Application to voxel-wise data
ModelArray is compatible with not only fixel-wise data,
but also voxel-wise data. The
has shown example walkthrough with example fixel-wise data, with hints
for voxel-wise data provided throughout the walkthrough. In general, the
workflow of applying
ModelArray to voxel-wise data is
consistent to that of fixel-wise data, but please note several important
differences when execution described as below.
Conversion of voxel-wise data format
When converting voxel-wise data between NIfTI and HDF5 file format
ModelArray requires, you will still use
ConFixel software, but a different converter tailored for
ConVoxel. We have provided a walkthrough
of how to use it here.
ModelArray to voxel-wise data
You can apply the same
ModelArray functions, arguments,
etc used in fixel-wise data to voxel-wise data; just be aware that an
“element” is a “voxel” now.
In addition, there are additional arguments specifically useful for voxel-wise data, regarding the subject-specific masks (i.e., individual masks). Different from fixel-wise data where subjects usually share the same mask, voxel-wise data may have subject-specific masks, where different subjects may have different brain coverage even in template space. Therefore, for voxels (e.g., at the edge of the brain) which do not have data from sufficient number of subjects, their statistic results might not be reliable. It’s better to exclude those voxels from the analysis.
ConVoxel, for voxels not within a subject-specific
mask (but within a larger, group-level mask), their values will be set
ModelArray can know the number of
subjects that have valid values in each voxel within the group mask.
To set up minimal number of subjects required for a voxel, you can
use these two arguments in
num.subj.lthr.abs: lower threshold, absolute number of subjects; e.g., 10
num.subj.lthr.rel: lower threshold, proportion of total number of subjects, a value between 0-1; e.g., 0.2 (i.e., 20% of all subjects)
Suppose a voxel has valid values from N subjects, where N:
- N >
- N >
Ntotal is the total number of subjects, which is
quantified by number of rows in argument
phenotypes. If N
satisfied above criteria, statistical analysis of this voxel will be
performed as normal; otherwise, this voxel will be skipped and
statistical outputs will be set as
For more details about these two arguments, please check the
reference pages of
Finally, if you choose to save number of observations used (i.e., if
ConVoxel, you’ll see an image called
*_model.nobs*. If the subject-specific masks you provided
are different across subjects, in this image you’ll probably see the
heterogeneity of number of observations used in the brain, with fewer
observations used at the edge of the brain.