ciftiTools

ciftiTools provides functions to read, write, visualize, and manipulate CIFTI files for grayordinate-based analysis of magnetic resonance (MR) data, including functional MRI (fMRI), within the R environment.

Key Features:

• CIFTI I/O: Read and write CIFTI files within R.
• Visualization: Visualize whole-cortex and grayordinate data.
• Data manipulation: Manipulate grayordinate and surface geometry data programmatically.
• Grayordinate support: Operate in grayordinate space integrating cortical hemispheres with subcortical and cerebellar regions in the CIFTI format introduced by the Human Connectome Project.
• Surface smoothing: Support surface smoothing to avoid issues associated with volumetric smoothing.
• Inter-subject alignment: Enable analyses that leverage improved inter-subject alignment in grayordinate space.
• Dimensionality reduction: Facilitate reduced-dimensionality representations for statistical analyses.
• Statistical compatibility: Support traditional statistical methods and Bayesian analyses on grayordinate data.
• fMRI analysis support: Provide capabilities focused on statistical analysis of functional MRI data in grayordinate space.

Scientific Applications:

• Grayordinate-based MR and fMRI analysis: Perform surface- and grayordinate-based analyses of MR and fMRI datasets.
• Whole-cortex visualization: Visualize integrated cortical, subcortical, and cerebellar data in a single CIFTI-based representation.
• Surface-based smoothing workflows: Apply surface smoothing approaches to avoid volumetric smoothing artifacts.
• Group and cross-subject analyses: Conduct group-level and inter-subject alignment analyses using grayordinate representations.
• Advanced statistical methods: Support Bayesian and other advanced statistical approaches on grayordinate data.
• Brain connectivity and function studies: Enable analyses relevant to brain connectivity and functional organization using grayordinates.

Methodology:

Implements reading, writing, visualization, and manipulation of CIFTI files and surface geometry in R, supports surface smoothing, and enables traditional and Bayesian statistical analyses on grayordinate representations that integrate cortical hemispheres with subcortical and cerebellar data.