Welcome to giga-connectome’s documentation!
Functional connectivity is a common approach in analysing resting state fMRI data. The Python tool Nilearn provides utilities to extract and denoise time-series on a parcellation. Nilearn also has methods to compute functional connectivity. While Nilearn provides useful methods to generate connectomes, there is no standalone one stop solution to generate connectomes from fMRIPrep outputs. giga-connectome (a BIDS-app!) combines Nilearn and TemplateFlow to denoise the data, generate timeseries, and most critically giga-connectome generates functional connectomes directly from fMRIPrep outputs. The workflow comes with several built-in denoising strategies and there are several choices of atlases (MIST, Schaefer 7 networks, DiFuMo, Harvard-Oxford). Users can customise their own strategies and atlases using the configuration json files.
giga-connectome is tested on fMRIPrep LTS (long-term support) 20.2.x. Currently, giga-connectome fully supports outputs of fMRIPrep LTS. For fMRIPrep 23.1.0 and later, giga-connectome does not support ICA-AROMA denoising, as the strategy is removed from the fMRIPrep workflow.
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