plot generation?</li>
<li>Could the issue be related to fMRIPrep projects in CONN25.b, or to empty/missing QA_COV / QA_DENOISE histogram data?</li>
</ol>
For reference, my current setup is:
CONN25.b / CONN GitHub master tested SPM25 MATLAB R2021b initially; full CONN master also tested fMRIPrep-preprocessed data 600 subjects Functional data in MNI152NLin6Asym_res-2 space GM/WM/CSF ROIs corrected to MNI152NLin6Asym_res-2 probability maps
Thank you very much for your help. Best regards, Marco Echevarria

RE: Dual Data Set and denoising in CONN

Alfonso Nieto-Castanon — Sun, 07 Jun 2026 11:29:05 GMT

Hi Seda,
Preprocessing is applied separately to: a) all voxel-level data (each voxel BOLD timeseries within your "primary" dataset, the one defined in the Setup.Functional tab), which will be used for voxel-level anlyses (e.g. as target voxels in seed-based connectivity maps); and b) all ROI-level data (all of the ROIs that you defined in the Setup.ROIs tab, independent of which dataset they are getting their BOLD signals from), which will be used for ROI-level analyses (e.g. as seeds in seed-based connectivity maps, or as ROIs in RRC connectivity matrices)
Hope this helps
Alfonso
Originally posted by sedatasci:
<blockquote>
Hi,
I am doing denoising and first level ROI to ROI analysis in CONN on my resting state fMRI images. I preprocessed my whole brain with fmriprep and I processed my cerebellum seperately as well. I input my whole brain as a first data set and cerebellum as a second data set. I also seperate my ROIs as whole brain and cerebellar ROIs. My question is when I do denoising, does it applies to first data set only (since I am only getting denoised whole brain fmri image as an output) or it applies to ROIs and give me denoised signals on my ROIs for both whole brain and cerebellum. Thank you for your help!
Best,
Seda
</blockquote>

RE: CONN 25.b - NaN values during denoising and second-level analysis

Alfonso Nieto-Castanon — Sun, 07 Jun 2026 11:25:05 GMT

Hi Remas,
Not sure about this case, I would suggest to try you identify the "first place" where those NaN values appear as some of the other/subsequent places may be just a consequence of those initial issues. Are those NaN somewhere in the BOLD signal itself? or perhaps they show up first in the first-level covariates created during preprocessing? and if so, in which preprocessing step?, was preprocessing run within CONN or were these covariates imported from fMRIPrep or other software packages?
Best
Alfonso
Originally posted by Remas Alsaadi:
<blockquote>
Hello,
I am using CONN Toolbox version 25.b for resting-state fMRI study.
Study design: - 19 subjects - 2 conditions:   - Quran   - Speaker - Seed-to-Voxel analysis
I am experiencing persistent NaN-related errors.
Error during second-level analysis: "Null design matrix (all subjects have missing data or all selected covariates have zero or NaN values)"
Error during denoising: "Input matrix must not contain NaN or Inf values"
I recreated the project from scratch and reran preprocessing.
I also noticed that some QC covariates contain NaN values. For example:
QC_meanFC = NaN for all subjects  QC_StdFC = NaN for all subjects QC_outlierScore = NaN for all subjects 
I tried: - Removing age covariate - Excluding one subject that showed 0 value on QC_validsubjects  - Removing "Effect of Quran" and "Effect of speaker" from denoising
After removing the condition effects, denoising completed successfully, but second-level analyses still produce NaN-related errors.
Could you please advise how to identify the source of these NaN values and whether the issue is related to denoising, QC covariates, ROI data, or project setup?
Thank you very much.
</blockquote>

RE: Error Second Level Analysis

Alfonso Nieto-Castanon — Sun, 07 Jun 2026 11:20:55 GMT

Hi Gabby
Did you get any warning or error message when running those first-level analyses, which may help understand why CONN is not able to proceed to second-level analysis normally? (if in doubt, please send me your conn_*.mat file and I will take a quick look to see if I can find out what may be going on here)
Best
Alfonso
Originally posted by Gabby Guadalupe:
<blockquote>
Hello, 
I am trying to calculate resting-state ROI-ROI functional connecvitiy differences between two groups (patients vs. controls), however, I am encountering the following error in Second-Level Analysis:
"Not ready to display second-level Analyses. No conditions found. Please re-run first-level analyses or select different first-level analysis to continue."
In Setup there is only 1 condition (rest), and for 1st-level I select "All conditions" before running.
I reran 1st-level with these parameters and received the same error:
<ul>
<li>Analysis option: ROI-to-ROI anlysis only,</li>
<li>correlation bivariate</li>
<li>no weighting</li>
<li>DMN.MPFC as seed 1, DMN PCC as seed 2</li>
<li>no temporal expansion</li>
<li>no frequency decomposition</li>
</ul>
Do I need to change one of the 1st level options or is there another way to complete 2nd level analysis? 
Thank you!  
-Gabby
</blockquote>

