Vertex-wise Random-Effects Meta-Analysis Across Studies

This function runs a vertex-wise random-effects meta-analysis across multiple studies (sites or cohorts). Is expects individual study results as they are outputted but `verywise::run_vw_lmm` or by `QDECR`.

Usage

run_vw_meta(
  stack,
  hemi,
  result_dirs,
  study_names,
  fs_template = "fsaverage",
  n_cores = 1,
  verbose = TRUE
)

Arguments

stack: : Character or numeric identifier for the fixed term to meta-analyise.
hemi: : Character string, hemisphere to analyze (`"lh"` or `"rh"`).
result_dirs: : Character vector of directories containing the results files of each study.
study_names: : Character vector of study names (must match the length of result_dirs).
fs_template: : Character string specifying the FreeSurfer template surface. The following values are accepted: * fsaverage (default) = 163842 vertices (highest resolution), * fsaverage6 = 40962 vertices, * fsaverage5 = 10242 vertices, * fsaverage4 = 2562 vertices, * fsaverage3 = 642 vertices
n_cores: : Integer, number of CPU cores to use for parallel processing (default: 1).
verbose: : Logical, verbose execution (default: TRUE)

Value

A named list with three FBM objects:

coef: Meta-analytic effect size estimates at each vertex.
se: Meta-analytic standard errors at each vertex.
p: Meta-analytic p-values at each vertex.

Additionally, these are exported as MGH files in the working directory.

Details

For each vertex, the function loads the effect size and standard error from each study, computes the variance, and runs a random-effects meta-analysis using rma. Results are saved as Filebacked Big Matrices (FBM) and as MGH files for downstream neuroimaging analysis.

Examples

if (FALSE) { # \dontrun{
run_vw_meta(
  stack = 1,
  hemi = "lh",
  result_dirs = c("study1/results", "study2/results"),
  study_names = c("Study1", "Study2"),
  fs_template = "fsaverage",
  n_cores = 4
)
} # }