Welcome to giga_connectome’s documentation!
Functional connectivity is a common approach in analysing resting state fMRI data. Python tool Nilearn provides utilities to extract, denoise time-series on a parcellation and compute functional connectivity. Currently there’s no standalone one stop solution to generate connectomes from fMRIPrep outputs. This BIDS-app combines Nilearn, TemplateFlow to denoise the data and generate timeseries and functional connectomes directly from fMRIPrep outputs. The workflow comes with several built in denoising strategies and three choices of atlases (MIST, Schaefer 7 networks, DiFuMo). Users can customise their own strategies and atlases using the configuration json files.
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