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June 08, 2021 10:06PM
I am exploring the BOLD5000 dataset (Chang et al., 2019; link to webpage; link to paper). I am considering how to model it to get a beta map for each stimulus, which would be the input for further analysis.

The dataset has four participants who each participated in a slow event related design over ~16 sessions of ~10 runs each. Over this time, they viewed ~5000 distinct images. If it were concatenated, the full dataset has on the order of 30,000 time points. While compressed and represented as 16-bit integers, the functional data for one subject is about 20GB as nii.gz--concatenation converts to 32-bit floats while uncompressing.

Of course, since it is slow and event related, I might just extract some volumes for analysis without modeling. But I am interested in thinking in how to approach the modeling problem at this scale. Since the images are all distinct, it seems that modelling all sessions/runs at once would be ideal.
Subject Author Posted

Modeling the BOLD5000 dataset

ChrisCox June 08, 2021 10:06PM

Re: Modeling the BOLD5000 dataset

Gang June 09, 2021 05:38PM

Re: Modeling the BOLD5000 dataset

ChrisCox June 09, 2021 09:01PM

Re: Modeling the BOLD5000 dataset

rick reynolds June 10, 2021 11:59AM

Re: Modeling the BOLD5000 dataset

ChrisCox June 10, 2021 03:07PM

Re: Modeling the BOLD5000 dataset

RWCox June 10, 2021 11:17AM

Re: Modeling the BOLD5000 dataset

ChrisCox June 10, 2021 02:56PM



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