Post Processing

Analyses performed on CUBIC:

• ModelArray (version 0.0.0.9000); ran linear model on ASL data (n = 209; 5 participants from the full 214 with processed ASL were removed after QA checks - basically those with QEI below 0.6 and rmsd over 0.3 were manually inspected for quality, those who did not pass manual inspection were excluded. Analyses and related files at cbica/projects/GRMPY/project/curation/testing/stats_ModelArray).
  1. Followed documentation from Model Array demo and quick start guide
  2. Used Confixel for conversion from .nii to .h5; final convoxel .csv available at cbica/projects/GRMPY/project/curation/testing/stats_ModelArray/GRMPY_convoxel_meanCBF_n209_finalversion.csv
  3. Ran qsub_call.sh, which called runRscript.sh which called the actual RScript.R. This used the singularity image cbica/projects/GRMPY/project/curation/testing/stats_ModelArray/modelarray.sif
  4. R script used the formula

      CBF ~ age (negligible non-linear effects) + sex + basilQEI + rmsd + als_score/bdi_score/scared_score_total/ari_score
     

     and function as below:

   mylm <- ModelArray.lm(formula, data = modelarray, phenotypes = phenotypes, scalar = "CBF",
                     element.subset = element.subset, full.outputs = full.outputs,
                     correct.p.value.terms = c("fdr", "bonferroni"),
                     correct.p.value.model = c("fdr", "bonferroni"),
                     pbar = TRUE, n_cores = num.cores) #n_cores = 4, element.subset = all voxels, full.outputs = TRUE
  

  1. Converted from .h5 to results folder named after date, containing all niftis, again using Confixel.
  2. No significance found for clinical scales: including fdr_correction images, cluster correction via easythresh and viewing via mricron.

  Results directories are pointed to in the file ReadMeResultFolderDescriptions.md in the stats_ModelArray directory.

  Results further confirmed by vector testing in R:

   GM (GMmeanCBF from ASLPREP_QC.csv) ~ age + sex + basilQEI + rmsd + als_score/bdi_score/scared_score_total/ari_score
  

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