neuroim2 (version 0.8.5)

SparseNeuroVec-class: SparseNeuroVec Class

Description

A class representing a sparse four-dimensional brain image, optimized for efficient storage and access of large, sparse neuroimaging data.

Constructs a SparseNeuroVec object for efficient representation and manipulation of sparse neuroimaging data with many zero or missing values.

Usage

SparseNeuroVec(data, space, mask, label = "")

Value

A SparseNeuroVec object, containing the sparse neuroimaging data, mask, and associated NeuroSpace information.

Arguments

data

A matrix or a 4-D array containing the neuroimaging data. The dimensions of the data should be consistent with the dimensions of the provided NeuroSpace object and mask.

space

A NeuroSpace object representing the dimensions and voxel spacing of the neuroimaging data.

mask

A 3D array, 1D vector of type logical, or an instance of type LogicalNeuroVol, which specifies the locations of the non-zero values in the data.

label

Optional character string providing a label for the vector

Slots

data

A matrix where each column represents a non-zero vector spanning the fourth dimension (e.g., time series for each voxel). Rows correspond to voxels in the sparse domain defined by the mask.

Inheritance

SparseNeuroVec inherits from:

• NeuroVec: Base class for 4D brain images

• AbstractSparseNeuroVec: Provides sparse representation framework

• ArrayLike4D: Interface for 4D array-like operations

Details

SparseNeuroVec objects store data in a compressed format, where only non-zero values are retained. This approach significantly reduces memory usage for sparse brain images. The class leverages the mask and mapping from its parent class AbstractSparseNeuroVec to efficiently manage the spatial structure of the data.

See Also

AbstractSparseNeuroVec-class for the parent sparse representation class. NeuroVec-class for the base 4D brain image class.

Examples

# Create a sparse 4D brain image
mask <- LogicalNeuroVol(array(runif(64*64*32) > 0.7, c(64,64,32)), NeuroSpace(c(64,64,32)))
data <- matrix(rnorm(sum(mask) * 100), nrow=sum(mask), ncol=100)
sparse_vec <- SparseNeuroVec(data=data, mask=mask, space=NeuroSpace(dim=c(64,64,32,100)))

# Access a subset of the data
subset <- sparse_vec[,,, 1:10]


bspace <- NeuroSpace(c(10,10,10,100), c(1,1,1))
mask <- array(rnorm(10*10*10) > .5, c(10,10,10))
mat <- matrix(rnorm(sum(mask)), 100, sum(mask))
svec <- SparseNeuroVec(mat, bspace, mask)
length(indices(svec)) == sum(mask)