I’ve been working on a source localization analysis using mne-Python, and I need some help with an issue that is probably related to the vertices. Here’s the context:

I’m examining the difference in neural activity between two conditions. Using cluster-based permutation tests on ERP levels, I found a significant cluster over both hemispheres in occipital regions between 200 and 380 ms after stimulus onset. Now, I aim to identify brain areas contributing to this ERP difference by testing whether the mean activity within specific, preselected ROIs differs in my detected time window. I’m using a standard source space model for all analyses. After making the forward solution, computing the covariance matrix, creating the inverse operators, and applying the inverse operators to get the source estimates, I get the following array Y (Vertices, Time Points in ms, Subjects, Conditions). In my case, Y(20484, 180, 38, 2).

So far so good…

In the next step, I selected an atlas (HCPMMP1) and a specific ROI (e.g., V1), extracted the relevant vertices for V1, and filtered these vertices from all my vertices in Y. I then summarized the activity over the time series for each person in the selected vertices to get the mean activity within the 180 ms of interest in V1. Now, it is possible to test the differences of the mean activity between both conditions in my ROI with a t-test, for example.

Additionally, I tried to visualize the mean activity in my ROI over the time course of 180 ms. But the problem is that regardless of the region I select, I always find a significant difference in the time window. Also, the mean activity plots always show the same patterns regardless of the ROI selected. It doesn’t matter if I’m analyzing a frontal region or a visual region. Based on the task, there should be no difference in the mean activity between both conditions in frontal regions.

I suspect there is an issue with the vertices or how they are selected from my source estimates. The standard brain has 20484 vertices, which I believe don’t match those of the HCPMMP1 atlas. Is this possible? When selecting a sample region (e.g., V1), it might have, for example, 9300 vertices, but their numbering ranges from 36 to 162,000. But I’m only selecting vertices up to 20484 from my array Y because it only contains 20484 vertices, meaning I’m not selecting the relevant 9300 vertices of the ROI but only 1100.

Here is an example of the relevant part of my code:

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Does my suspicion make sense, and could this lead to the same pattern? Does anyone have any suggestions to resolve this issue?

I hope I was able to explain my problem sufficiently.

Thanks for your time!

Best regards, Chris