NITRC CONN : functional connectivity toolbox Forum: help

Thereshoding of reporting subregions of the cluster

sevilay ayyildiz — Thu, 18 Jun 2026 10:08:25 GMT

Hello,
I am conducting a seed-to-voxel functional connectivity analysis in CONN (resting-state fMRI, group comparison). My significant clusters are large (e.g., 1727 voxels) and the CONN output lists anatomical sub-regions from the Harvard-Oxford atlas.
Example result of that cluster:
346 voxels (20%) covering 61% of PT l  227 voxels (13%) covering 58% of pSTG l  158 voxels (9%) covering 16% of CO l  ... 59 voxels (3%) covering 19% of HG l  51 voxels (3%) covering 14% of PP l  17 voxels (1%) covering 4% of aMTG l  10 voxels (1%) covering 0% of PostCG l  9 voxels (1%) covering 1% of aSMG l  2 voxels (0%) covering 0% of Thalamus l 
My question is: Is there a recommended minimum threshold for reporting these sub-regions in a results table? For example, should I only list sub-regions above a certain percentage of the cluster (e.g., ≥3% or ≥5%), or above a minimum voxel count?
Do you have suggestion should I report regions comprising spesific percentage (for example (>5) of the cluster or with substantial anatomical coverage? Or should I just report the all regions in the cluster?
Thank you in advance!

Covariate and Contrast Selection for Partially Longitudinal Data

mnarc — Wed, 17 Jun 2026 17:44:28 GMT

Hello!
I have a partially longitudinal resting fMRI dataset (participants visited once or twice). In each session, participants completed a resting-state scan in which they saw either a fixation cross or a movie. This was not necessarily a between-subjects variable, because some participants saw fixation in session 1 and movie in session 2, or vice versa. I'd like to perform a contrast between fixation and movie.
To do so, I have created 4 covariates to capture the fact that one participant may have seen a movie on their first visit, but a fixation on their second:
<ul>
<li>Fixation_01 (coded as 1 if they viewed a fixation cross in session 1, 0 if they did not, NaN if there is missing data; 133 non-NA subjects)</li>
<li>Movie_01 (coded as 1 if they viewed a movie in session 1, 0 if they did not, NaN if there is missing data; 133 non-NA subjects)</li>
<li>Fixation_02 (same as above but for session 2, 43 non-NA subjects)</li>
<li>Movie_02 (same as above but for session 2, 43 non-NA subjects)</li>
</ul>
CONN also automatically sorted sessions into the "conditions" tab so I have the conditions of 01_rest and 02_rest.
 
Now, I would like to contrast fixation vs. movie averaging across both sessions to see if there is a difference, to do this I tried running:
between-subjects contrast: [1 -1 1 -1] (with the variables in the order I listed above)
between-conditions contrast: [1/2 1/2]
However, I get this warning and a design matrix with only 37 samples (I am unclear on where this number is coming from).
<img src="data:image/png;base64,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" width="335" height="424"> 
Is there something wrong in the way I have set up the covariates and/or the contrast? 
Thank you so much for your help!!

RE: Dual Data Set and denoising in CONN

sedatasci — Wed, 17 Jun 2026 11:10:06 GMT

Thank you Alfonso, it was very helpful!
Best,
Seda

RE: Framewise Displacement Settings: What is the literature support for the intermediate setting (FD 0.9 + std 6)?

Renzo Torrecuso — Tue, 16 Jun 2026 12:59:33 GMT

Dear Alfonso,
Thanks a lot indeed for your reply, as always, very helpfull.
Following on that topic another quite tricky question (at least for me now, but believe will be useful for many).
When the super handy option of "Describe Methods" produces the paragraph of "Describe Quality Control steps" one reads that "(...) and excluding participants whose QC metrics were extreme outliers (values more than three interquartile ranges beyond the first or third quartile)".
My question is: are they actually automatically exluded? If YES, why do they still appear in my Secon dlevel covariates?
Thanks a lot for any input!
All best!
Renzo

Different Roi to Roi order may hide results?

Renzo Torrecuso — Tue, 16 Jun 2026 12:53:31 GMT

Dear Alfonso or CONN Experts,

In my ROI-to-ROI analysis, I obtain results with Cohort A and save the ROI-to-ROI order. Then, when I load this order for Cohort B, the latter results disappear.

I would highly appreciate any insights regarding the following question:

1- Why does this happen if we have a connectome that includes all regions and, potentially, all of them could connect to each other?

2- How could I create or use a "universal" ROI order?

Thanks a lot!

Cheers

Renzo

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>