Wrapper to CompCor for HCP-format data. Can be used to clean
the surface-based CIFTI data with aCompCor using the noise PCs and ROIs
calculated from the NIFTI fMRI data and NIFTI mask. Can also be used to just
obtain the noise PCs and ROIs without performing aCompCor, if the CIFTI
data is not provided.
CompCor_HCP(
nii,
nii_labels,
ROI_noise = c("wm_cort", "csf"),
noise_nPC = 5,
noise_erosion = NULL,
idx = NULL,
cii = NULL,
brainstructures = c("left", "right"),
center = TRUE,
scale = TRUE,
DCT = 0,
nuisance_too = NULL,
verbose = FALSE
)The noise components, and if cii is provided, the cleaned
surface-based data as a "xifti" object.
\(I\) by \(J\) by \(K\) by \(T\) NIFTI object or array (or file path to the NIFTI) which contains whole-brain data, including the noise ROIs. In the HCP, the corresponding file is e.g. "../Results/rfMRI_REST1_LR/rfMRI_REST1_LR.nii.gz"
\(I\) by \(J\) by \(K\)
NIFTI object or array (or file path to the NIFTI) which
contains the corresponding labels to each voxel in nii. Values should
be according to this table:
https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT .
In the HCP, the corresponding file is "ROIs/Atlas_wmparc.2.nii.gz".
A list of numeric vectors. Each entry should represent labels
in nii_labels belonging to a single noise ROI, named by that entry's
name. Or, this can be a character vector of at least one of the following:
"wm_cort" (cortical white matter), "wm_cblm" (cerebellar white
matter), "csf" (cerebrospinal fluid). In the latter case, these labels
will be used:
"wm_cort"c(3000:4035, 5001, 5002)
"wm_cblm"c(7, 46)
"csf"c(4, 5, 14, 15, 24, 31, 43, 44, 63, 250, 251, 252, 253, 254, 255))
These default ROIs are based on this forum post: https://www.mail-archive.com/hcp-users@humanconnectome.org/msg00931.html
Default: c("wm_cort", "csf")
The number of principal components to compute for each noise ROI. Alternatively, values between 0 and 1, in which case they will represent the minimum proportion of variance explained by the PCs used for each noise ROI. The smallest number of PCs will be used to achieve this proportion of variance explained.
Should be a list or numeric vector with the same length as ROI_noise.
It will be matched to each ROI based on the name of each entry, or if the
names are missing, the order of entries. If it is an unnamed vector, its
elements will be recycled. Default: 5 (compute the top 5 PCs for
each noise ROI).
The number of voxel layers to erode the noise ROIs by.
Should be a list or numeric vector with the same length as ROI_noise.
It will be matched to each ROI based on the name of each entry, or if the
names are missing, the order of entries. If it is an unnamed vector, its
elements will be recycled. Default: NULL, which will use a value of
0 (do not erode the noise ROIs).
A numeric vector indicating the timepoints to use, or
NULL (default) to use all idx. (Indexing begins with 1, so the
first timepoint has index 1 and the last has the same index as the length of
the scan.)
"xifti" (or file path to the CIFTI) from which the noise
ROI components will be regressed. In the HCP, the corresponding file is e.g.
"../Results/rfMRI_REST1_LR/rfMRI_REST1_LR_Atlas_MSMAll.dtseries.nii". If not
provided, only the noise components will be returned (no data will be cleaned).
Choose among "left", "right", and "subcortical".
Default: c("left", "right") (cortical data only)
Center the columns of the data by median, and scale the
columns of the data by MAD? Default: TRUE for both. Affects both
X and the noise data. center also applies to nuisance_too
so if it is FALSE, nuisance_too must already be centered.
Add DCT bases to the nuisance regression? Use an integer to
indicate the number of cosine bases. Use 0 (default) to forgo detrending.
The data must be centered, either before input or with center.
A matrix of nuisance signals to add to the nuisance
regression. Should have \(T\) rows. NULL to not add additional
nuisance regressors (default).
Should occasional updates be printed? Default: FALSE.
Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90-101 (2007).
Muschelli, J. et al. Reduction of motion-related artifacts in resting state fMRI using aCompCor. NeuroImage 96, 22-35 (2014).
CompCor