Constructing Datasets for MVPA Analysis

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.

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.

Bradley Buchsbaum

2025-03-20

Creating a Real Volumetric (Image-Based) Dataset

Creating a Real Surface-Based Dataset

Notes