<p>You have one p-values for each ROI timecourse (for the largest cluster) indicating whether your conditions are exchangeable. If you want to correct for the 4 ROIs you could use any adequate multiple comparison correction, from classical Bonferroni (which might be too conservative as the tests are likely not completly independent) to Benjamini-Hochberg style FDR correction (but applied to the cluster permutation test p-values).</p>

<p>This leaves us with the question which p-values count, one per ROI (the largest cluster, i.e. smallest p-value) or the p-values for all supra cluster-threshold clusters. I would think the former is more appropriate, as you already corrected for the multiple clusters.</p>

<p>That is, for the Bonferroni correction you would multiply your p-values by 4, (or identically, divide your critical alpha by 4).</p>

<p>This solution is incomplete, as I do not know how to get corrected p-values for the second largest cluster with FDR or Holm-Bonferroni.</p>

CC BY-SA 4.0
2019-08-08T18:09:32.717Z
2019-08-08T18:09:32.717Z
behinger
<p>I fully endorse Benedikt's answer. Further, you have to specify the question you are asking.</p>

<p>Let's say you want to know whether activity in a face recognition task differs between conditions in FFA, PPA, STS, and FEF and that the <strong>location</strong> of the difference is what you want to know, then Bonferroni is the correction of choice. </p>

<p>In case you ask the question whether the activity in the brain differs <strong>somewhere</strong>, but you do not care where specifically, then false discovery rate correction (FDR) is the method of choice. </p>

<p>The former allows statements like it is FFA and not STS, the latter allows statements like the conditions lead to differential activity in a collection of areas, whose identity is not too certain. </p>

CC BY-SA 4.0
2019-08-08T19:59:05.590Z
2019-08-08T19:59:05.590Z
Peter König
<p>I would like to know how to correct a p value of a stiatistic when the same test was run over multiple regions of interest. The modality is MEG. 
So, I have 4 Regions Of Interests, and I ran the same cluster-based permutation test on the averaged timecourse of each ROI. I got more than 10 clusters, each with p &lt; 0.05, but how can I get the corrected p value considering there was more than one region? </p>

<p>Any help will be much appreciated. </p>

CC BY-SA 4.0
2019-09-09T22:41:47.180Z
You may find the following useful, but I would ask are you sure the program you are using does not already correct the p-values? http://www.fieldtriptoolbox.org/workshop/oslo2019/statistics/
Comte
<cognitive-neuroscience><methodology><eeg><statistics><meg>
correction for multiple comparison over many ROIs
Sophy
# correction for multiple comparison over many ROIs

(Asked by: username_0 on 2019-03-16)

I would like to know how to correct a p value of a stiatistic when the same test was run over multiple regions of interest. The modality is MEG.
So, I have 4 Regions Of Interests, and I ran the same cluster-based permutation test on the averaged timecourse of each ROI. I got more than 10 clusters, each with p < 0.05, but how can I get the corrected p value considering there was more than one region?

Any help will be much appreciated.

**Tags:** cognitive-neuroscience, methodology, eeg, statistics, meg

> **Comments:**
> **username_1**: You may find the following useful, but I would ask are you sure the program you are using does not already correct the p-values? http://www.fieldtriptoolbox.org/workshop/oslo2019/statistics/  

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## 2 answers

### Answer by username_2 on 2019-08-08. Score: 2

You have one p-values for each ROI timecourse (for the largest cluster) indicating whether your conditions are exchangeable. If you want to correct for the 4 ROIs you could use any adequate multiple comparison correction, from classical Bonferroni (which might be too conservative as the tests are likely not completly independent) to Benjamini-Hochberg style FDR correction (but applied to the cluster permutation test p-values).

This leaves us with the question which p-values count, one per ROI (the largest cluster, i.e. smallest p-value) or the p-values for all supra cluster-threshold clusters. I would think the former is more appropriate, as you already corrected for the multiple clusters.

That is, for the Bonferroni correction you would multiply your p-values by 4, (or identically, divide your critical alpha by 4).

This solution is incomplete, as I do not know how to get corrected p-values for the second largest cluster with FDR or Holm-Bonferroni.


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### Answer by username_3 on 2019-08-08. Score: 0

I fully endorse Benedikt's answer. Further, you have to specify the question you are asking.

Let's say you want to know whether activity in a face recognition task differs between conditions in FFA, PPA, STS, and FEF and that the **location** of the difference is what you want to know, then Bonferroni is the correction of choice.

In case you ask the question whether the activity in the brain differs **somewhere**, but you do not care where specifically, then false discovery rate correction (FDR) is the method of choice.

The former allows statements like it is FFA and not STS, the latter allows statements like the conditions lead to differential activity in a collection of areas, whose identity is not too certain.


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