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Description: BASC is a technique that allows you to assess the stability of your functional image parcellations at the individual level by performing a circular block bootstrap over the timecourse. BASC also creates group level functional parcellations, and by bootstrapping across the individual-wise stability maps, allows you to assess how representative your group level maps are of the individuals in your cohort. Importantly, this method is wholly data derived and does not place group level constraints to extract the individual level parcels.
TODO: At BrainHack we are working on two features: (1) Outputting individual level parcellations for individual-level graph theoretical analysis in PyNets. (2) Refactoring code to speed up BASC calculations.
The text was updated successfully, but these errors were encountered:
Aki Nikolaidis & Pierre Bellec
The text was updated successfully, but these errors were encountered: