vertex_analysis {VertexWiseR}

Vertex-wise analysis

Description

Fits a linear or linear mixed model with the cortical or hippocampal surface data as the predicted outcome, and returns cluster-thresholded (Random field theory) t-stat map selected contrast.

Usage

vertex_analysis(
  model,
  contrast,
  random,
  surf_data,
  p = 0.05,
  atlas = 1,
  smooth_FWHM,
  VWR_check = TRUE
)

Arguments

model

An N X V data.frame object containing N rows for each subject and V columns for each predictor included in the model. This data.frame should not include the random effects variable.

contrast

A numeric vector or object containing the values of the predictor of interest. The cluster-thresholded t-stat maps will be estimated only for this predictor

random

An object containing the values of the random variable (optional)

surf_data

A matrix object containing the surface data, see SURFvextract() or HIPvextract() output format.

p

A numeric object specifying the p-value to threshold the results (Default is 0.05)

atlas

A numeric integer object corresponding to the atlas of interest. 1=Desikan, 2=Schaefer-100, 3=Schaefer-200, 4=Glasser-360, 5=Destrieux-148.

smooth_FWHM

A numeric vector object specifying the desired smoothing width in mm

VWR_check

A boolean object specifying whether to check and validate system requirements. Default is TRUE.

Details

The function imports and adapts the 'BraiStat' Python library.

Output definitions:

• nverts: number of vertices in the cluster

• P: p-value of the cluster

• ⁠X, Y and Z⁠: MNI coordinates of the vertex with the highest t-statistic in the cluster.

• tstat: t statistic of the vertex with the highest t-statistic in the cluster

• region: the region this highest -statistic vertex is located in, as determined/labelled by the selected atlas

Value

A list object containing the cluster level results, thresholded t-stat map, and positive, negative and bidirectional cluster maps.

Examples

demodata = readRDS(system.file('demo_data/SPRENG_behdata_site1.rds', 
package = 'VertexWiseR'))[1:100,]
CTv = readRDS(file = url(paste0("https://github.com",
"/CogBrainHealthLab/VertexWiseR/blob/main/inst/demo_data/",
"SPRENG_CTv_site1.rds?raw=TRUE")))[1:100,]

vertexwise_model=vertex_analysis(model=demodata[,c(2,7)], 
contrast=demodata[,7], surf_data = CTv, atlas=1,p = 0.05, 
VWR_check=FALSE)

#Description of the output:
#vertexwise_model$cluster_level_results