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.
Guía validada por dominio para decisiones de preprocesamiento de resonancia magnética funcional: corrección de movimiento, sincronización de cortes, normalización espacial, suavizado, regresión de confusión y control de calidad Fuente: haoxuanlithuai/awesome_cognitive_and_neuroscience_skills.