NITRC CONN : functional connectivity toolbox Forum: help

URGENT HELP: Hard time to compare Roi to Roi bewteen 2 or more cohorts. (so far impossible for cluster level correction).

Renzo Torrecuso — Tue, 04 Aug 2026 8:55:38 GMT

Dear Alfonso and CONN users,

I have been strugling for months with what seems to be a limitation of CONN (hopefully is just something I have to learn about the toolbox)

If I compare the Roi to Roi results between 2 diferent cohorts, corrected for cluster level, I run into a strange behaviour of the toolbox: I get VERY different results for even slight changes in the order of the regions in the connectome. Also, if I change the order I get different results that are not at all similar to the previous region order. In other words, you can either not see true results in one cohort or see 2 different results in one cohort. Then, only one of the regions order works in the second cohort. What leaves us with the very dubious task to assume which is "the right" one.

To make things even more complicated, If you remove the order of the regions, unfortunately thats no garanty that you will get the results in the same order, because even the order of the regions when you "remove the order" changes, is not always shown in a fix order.

Unless there's some procedure which sets some order in the Roi to Roi results, this limitations makes it, unfortunately, unreliable for publication.

Please could anybody help?

Best

Renzo

multi-site study subject import

berfin — Tue, 04 Aug 2026 8:38:42 GMT

Dear all,
I am currently trying to import two datasets (from two different sites, A and B) of the same study. 
While the group structure is the same, site A has 2 sessions and the site B has an extra follow-up session. Data from site B is already preprocessed, and has its own separate CONN project, while the data from site A is only BIDS-ed, therefore needs to be preprocessed on CONN first. 
Also, all data should be smoothed on CONN.
Is there a way to add the CONN-subjects from one CONN project to the other and then do the setup? (i.e. there is an "import from fmriprep" function, but not an "import from another CONN project option) As the session numbers differ, I cannot simply merge the two projects.
Kind regards,
Berfin

RE: URGENT HELP : Clarification about "Display effects" in seed-to-voxel analysis

adeleh — Tue, 04 Aug 2026 5:14:33 GMT

Dear Alfonso,
Thank you very much for your explanation. It is now clear to me that the bars displayed in the "Display effects" window correspond to the adjusted means estimated by the second-level model!
However, I am still confused by the magnitude of the difference between the raw and adjusted values.
In my exported raw data, the average Fisher-transformed connectivity values are:
<ul data-start="1121" data-end="1152">
<li data-section-id="yfc14w" data-start="1121" data-end="1134">V1 = 0.1904</li>
<li data-section-id="w9f8ud" data-start="1137" data-end="1150">V3 = 0.1802</li>
</ul>
whereas the adjusted means displayed by CONN are:
<ul data-start="1209" data-end="1241">
<li data-section-id="1qwnv9d" data-start="1209" data-end="1223">V1 = -0.1256</li>
<li data-section-id="w9bjex" data-start="1226" data-end="1239">V3 = 0.6076</li>
</ul>
Thus, the adjustment not only changes the absolute values, but also reverses the apparent direction of the V1–V3 difference.
My second-level model includes only two covariates (age and sex), and age and sex were entered without centering (and sex coded as 0/1).
Could this large discrepancy simply result from the lack of centering, or should I expect the adjusted means to remain relatively close to the raw means in this situation?
Also, if centering is recommended, should the age covariate be centered using the mean age of the subjects included in the second-level model (patients only), rather than all subjects in the project (patients + controls) ?
Thank you very much again for your help !
Adèle
 
Originally posted by Alfonso Nieto-Castanon:
<blockquote>
Dear Adele,
Effect-size refers to the effects estimated in your particular second-level model and used for statistical inference. In your case, for example, with a paired analysis comparing V1 vs V3 while controlling for age and gender, effect sizes represent the "adjusted means" in your model (i.e. the average difference between V1 and V3 estimated at the zero-level of your covariates; e.g. if your age/gender covariates are centered the zero-level of those covariates will correspond to the average age/gender in your sample)
More generally, if your second level model design matrix is X, your between-subjects contrast vector is C, and your between-conditions vector is M, then the second-level analysis will fit to your data matrix Y a model of the form:
Y ~ X*B 
where B is the estimated matrix of regressor coefficients. The "effect-sizes" displayed in CONN's plot always correspond to the elements of
effects = C*B*M'
In your particular case, X=[Subjects Age Gender], Y=[V1 V3], C=[1 0 0] and M=[-1 1], so the effect sizes are displaying the difference between B(1,1) and B(1,2), containing respectively the estimated adjusted means from your data Y.
Hope this helps
Alfonso 
Originally posted by adeleh:
<blockquote>
Dear Alfonso,
I am currently finalizing the results for my PhD thesis, so I would be extremely grateful if you could help clarify this point.
I am running a seed-to-voxel paired analysis comparing V1 vs V3, with age and sex included as second-level covariates.
For three significant clusters, I opened the REX Results window, then the plot window and exported both:
<ul data-spread="false">
<li>Export raw data</li>
<li>Export effect sizes</li>
</ul>
From the exported rawdata.mat file, I obtained a 46 × 3 matrix (23 subjects × 2 conditions, 3 significant clusters). The first 23 rows correspond to V1 and the last 23 rows correspond to V3.
For the first cluster, the average raw connectivity values are:
<ul data-spread="false">
<li>V1: 0.1904</li>
<li>V3: 0.1802</li>
</ul>
Thus, the raw functional connectivity appears to decrease slightly from V1 to V3.
However, the exported effectsize.mat file contains:
<pre dir="ltr"><code dir="ltr">effectsize.data =

