FMRIprep: a robust preprocessing pipeline for task-based and resting-state fMRI data
Year: 2017
Type: other
Abstract: The statistical analysis of functional MRI requires suitable data, which is prepared with pre-processing workflows [1]. These workflows typically include head-motion and distortion corrections, intra-subject registration, spatial normalization, etc. [2]. However, these workflows remain largely subordinated to the features of the input data, the subsequent statistical analysis tool of choice, the investigator's preferences. Moreover settings and details are rarely reported in published literature. FMRIprep is a software instrument for fMRI preparation that aims at the standardization of fMRI preprocessing. The tool is designed around three principles: 1) robustness to the heterogeneity of input data (e.g. acquisition parameters, protocol design, etc.); 2) ease of use, relying on the BIDS standards [3] for the input data and the resulting derivatives; and 3) a "glass-box" philosophy that stresses the need for visually assessing the outcomes before analysis, and the transparency on the workflow design and tooling configuration. [1] Ashburner J et al. (2009), Preparing fMRI data for statistical analysis, fMRI Techniques and Protocols (Neuromethods), Humana Press, doi:10.1007/978-1-60327-919-2_6; [2] Strother S. (2006), Review of Preprocessing Steps for BOLD fMRI, IEEE EMB Mag, 25(2):27-41. doi:10.1109/MEMB.2006.1607667; [3] Gorgolewski KJ et al. (2016), The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 3:160044. doi:10.1038/sdata.2016.44; [4] Poldrack RA et al. (2013), Toward open sharing of task-based fMRI data: the OpenfMRI project. Frontiers in Neuroinformatics 7:12. doi:10.3389/fninf.2013.00012; [5] Gorgolewski KJ et al. (2016), Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Zenodo [Software]. doi:10.5281/zenodo.50186; [6] Gorgolewski KJ et al. (2016), BIDS Apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods [Internet]. bioRxiv. p. 079145. doi:10.1101/079145;
Institution: Stanford University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W2746726431