A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn

Jupyter Notebook Python Submitted 13 May 2023 • Published 19 June 2023
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Authors

Hao-Ting Wang (0000-0003-4078-2038), Steven L. Meisler (0000-0002-8888-1572), Hanad Sharmarke, Natasha Clarke, François Paugam, Nicolas Gensollen (0000-0001-7199-9753), Christopher J. Markiewicz (0000-0002-6533-164X), Bertrand Thirion (0000-0001-5018-7895), Pierre Bellec (0000-0002-9111-0699)

Citation

Wang et al., (2023). A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn. NeuroLibre Reproducible Preprints, 12, https://doi.org/10.55458/neurolibre.00012

@article{Wang2023, doi = {10.55458/neurolibre.00012}, url = {https://doi.org/10.55458/neurolibre.00012}, year = {2023}, publisher = {NeuroLibre}, pages = {12}, author = {Hao-Ting Wang and Steven L. Meisler and Hanad Sharmarke and Natasha Clarke and François Paugam and Nicolas Gensollen and Christopher J. Markiewicz and Bertrand Thirion and Pierre Bellec}, title = {A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn}, journal = {NeuroLibre Reproducible Preprints} }
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reproducibility nilearn fMRIPrep nuisance regressor resting-state fMRI functional connectivity

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