SCCneuroimage

SUMMARY

In this GitHub repository you will find everything you need to replicate results obtained in: "Arias, J. A., Cadarso Suarez, C., & Fernandez Aguiar, P. (Under review). Simultaneous Confidence Corridors in neuroimage data analysis: applications for Alzheimer's Disease diagnosis" including:

Positron Emission Tomography (PET) data extracted from the Alzheimer's Disease Neuroimaging Initiative database.
PET data and ROIs for simulation study.
Matlab code for the pre-processing of these images.
R scripts for the ellaboration of SCCs for neuroimaging data.
R scripts for the evaluation of SCCs compared to classical SPM for these neuroimaging datasets.

KEY STEPS:

PET images pre-processing

Realignement
Unwrapping
Corregistration
Normalization
Masking

Import PET images into R

Import one (1) individual
Loop for all participants
Create a complete and clean database (vars: PPT, group, sex, age, z, x, y, PET)
Mean average normalization

Calculate Simultaneous Confidence Corridors (SCC)

List of PPTs according to their group/sex/age or a combination
Convert data to a Functional Data setup (SCC matrices)
Extract contours for PET data
Compute triangulations over these contours
Construct SCCs for one-sample case
Construct SCCs for the difference between estimated mean functions

Visualization of regions suffering AD-induced neural loss

Visualize SCC for the difference between two groups
Overlay points falling above or below estimated confidence corridors
Exploration of results

Evaluation of obtained predictions

CHEATSHEET:

Open "MASTERSCRIPT (for simulations).R"
Press Enter in everything, un-comment lines 36 & 37 if necessary, modify param.z if necessary.
Create folders as requested (yet to be improved) and copy/paste SCC results if you have them already (as it is with my case).
Also create SPM folder with binary.nii files. This has to be carried out manually.

iguanamarina/SCCneuroimage