Calculates temporal signal-to-noise ratio (tSNR) for every in-mask voxel in a cleaned BOLD image. tSNR is defined as the ratio of the mean signal to its temporal standard deviation across the timeseries. It is a standard quality metric for fMRI acquisitions and preprocessing pipelines.
Calculates temporal signal-to-noise ratio (tSNR) for each voxel in the BOLD image: mean signal divided by standard deviation across timepoints. A standard quality metric for assessing fMRI data quality after preprocessing.
Arguments
- bold
A
boldR_boldobject fromprepare_bold().
Value
A list of class boldr_tsnr with components:
- tsnr
3D numeric array (same x × y × z as input). Contains tSNR values for in-mask voxels and
NAelsewhere.- mean_tsnr
Numeric. Mean tSNR across in-mask voxels.
- median_tsnr
Numeric. Median tSNR across in-mask voxels.
- n_voxels
Integer. Number of in-mask voxels.
A list of class boldR_tsnr with components:
- tsnr
Numeric array. tSNR map with same x/y/z dimensions as BOLD.
- mean_tsnr
Numeric. Mean tSNR across in-mask voxels.
- median_tsnr
Numeric. Median tSNR across in-mask voxels.
- bold
The input
boldR_boldobject.
Examples
if (FALSE) { # \dontrun{
sub <- read_fmriprep("data/derivatives/fmriprep", "01", task = "rest")
cleaned <- prepare_bold(sub)
tsnr_out <- compute_tsnr(cleaned)
tsnr_out$mean_tsnr
} # }
if (FALSE) { # \dontrun{
fprep <- read_fmriprep("data/derivatives/fmriprep", subject = "01")
bold <- prepare_bold(fprep, tr = 2, drop_volumes = 4)
tsnr <- compute_tsnr(bold)
tsnr$mean_tsnr
} # }