Cluster1 Cluster2 Cluster3
V1 -0.1256 -0.1184 0.1258
V3 0.6076 0.6259 0.7745</code></pre>
For the same cluster, the "Display effects" plot therefore shows a large increase from V1 to V3, which seems inconsistent with the raw connectivity values.
My question is:
What exactly do the two bars in the "Display effects" plot represent?
I initially thought that the bars represented the average functional connectivity for each condition, but the exported raw data suggest that this is not the case.
Thank you very much for your time !
Best regards,
Adele
</blockquote>
 
</blockquote>

RE: Preprocessing and analysis of white matter fMRI data

snow balls — Mon, 03 Aug 2026 4:27:22 GMT

This is a really interesting research topic. Analyzing white matter signals and lesions definitely requires careful preprocessing and interpretation.

RE: URGENT HELP : Clarification about "Display effects" in seed-to-voxel analysis

Alfonso Nieto-Castanon — Sun, 02 Aug 2026 10:20:15 GMT

Dear Adele,
Effect-size refers to the effects estimated in your particular second-level model and used for statistical inference. In your case, for example, with a paired analysis comparing V1 vs V3 while controlling for age and gender, effect sizes represent the "adjusted means" in your model (i.e. the average difference between V1 and V3 estimated at the zero-level of your covariates; e.g. if your age/gender covariates are centered the zero-level of those covariates will correspond to the average age/gender in your sample)
More generally, if your second level model design matrix is X, your between-subjects contrast vector is C, and your between-conditions vector is M, then the second-level analysis will fit to your data matrix Y a model of the form:
Y ~ X*B 
where B is the estimated matrix of regressor coefficients. The "effect-sizes" displayed in CONN's plot always correspond to the elements of
effects = C*B*M'
In your particular case, X=[Subjects Age Gender], Y=[V1 V3], C=[1 0 0] and M=[-1 1], so the effect sizes are displaying the difference between B(1,1) and B(1,2), containing respectively the estimated adjusted means from your data Y.
Hope this helps
Alfonso 
Originally posted by adeleh:
<blockquote>
Dear Alfonso,
I am currently finalizing the results for my PhD thesis, so I would be extremely grateful if you could help clarify this point.
I am running a seed-to-voxel paired analysis comparing V1 vs V3, with age and sex included as second-level covariates.
For three significant clusters, I opened the REX Results window, then the plot window and exported both:
<ul data-spread="false">
<li>Export raw data</li>
<li>Export effect sizes</li>
</ul>
From the exported rawdata.mat file, I obtained a 46 × 3 matrix (23 subjects × 2 conditions, 3 significant clusters). The first 23 rows correspond to V1 and the last 23 rows correspond to V3.
For the first cluster, the average raw connectivity values are:
<ul data-spread="false">
<li>V1: 0.1904</li>
<li>V3: 0.1802</li>
</ul>
Thus, the raw functional connectivity appears to decrease slightly from V1 to V3.
However, the exported effectsize.mat file contains:
<pre dir="ltr"><code dir="ltr">effectsize.data =

Cluster1 Cluster2 Cluster3
V1 -0.1256 -0.1184 0.1258
V3 0.6076 0.6259 0.7745</code></pre>
For the same cluster, the "Display effects" plot therefore shows a large increase from V1 to V3, which seems inconsistent with the raw connectivity values.
My question is:
What exactly do the two bars in the "Display effects" plot represent?
I initially thought that the bars represented the average functional connectivity for each condition, but the exported raw data suggest that this is not the case.
Thank you very much for your time !
Best regards,
Adele
</blockquote>

RE: URGENT HELP : Clarification about "Display effects" in seed-to-voxel analysis

Fenna Berends — Fri, 31 Jul 2026 14:04:10 GMT

Dear Adele,
 
I saw your message and was struggling with kind of the same thing a few days ago for my thesis.
I found this in the Conn handbook (see screenshot): Click on ‘plot effects’ to compute effect-sizes (e.g. connectivity values within each group) averaged across all voxels within each significant cluster.
So I think effect size is average connectivity within each group. Maybe it helps!
 
