The neuroSCC package provides tools to preprocess and structure neuroimaging data
for functional data analysis using Simultaneous Confidence Corridors (SCCs). It wraps external packages
to prepare data from PET images, extract contours, generate meshes, and evaluate regions of statistical significance.
The methods implemented support both group comparisons and single-subject vs. group inference, following the methodology described in Wang et al. (2020) and the author's PhD thesis.
Maintainer: Juan A. Arias Lopez juanantonio.arias.lopez@usc.es (ORCID) [copyright holder]
Other contributors:
Virgilio Gomez Rubio Virgilio.Gomez@uclm.es (ORCID) [reviewer]
Pablo Aguiar Fernandez pablo.aguiar@usc.es (ORCID) [thesis advisor]
Andrew Haddon Kemp A.H.Kemp@swansea.ac.uk (ORCID) [thesis advisor]
This package serves as a bridge between neuroimaging file formats (e.g., NIfTI) and advanced
statistical tools like ImageSCC::scc.image. It includes the following key components.
Loading and cleaning PET image data.
Extracting ROIs and constructing functional data matrices.
Generating synthetic Poisson clones for 1-vs-group settings.
Extracting SCC-detected points and evaluating detection metrics.
neuroCleaner, databaseCreator, getPoints