Bayesian meta-analysis of the cortical surface (BMACS) is a novel model-based meta-analysis method that performs meta-analysis on the cortical surface. The core model is based on the log-Gaussian Cox processes (LGCP), and is performed through the integrated nested Laplace approximation (INLA) for parameter estimation. This repository contains the code to perform BMACS for human reasoning.
TL;DR To replicate the analysis, execute *.Rmd files in scripts folder in numerical order.
Folder description:
data: Current study uses BMACS to study human reasoning (inductive/deductive). To perform the meta-analysis, coordinates from 76 studies were collected and released indata/data_coords_*.csv.scripts:*.Rmdfiles contain codes used in the analysis. See*.htmlfiles for rendered version.output: various result files are located here. To see the resulting reasoning maps estimated by BMACS, check*.dlabel.niifiles where the brain is represented on the fs_LR 32k meshes.R: functions used in the current analysis.external: external software/data used in the current analysis.
Some essential software that were used in BMACS are listed below.
INLA: To run BMACS using R-INLA
inlabru: To simulate lgcp
gifti, freesurferformats, ciftiTools: To read/write brain data files
here, glue, tidyverse: data-analysis tools
The RegistrationFusion from CBIG was used to project coordinates from MNI space to fsaverage space.
The package is modified and redistributed in the current repository, which follows MIT license.
MATLAB and Freesurfer need to be installed.
The wb_command and surface meshes are needed to produce final output on the fs_LR surface, following the recommendation of HCP wiki.
Connectome workbench can be downloaded through here.
The data can be acquired through running scripts/003_Posterior-Analysis.Rmd or you can download from here.
see License.