RE: fc-MVPA source voxel correlation matrix

Alfonso Nieto-Castanon — Sun, 07 Jun 2026 10:51:08 GMT

Hi Jane,
Yes, that is perfectly correct. You are changing a bit the meaning of "n" in your notation, some times referring to the n-th source voxel (in R_n), and other times referring to the n-th subject (in r_n(x,y)). So just for clarity and changing a bit the notation: the R_x matrix has size [#-of-voxels by #-of-subjects], and the element (y,n) of this matrix R_x(y,n) will contain the functional correlation between the x-th voxel (the source) and the y-th voxel (the target) for the n-th subject (i.e. r_n(x,y)).
After that, fc-MVPA will compute the SVD of the matrix R_x (for each source voxel x) and keep the right singular vectors, which can be effectively computed as the eigenvectors of the much smaller R_x' * R_x matrix (a [#-of-subjects by #-of-subjects] matrix).
And regarding your specific questions, the total set of voxels included as x and y in your matrices is determined in CONN by the analysis mask (defined in the Setup.Options tab). The voxels included in y can also be further limited by the ICA-mask (for masked-ICA analyses only; this is defined in CONN in the first-level analysis tab by clicking on "masked-ICA" checkbox and selecting there your ICA-mask). To be  precise, x ranges over all voxels specified in your general analysis mask, and y ranges over all voxels specified in the conjunction of your general analysis mask and your specific ICA-mask (if applicable). The index x controls at which voxels CONN will compute fc-MVPA components, and the index y controls which portion of the correlation matrix (between a given voxel and the rest of the brain) will be used in the dimensionality reduction step.
Hope this heps
Alfonso
Originally posted by JANE OCONNOR:
<blockquote>
Hi CONN team,
I have created a mock up of the single subject matrix (screenshot attached) as I understand it would be set up for dimensionality reduction. Is there a way in CONN to determine how many total voxel were used, or the dimensionality of the individual correlation matrix?
My figure and explanation is based on how I understood Nieto-Castanon, A. (2022). Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA).  
Does the correlation matrix I created for the source voxel make sense and do you detect any obvious errors? for context I have n=35. 
For each voxel (<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="7" height="16">) a Pearson correlation, <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="40" height="16"> is calculated for that voxel (<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="7" height="16">) and every other voxel’s (<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="40" height="16">) BOLD time series.
<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="311" height="72">
Where <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="14" height="16"> represents the matrix for n source voxel and   <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="38" height="16"> represents the Pearson correlation coefficient of the time series (BOLD) between voxel (<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="7" height="16">) and voxel (<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="7" height="16">).
  <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="53" height="16"> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 5.0pt; mso-text-raise: -5.0pt; mso-ansi-language: EN-CA; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"><img src="data:image/emf;base64,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" width="170" height="16">
</blockquote>

RE: Completing missing preprocessing steps after troubleshooting

Alfonso Nieto-Castanon — Sun, 07 Jun 2026 10:23:01 GMT

Hi Johanna,
The preprocessing pipeline runs one step at a time through all subjects (i.e. first it runs realignment through all subjects, when that finishes correctly it runs slice-timing correction through all subjects, etc.). When the pipeline is stop, either manually or by running into an error, the CONN project will maintain the state of the last step in the pipeline which was successfuly run, so you can continue the pipeline through the next step (the one that did not complete correctly) until the end.
In your case that means you can click on the 'Run Preprocessing' button to restart your preprocessing pipeline, then select all of the steps in your current pipeline prior to "functional Slice-Timing Correction" step (the ones that alreaedy finished correctly) and click on 'Remove' to remove those steps from the pipeline, and then click on 'Start' to have the preprocessing pipeline continue where it left off.
Hope this helps
Alfonso
Originally posted by shivapriya25:
<blockquote>
Hi,
I've just run into a problem concerning slice timing in one subject during preprocessing, so I had to stop the pipeline and troubleshoot first. Now I've seen that apparently, preprocessing has not run through in all subjects, and the last step seems to habe generated some file named "meanuXXXX". Do I have to start from scratch or can I somehow continue where I left? (I've used the stanard pipeline)
 
Thank you so much for your help
Johanna
 
 
</blockquote>

Completing missing preprocessing steps after troubleshooting

shivapriya25 — Fri, 05 Jun 2026 7:04:34 GMT

Hi,
I've just run into a problem concerning slice timing in one subject during preprocessing, so I had to stop the pipeline and troubleshoot first. Now I've seen that apparently, preprocessing has not run through in all subjects, and the last step seems to habe generated some file named "meanuXXXX". Do I have to start from scratch or can I somehow continue where I left? (I've used the stanard pipeline)
 
Thank you so much for your help
Johanna