Dear all, 

We are running a whole-brain dynamic functional connectivity (dFC) to explore potential brain nodes (attractors). Here’s a summary of our process and the issues we’re encountering:
1. We conducted a whole-brain dFC study using CONN for preprocessing and denoising with a specific band-pass filter.
2. We then resized the voxels to 6mm and applied the attractors code. This analysis revealed two significant clusters.

We now aim to perform a seed-based analysis using these specific ROIs, incorporating a sliding-window approach in CONN to capture the intrinsic dynamics similar to our initial whole-brain analysis. To achieve this, I have attempted to:

• Add the denoised and resized brain images and skip the preprocessing and denoising steps.


• Modify the setup section to include the sliding windows and ROIs.


• Run the seed-based analysis as usual.

However, the results from the second-level analysis appear unusual, with all clusters showing the same size. I believe there may be an error in my approach, but I can't figure it out.

Could you please help with the following:

- Are preprocessing and denoising steps necessary for performing a sliding-window analysis?

- Should I prepare the data in a specific way?

- How should I interpret the second-level results from this analysis?

Thank you for your assistance.

Best regards.
Sofia Amaoui,