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:

1. Open "MASTERSCRIPT (for simulations).R"

2. Press Enter in everything, un-comment lines 36 & 37 if necessary, modify param.z if necessary.

3. Create folders as requested (yet to be improved) and copy/paste SCC results if you have them already (as it is with my case).

4. Also create SPM folder with binary.nii files. This has to be carried out manually.