fMRI preprocessing transforms raw scanner data into a form suitable for statistical analysis. Unlike generic data cleaning, every preprocessing decision in fMRI involves domain-specific trade-offs: choosing the wrong step order can introduce artifacts that mimic neural signal, smoothing at the wrong scale destroys the spatial information needed for multivariate analyses, and failing to correct for susceptibility d...
A competent programmer without neuroimaging training would get many of these decisions wrong. This skill encodes the domain knowledge required to make correct preprocessing choices for different analysis goals.
This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.
Conseils validés par domaine pour les décisions de prétraitement IRMf : correction de mouvement, synchronisation des tranches, normalisation spatiale, lissage, régression de confusion et contrôle qualité Source : haoxuanlithuai/awesome_cognitive_and_neuroscience_skills.