Load confound

May 26, 2021 1 min read 👏

Load a sensible subset of the fMRI confounds generated with fMRIprep in python (Esteban et al., 2018). The predefined denoising strategies are all adapted from Ciric et al. 2017. Check the docstring of each strategy for more info and a list of parameters. It is also possible to fine-tune a subset of noise components and their parameters.

This package is at an alpha stage of development. The API may still be subject to changes. The beta release is planned for May 2021.

Reference: Ciric R, Wolf DH, Power JD, Roalf DR, Baum GL, Ruparel K, Shinohara RT, Elliott MA, Eickhoff SB, Davatzikos C., Gur RC, Gur RE, Bassett DS, Satterthwaite TD. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage. 2017. doi:10.1016/j.neuroimage.2017.03.020

Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, Kent JD, Goncalves M, DuPre E, Snyder M, Oya H, Ghosh SS, Wright J, Durnez J, Poldrack RA, Gorgolewski KJ. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Meth. 2018; doi: 10.1038/s41592-018-0235-4

Project Author(s)

load_confounds team

https://github.com/SIMEXP/load_confounds

This post was automatically generated by Hao-Ting Wang

Edit this page

Data Analysis Human Neuroscience Software

Related