In bbuchsbaum/rMVPA: Multivoxel Pattern Analysis in R

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This vignette demonstrates how to create real neuroimaging datasets for MVPA analysis. The examples below show how to create both volumetric (image-based) and surface-based datasets using functions defined in dataset.R. Note that the code examples are set to not evaluate (eval=FALSE) since they refer to file paths that must be replaced with actual data files.

Creating a Real Volumetric (Image-Based) Dataset

This example assumes you have a 4D fMRI file ("bold.nii.gz") and a corresponding 3D brain mask file ("mask.nii.gz").

library(neuroim2)

# Read the fMRI data as a NeuroVec object using neuroim2::read_vec
train_neurovec <- neuroim2::read_vec("path/to/bold.nii.gz", mode = "normal")

# Read the brain mask and create a NeuroVol object
mask_vec <- neuroim2::read_vec("path/to/mask.nii.gz", mode = "normal")
mask_vol <- NeuroVol(as.array(mask_vec), NeuroSpace(dim(mask_vec), spacing = c(2, 2, 2)))

# Create the MVPA image dataset
real_dataset <- mvpa_dataset(train_data = train_neurovec, mask = mask_vol)

# Display dataset details
print(real_dataset)

Creating a Real Surface-Based Dataset

This example assumes you have cortical geometry stored in a file (e.g., "subject.lh.smoothwm.asc") and a signal matrix in CSV format ("surface_data.csv"). The signal matrix should have dimensions corresponding to the number of vertices and the number of observations.

## remotes::install_github("bbuchsbaum/neurosurf")
library(neurosurf)

# Load the cortical geometry
geom <- read_surf_geometry("path/to/subject.lh.smoothwm.asc")

# Read the surface data from a CSV file
# The CSV should not have a header and have dimensions: number of vertices x number of observations
data_matrix <- as.matrix(read.csv("path/to/surface_data.csv", header = FALSE))

# Verify that the number of rows in the data matches the geometry
nvert <- nrow(neurosurf::vertices(geom))
if(nrow(data_matrix) != nvert) {
  stop("The number of vertices in the data does not match the geometry.")
}

# Create a NeuroSurfaceVector using the geometry and the data matrix
real_neurosurf <- NeuroSurfaceVector(geom, 1:nvert, data_matrix)

# Create the MVPA surface dataset; if no mask is provided, one is generated automatically
real_surface_dataset <- mvpa_surface_dataset(train_data = real_neurosurf, name = "lh")

# Display dataset details
print(real_surface_dataset)

Notes

• For volumetric datasets, the NeuroSpace object is used to define dimensions and voxel spacing.
• For surface datasets, ensure that the signal matrix and cortical geometry are compatible.

Replace the file paths with actual paths to your real data files before running the code.

bbuchsbaum/rMVPA documentation built on June 10, 2025, 8:23 p.m.