Chen, et al. (2018b). Handling Multiplicity in Neuroimaging through Bayesian Lenses with Hierarchical Modeling

Chen GC, Xiao Y, Taylor PA, Riggins T, Geng F, Redcay E, Cox RW (2018). Handling Multiplicity in Neuroimaging through Bayesian Lenses with Hierarchical Modeling

See also

-> brief entry

Tag	Label
FMRI paradigm:	task-block
FMRI dset:	EPI
Anatomical dset:	MPRAGE
Subject population:	human
Subject characteristic:
Subject age:	adult
Template space:
Template align method:
Tissue segmentation:
Tissue regression:
Comments:

runBGA.tcsh	An example of group level analysis with the “Bayesian multilevel” (BML) approach.
test_ToMI_1106.txt	The data table of subject information input into the BML analysis. See the associated paper for full description and generation.

These scripts describe different approaches for processing FMRI data with AFNI. Please read the comments at the tops of the scripts carefully, as well as the bioRxiv papers associated with each, in order to understand the steps.

#!/bin/tcsh

# Example of Bayesian multilevel (BML) modeling for group-level
# analysis.  Uses data table in standard AFNI format (similar to
# 3dMVM, 3dLME, etc.) that is in file "test_ToMI_1106.txt".
#
# To execute, type:
#   tcsh runBGA.tcsh
#
# Note that this program requires specific R packages like brms (and
# its dependencies) to run.  If packages are missing, please try
# running:
#   rPkgsInstall -pkgs ALL
# to install any missing ones.
#
# ver  : 1.1
# date : Oct 05, 2018
# auth : G Chen, JK Rajendra
#
#===================================================================

BayesianGroupAna.py                    \
    -dataTable test_ToMI_1106.txt      \
    -y zscore                          \
    -x total                           \
    -prefix test                       \
    -chains 4 -iterations 1000         \
    -RData