A brain model axis describes which surface vertices and volume voxels a
matrix dimension of a CIFTI-2 file contains, in the order in which they appear in the
matrix. This is the mapping of a dense file (.dscalar, .dtseries, .dlabel,
.dconn), and of the files that mix dense and parcellated data.
The index ranges of the models are computed from the index lists, so the models cover the dimension without gaps, which the format requires. A structure may appear in several models (e.g. as a surface and as a volume part in a grayordinates file), and the order of the models is the order of the matrix indices.
cifti.axis.brain.models(models, surfaces = NULL, volume = NULL)a named list with the entries 'type', 'brain_models', 'surfaces' and 'volumes',
an axis to be passed to cifti.header.from.axes or
write.cifti.
list of brain model entries, as created by
cifti.brain.model.surface and cifti.brain.model.volume.
named integer vector or NULL, the number of vertices of the complete
surfaces the file refers to, named by brain structure (e.g.
c(CORTEX_LEFT = 32492, CORTEX_RIGHT = 32492)). This is written as the optional
Surface elements of the axis. Connectome Workbench does not write them, and the
surface size is then taken from the brain model entries only, so this can be left at
NULL unless the file has to state the surface sizes explicitly.
a volume, see cifti.volume, or NULL. Required if any of
the models is a volume model, because the voxel indices of a model can not be
interpreted without the voxel grid and the transformation matrix.
Other cifti functions:
cifti.axis.from.template(),
cifti.axis.labels(),
cifti.axis.parcels(),
cifti.axis.parcels.from.annot(),
cifti.axis.scalars(),
cifti.axis.series(),
cifti.brain.model.surface(),
cifti.brain.model.volume(),
cifti.dim.labels(),
cifti.file.type.for.axes(),
cifti.grayordinates(),
cifti.header.from.axes(),
cifti.label.table(),
cifti.parcel(),
cifti.parcels(),
cifti.series.info(),
cifti.structure.data(),
cifti.structures(),
cifti.volume(),
print.fs.cifti(),
print.fs.cifti.data(),
print.fs.connectome(),
read.cifti(),
read.cifti.header(),
read.cifti.rows(),
read.fs.connectome.cifti(),
write.cifti(),
write.fs.connectome.cifti(),
write.fs.morph.cifti(),
write.fs.parcellated.cifti(),
write.fs.parcellation.cifti(),
write.fs.series.cifti()
axis <- cifti.axis.brain.models(list(
cifti.brain.model.surface("lh", 10L),
cifti.brain.model.surface("rh", 12L)))
axis$type
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