We are performing site maintenance. Some features may not not be available at this time.

Hi,

Often times people use sliding-window analyses in the context of RRC analyses, which considerably reduces the storage requirement as for each temporal window and subject we would just compute a single ROI-to-ROI connectivity matrix. Using sliding-window analyses in the context of SBC analyses is more demanding in terms of storage, as for each temporal window and subject we would compute one separate whole-brain connectivity map for each seed. This is perfectly fine when you have one or a few seeds of interest a priori, but if you have defined many seeds of interest in your SBC analyses I imagine this could easily add up to the large storage demands (>1Tb) that you describe.

Hope this helps

Alfonso

Originally posted by sfeuer45:

Hello,

I have been trying to perform sliding window analysis using CONN after successfully completing gPPI analysis. With eight subjects, I attempted to complete sliding window analysis, where the window length is 20 TRs (pretty standard according to Savva et al 2019) and the window shift is 4 TRs (much higher than standard, because I was trying to avoid storage issues and this results in less windows). 

The result was 53 windows each for three sessions, across eight participants. I suppose this is quite a few, but this is less than I would expect if I stuck with standard values for window shift. However, when I ran 1st-level SBC analysis utilizing these windows, it took up 1.1 TB of storage on my machine.

This feels like a lot for such a widely-used method and would prevent me from doing any further analysis. Is it possible that I performed the analysis incorrectly? Or have others had this issue with sliding window analysis taking up a lot of storage? Happy to clarify any other parameters I used.