Kind regards,
 
Fenna

URGENT HELP : Clarification about "Display effects" in seed-to-voxel analysis

adeleh — Fri, 31 Jul 2026 8:38:12 GMT

Dear Alfonso,
I am currently finalizing the results for my PhD thesis, so I would be extremely grateful if you could help clarify this point.
I am running a seed-to-voxel paired analysis comparing V1 vs V3, with age and sex included as second-level covariates.
For three significant clusters, I opened the REX Results window, then the plot window and exported both:
<ul data-spread="false">
<li>Export raw data</li>
<li>Export effect sizes</li>
</ul>
From the exported rawdata.mat file, I obtained a 46 × 3 matrix (23 subjects × 2 conditions, 3 significant clusters). The first 23 rows correspond to V1 and the last 23 rows correspond to V3.
For the first cluster, the average raw connectivity values are:
<ul data-spread="false">
<li>V1: 0.1904</li>
<li>V3: 0.1802</li>
</ul>
Thus, the raw functional connectivity appears to decrease slightly from V1 to V3.
However, the exported effectsize.mat file contains:
<pre dir="ltr"><code dir="ltr">effectsize.data =

Cluster1 Cluster2 Cluster3
V1 -0.1256 -0.1184 0.1258
V3 0.6076 0.6259 0.7745</code></pre>
For the same cluster, the "Display effects" plot therefore shows a large increase from V1 to V3, which seems inconsistent with the raw connectivity values.
My question is:
What exactly do the two bars in the "Display effects" plot represent?
I initially thought that the bars represented the average functional connectivity for each condition, but the exported raw data suggest that this is not the case.
Thank you very much for your time !
Best regards,
Adele

RE: ERROR: Mean functional file not found

shayla yonce — Tue, 28 Jul 2026 14:02:52 GMT

Did you ever find the solution to this problem?

Interpretation REX / Plot effects

Fenna Berends — Mon, 27 Jul 2026 13:56:42 GMT

Dear Alfonso,
For my research I am looking into functional connectivity differences between dieting and non-dieting individuals, for calorie labeled food cues and unlabeled food cues. I ran the complete analysis (seed-to-voxel), but am looking for a way to create bar plots for connectivity differences for each condition/group for my right Ventral Striatum - Anterior Cinculate Cortex cluster. To illustrate, I am looking for a way to graph average connectivity values for dieters: unlabeled food cue, dieters: calorie labeled food cue and non-dieters: unlabeled food cues and dieters: calorie labeled food cues. I see that I can get a graph with 'Effect Size' with the function 'Plot Effects'. However, I cannot find in the handbook or on other sources what exactly these 'effect sizes' mean within this function, what the confidence intervals are (for example 95% CI) nor a way to extract all individual connectivity scores within groups / calories so that I could calculate it manually. Could you help me out with this matter? Is it indeed the case that 'Plot Effects' show connectivity differences for my cluster between groups and conditions? 
It would help me a lot! Thank you in advance! I added an image to illustrate, and used standard settings for cluster based inferences (voxel treshold p < .001 two-sided, cluster treshold p < .005 parametric stats)
Have a great week!
Kind regards, 
Fenna

Mixed ANCOVA with session-specific covariate

Pavel Hok — Thu, 23 Jul 2026 14:16:28 GMT

Dear CONN Experts,
I would like to evaluate a group*condition interaction (2 groups with 2 exams each) with a second-level covariate. I believe it would be an example of a mixed 2x2 ANCOVA design (subject effects [1 -1 0] with between-condition contrast [1 -1]). Actually, the covariate is not a nuisance covariate but rather a change in behavioral scores between the sessions. Hence, I am more interested in the interaction covariate*condition, i.e., with subject effects set to [0 0 1]. However, I am concerned that the covariate (change in a score) is not really meaningful if conditions are modelled separately.
One solution would be to somehow include a session-specific covariate (i.e., original scores per session). I could not find such design description in the documentation and I searched the forum to find a several related posts still waiting for reply:
https://www.nitrc.org/forum/message.php?msg_id=38860 https://www.nitrc.org/forum/message.php?msg_id=40691 https://www.nitrc.org/forum/message.php?msg_id=41198
A generative AI suggested to include the session-specific covariate as a first-level covariate set to a constant value per subject. Unfortunately, I found no such recommendation here.
Thanks in advance!
Kind regards
Pavel