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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.