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fmridataset

https://github.com/bbuchsbaum/fmridataset

Science Score: 26.0%

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Readme Changelog Contributing Security Codemeta

README.md

fmridataset

R-CMD-check Codecov test coverage lint pkgcheck CRAN status Lifecycle: experimental <!-- badges: end -->

Overview

fmridataset provides a unified framework for representing functional magnetic resonance imaging (fMRI) data from various sources. The package supports multiple data backends and offers a consistent interface for working with fMRI datasets regardless of their underlying storage format.

Features

  • Unified Interface: Work with fMRI data from NIfTI files, BIDS projects, pre-loaded NeuroVec objects, and in-memory matrices through a single API
  • Lazy Loading: Efficient memory management with on-demand data loading
  • Flexible Backends: Pluggable storage backends for different data formats
  • Data Chunking: Built-in support for processing large datasets in chunks
  • Temporal Structure: Rich sampling frame representation for run lengths, TR, and temporal organization
  • Integration Ready: Seamlessly integrates with neuroimaging analysis workflows

Installation

You can install the development version of fmridataset from GitHub with:

``` r

install.packages("devtools")

devtools::install_github("bbuchsbaum/fmridataset") ```

Quick Start

Creating Datasets

```r library(fmridataset)

From NIfTI files

dataset <- fmridataset( scans = c("run1.nii", "run2.nii"), mask = "mask.nii", TR = 2.0, runlength = c(240, 240) )

From in-memory matrix

matdata <- matrix(rnorm(1000), nrow = 100, ncol = 10) dataset <- matrixdataset( datamat = matdata, TR = 1.5, runlength = 100 )

From pre-loaded NeuroVec objects

dataset <- fmrimemdataset( scans = list(neurovec1, neurovec2), mask = mask_vol, TR = 2.0 ) ```

Data Access

```r

Get full data matrix

datamatrix <- getdata_matrix(dataset)

Get spatial mask

mask <- get_mask(dataset)

Access temporal properties

ntimepoints(dataset$samplingframe) nruns(dataset$samplingframe) getTR(dataset$samplingframe) ```

Data Chunking

```r

Process data in chunks

chunks <- datachunks(dataset, nchunks = 5) for (i in 1:5) { chunk <- chunks$nextElem() # Process chunk$data, chunk$voxelind, etc. }

Run-wise processing

runchunks <- datachunks(dataset, runwise = TRUE) run1data <- runchunks$nextElem() ```

Type Conversions

```r

Convert to matrix format

matdataset <- as.matrixdataset(dataset)

All dataset types support the same interface

print(dataset) summary(dataset$sampling_frame) ```

Architecture

The package uses a modular architecture with the following key components:

  • Storage Backends: Pluggable data access layer (matrix_backend, nifti_backend)
  • Dataset Constructors: High-level dataset creation functions
  • Sampling Frames: Temporal structure representation
  • Data Access Methods: Consistent interface for data retrieval
  • Chunking System: Efficient processing of large datasets

Related Packages

Getting Help

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

License

GPL (>= 3)

Owner

  • Login: bbuchsbaum
  • Kind: user

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Dependencies

DESCRIPTION cran
  • R >= 4.0.0 depends
  • iterators * imports
  • methods * imports
  • tibble * imports
  • bidser * suggests
  • foreach * suggests
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