NeuroSurfaceVector-class: NeuroSurfaceVector Class

In bbuchsbaum/neurosurf: Data structures and visualization for surface-based neuroimaging data

NeuroSurfaceVector Class

Description

Represents a 3D surface with multiple values per vertex.

Details

The NeuroSurfaceVector class extends the concept of NeuroSurface to handle multiple measurements for each vertex across the entire surface. Unlike NeuroSurface which stores a single value per vertex, NeuroSurfaceVector stores a matrix of values where columns represent different measures and rows correspond to vertices.

This structure is particularly useful for:

• Time series data where each column represents a different timepoint

• Multi-modal data where each column represents a different imaging modality

• Statistical results where columns represent different statistical parameters

• Feature vectors for machine learning applications

The data matrix organization (vertices as rows, measures as columns) facilitates efficient vertex-wise operations and analyses. This is in contrast to ROISurfaceVector where the matrix is transposed (measures as rows, vertices as columns).

Slots

geometry

SurfaceGeometry instance representing the surface structure

indices

Integer vector of valid surface node indices

data

Matrix of values, where columns represent different measures and rows correspond to surface nodes

Note

The number of rows in 'data' must match the number of nodes in 'geometry'.

See Also

SurfaceGeometry, NeuroSurface

Examples

# Create a simple tetrahedron mesh for the example
vertices <- c(
  0, 0, 0,
  1, 0, 0,
  0, 1, 0,
  0, 0, 1
)
triangles <- c(
  1, 2, 3,
  1, 2, 4,
  1, 3, 4,
  2, 3, 4
)

# Create mesh3d object
mesh <- rgl::mesh3d(vertices = vertices, triangles = triangles)

# Create a graph representation
edges <- rbind(
  c(1,2), c(1,3), c(1,4),
  c(2,3), c(2,4),
  c(3,4)
)
graph <- igraph::graph_from_edgelist(edges)

# Create a SurfaceGeometry object
geometry <- new("SurfaceGeometry",
               mesh = mesh,
               graph = graph,
               hemi = "left")

# Define indices for all vertices
indices <- 1:4

# Create a Matrix with multiple measures for each vertex
# Each row corresponds to a vertex, each column to a different measure
require(Matrix)
vertex_data <- Matrix(
  c(0.5, 1.2, 0.8,    # Measure 1 values for vertices 1-4
    0.7, 0.3, 1.5, 0.9,  # Measure 2 values for vertices 1-4
    1.1, 0.6, 0.4, 1.3), # Measure 3 values for vertices 1-4
  nrow = 4, ncol = 3,
  byrow = FALSE
)

# Create the NeuroSurfaceVector object
neuro_surface_vector <- new("NeuroSurfaceVector",
                         geometry = geometry,
                         indices = indices,
                         data = vertex_data)

# The data matrix now maps multiple values to each surface vertex
# Vertex 1 has values: 0.5, 0.7, 1.1
# Vertex 2 has values: 1.2, 0.3, 0.6
# etc.

bbuchsbaum/neurosurf documentation built on June 10, 2025, 8:22 p.m.