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Bayesian_VLSM_R

Contributor: Marie Pittet, [email protected], Jan 2025

Full R-based Bayesian Voxel-Based Lesion-Symptom Mapping with publication ready plots of selected slices. This is particularly suitable for the exploration of the effects of lesions on behavioral outcomes (symptoms) in small sample sizes. A first analysis using the frequentist approach (t-tests) is performed for comparison. The second approach is the bVLSM using bayesian t-tests (with the "BayesFactor" package), with Cauchy priors. BVLSM -also called Bayesian Lesion-Deficit Inference (BLDI), does not really benefit from lesion-volume correction (Sperber et al., 2023), hence it is not applied here. A visualization in the axial plane is provided, as well as a visualization of selected slices in the sagittal, coronal, and axial planes. Additionnally, the significant voxels are attributed to Talairach atlas' regions or Automated anatomical labeling (AAL) regions in a table for easy interpretation. The AAL atlas had to be resampled to fit the MNI1mm template used for normalization.

Input:

  • A 4d file containing the normalized lesions of all patients
  • A vector containing the behavioral scores of interest of all patients. They must appear in the same order as the patients' lesion file so that the lesions and the behavioral scores are correctly attributed to the right patient. Adapt the symptom_scores variable to reflect your behavioral scores of interest.
  • For visualization, selected slices are given as examples but can be modified by modifying the numbers in the selected_slices variable for the axial plane, or in the x_slices, y_slices, and y_slices for the visualization in 3 planes.

Output:

  • A map of log10 Bayesian Factor values (logBF) instead of BF factors. This is because the latter can range between 0 and very large values with non-linear interpretation and is not easy to handle within visualization softwares. The logBF map can be opened with neuroimaging visualization softwares (such as ITK-SNAP for a free option) on top of the brain template used for lesion normalization (e.g. MNI152).
  • a publication-ready map of the logBF map on top of the MNI template in one plane (axial), or three planes (sagittal, axial, coronal).
  • a table with the attribution of significant voxels to Talairach atlas regions or AAL regions.

Log(BF) values are usually interpreted as follows (Krass & Raftery, 1995):

  • logBF = 0 no evidence for either the null or the alternative hypothesis
  • logBF = [0, 0.5[ weak evidence for alternative hypothesis
  • logBF = [0.5, 1[ Substantial evidence for alternative hypothesis
  • logBF = [1, 1.5[ Strong evidence for alternative hypothesis
  • logBF = [1.5, 2[ Very strong evidence for alternative hypothesis
  • logBF = [2, inf] Decisive evidence for alternative hypothesis

References: Sperber, C., et al. (2023). Bayesian lesion-deficit inference with Bayes factor mapping: Key advantages, limitations, and a toolbox. NeuroImage.
Talairach, J., & Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the Human Brain. Thieme Medical Publishers.
Tzourio-Mazoyer, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage.
Kass, R. E., & Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association.