Choosing a Backend

In neuroim2: Data Structures for Brain Imaging Data

knitr::opts_chunk$set(collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE)
suppressPackageStartupMessages(library(neuroim2))

Why Backends Matter

neuroim2 supports multiple representations for the same conceptual data: 3D volumes, 4D time-series, sparse masked data, memory-mapped arrays, and file-backed access. Choosing the right representation is usually the biggest practical decision you make up front.

This guide is the shortest path to that choice.

Quick Rules

• Use DenseNeuroVol or DenseNeuroVec when the data fits comfortably in memory and you want the simplest behavior.
• Use SparseNeuroVol or SparseNeuroVec when only a subset of voxels is meaningful and absent voxels should be treated as missing support rather than literal zeros.
• Use MappedNeuroVec when you want fast random access to an uncompressed NIfTI on disk without loading it all into memory.
• Use FileBackedNeuroVec when you want on-demand volume access from a file and can tolerate more I/O per access.
• Use NeuroHyperVec when the input is genuinely 5D.

Decision Table

| Situation | Recommended backend | Why | |:--|:--|:--| | Small-to-moderate 3D image | DenseNeuroVol | simplest, lowest overhead | | Small-to-moderate 4D fMRI series | DenseNeuroVec | easiest indexing and arithmetic | | Masked analysis over a subset of voxels | SparseNeuroVec | stores only supported voxels | | Large uncompressed NIfTI with random-access needs | MappedNeuroVec | memory mapping is efficient | | Large file, on-demand volume access | FileBackedNeuroVec | no full materialization | | 5D image | NeuroHyperVec | intended representation |

Dense In-Memory Data

If the dataset fits in RAM, start here.

file_name <- system.file("extdata", "global_mask_v4.nii", package = "neuroim2")
vec <- read_vec(file_name)

class(vec)
dim(vec)

This gives you the most predictable behavior for:

• arithmetic
• indexing
• resampling
• plotting
• converting to matrices or arrays

Sparse Masked Data

If you only care about a subset of voxels, read the data through a mask and keep the representation sparse.

file_name <- system.file("extdata", "global_mask_v4.nii", package = "neuroim2")
mask_vol <- read_vol(file_name) > 0
svec <- read_vec(file_name, mask = mask_vol)

class(svec)
dim(svec)
sum(mask(svec))

Use this when:

• your workflow is defined on a brain mask or ROI support
• you want to avoid materializing out-of-mask voxels
• absent voxels should be treated as missing support

Memory-Mapped Access

For large uncompressed NIfTI files, mmap mode gives efficient on-disk access.

file_name <- system.file("extdata", "global_mask_v4.nii", package = "neuroim2")
mvec <- read_vec(file_name, mode = "mmap")

series(mvec, 1, 1, 1)
sub_vector(mvec, 1:5)

Use this when:

• the file is uncompressed
• you need repeated random access
• you want to avoid holding the full array in memory

File-Backed Access

filebacked mode is the safest on-disk choice when you want deferred access without requiring the full dense object in memory.

file_name <- system.file("extdata", "global_mask_v4.nii", package = "neuroim2")
fbvec <- read_vec(file_name, mode = "filebacked")

fbvec[[1]]
sub_vector(fbvec, 1:3)

Use this when:

• you mostly work volume-by-volume
• you want an explicit on-disk representation
• you do not want to materialize the entire dataset

5D Data

When the input is 5D, use read_image() or read_hyper_vec().

img5d <- read_image("some_5d_image.nii.gz")
class(img5d)

Recommended Defaults

If you are unsure:

1. Start with read_vec() or read_vol().
2. Move to SparseNeuroVec only when a mask meaningfully reduces the support.
3. Move to mmap or filebacked when memory or I/O costs force the issue.

Related Articles

• vignette("Overview") for general orientation
• vignette("NeuroVector") for 4D workflows
• vignette("Resampling") for spatial transforms
• vignette("regionOfInterest") for ROI and searchlight workflows

neuroim2 documentation built on April 16, 2026, 5:07 p.m.