SingleVoxelFSTS {BayesDLMfMRI} | R Documentation |
SingleVoxelFSTS
Description
This function is used to perform an activation analysis for single voxels based on the FSTS algorithm.
Usage
SingleVoxelFSTS(
posi.ffd,
covariates,
ffdc,
m0,
Cova,
delta,
S0,
n0,
N1,
Nsimu1,
Cutpos1,
Min.vol,
r1
)
Arguments
posi.ffd |
the position of the voxel in the brain image. |
covariates |
a data frame or matrix whose columns contain the covariates related to the expected BOLD response obtained from the experimental setup. |
ffdc |
a 4D array ( |
m0 |
the constant prior mean value for the covariates parameters and common to all voxels within every neighborhood at |
Cova |
a positive constant that defines the prior variances for the covariates parameters at |
delta |
a discount factor related to the evolution variances. Recommended values between |
S0 |
prior covariance structure among voxels within every cluster at |
n0 |
a positive hyperparameter of the prior distribution for the covariance matrix |
N1 |
is the number of images ( |
Nsimu1 |
is the number of simulated on-line trajectories related to the state parameters. These simulated curves are later employed to compute the posterior probability of voxel activation. |
Cutpos1 |
a cutpoint time from where the on-line trajectories begin. This parameter value is related to an approximation from a t-student distribution to a normal distribution. Values equal to or greater than 30 are recommended ( |
Min.vol |
helps to define a threshold for the voxels considered in
the analysis. For example, |
r1 |
a positive integer number that defines the distance from every voxel with its most distant neighbor. This value determines the size of the cluster. The users can set a range of different |
Details
This function allows the development of an activation analysis for single voxels. A multivariate dynamic linear model is fitted to a cluster of voxels, with its center at location (i,j,k)
, in the way it is presented in (Cardona-Jiménez and de B. Pereira 2021).
Value
a list containing a vector (Evidence) with the evidence measure of
activation for each of the p
covariates considered in the model, the simulated
online trajectories related to the state parameter, the simulated BOLD responses,
and a measure to examine the goodness of fit of the model \((100 \ast |Y[i,j,k]_t - \hat{Y}[i,j,k]_t |/ \hat{Y}[i,j,k]_t )\) for that particular voxel (FitnessV
).
References
Cardona-Jiménez J, de B. Pereira CA (2021). “Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: An application to task-based fMRI data analysis.” Computational Statistics & Data Analysis, 163, 107297. ISSN 0167-9473, doi:10.1016/j.csda.2021.107297, https://www.sciencedirect.com/science/article/pii/S0167947321001316.
Cardona-Jiménez J (2021). “BayesDLMfMRI: Bayesian Matrix-Variate Dynamic Linear Models for Task-based fMRI Modeling in R.” arXiv e-prints, arXiv–2111.
Examples
## Not run:
# This example can take a long time to run.
fMRI.data <- get_example_fMRI_data()
data("covariates", package="BayesDLMfMRI")
res.indi <- SingleVoxelFSTS(posi.ffd = c(14, 56, 40),
covariates = Covariates,
ffdc = fMRI.data,
m0 = 0, Cova = 100, delta = 0.95, S0 = 1,
n0 = 1, Nsimu1 = 100, N1 = FALSE, Cutpos1 = 30,
Min.vol = 0.10, r1 = 1)
## End(Not run)