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[Preprint]. 2023 Jul 5:2023.04.18.537240. Originally published 2023 Apr 19. [Version 3] doi: 10.1101/2023.04.18.537240

Figure 1. Workflow for post-fMRIPrep time series extraction with Nilearn tools.

Figure 1.

The Python-based workflow describes the basic procedure to generate functional connectomes from fMRIPrep outputs with a Nilearn data loading routine (e.g., NiftiMapsMasker or NiftiLabelsMasker), fMRIPrep confounds output retrieval function (e.g., load_confounds_strategy), and connectome generation routine (ConnectivityMeasure). Path to the preprocessed image data is passed to load_confounds_strategy and the function fetches the associated confounds from the .tsv file. The path of an atlas and the path of the preprocessed image file is then passed to the masker, along with the confounds, for time series extraction. The time series are then passed to ConnectivityMeasure for generating connectomes.