ffdEvidenceFETS {BayesDLMfMRI} | R Documentation |

This function can be used to build activation maps for task-based fMRI data.

```
ffdEvidenceFETS(
ffdc,
covariates,
m0 = 0,
Cova = 100,
delta = 0.95,
S0 = 1,
n0 = 1,
N1 = FALSE,
Nsimu1 = 100,
Cutpos1 = 30,
r1 = 1,
perVol = 0.1,
Test = "LTT",
Ncores = NULL,
seed = NULL
)
```

`ffdc` |
a 4D array ( |

`covariates` |
a data frame or matrix whose columns contain the covariates related to the expected BOLD response obtained from the experimental setup. |

`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 ( |

`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 |

`perVol` |
helps to define a threshold for the voxels considered in the analysis. For example, |

`Test` |
test type either |

`Ncores` |
a positive integer indicating the number of threads or cores to be used in the computation of the activation maps. |

`seed` |
random seed. |

Every voxel from the 4D array image is clustered with its nearest neighbors. There are as many clusters as voxels in the image. Then, activation maps are obtained by fitting a multivariate dynamic linear model on every cluster of voxels. The resulting activation evidence measure for every voxel is obtained using the Forward Estimated Trajectories Sampler (FETS) algorithm. To deeply understand the method implemented in this package, a reading of (Cardona-Jiménez and de B. Pereira 2021) and (Cardona-Jiménez 2021) is mandatory.

It returns a list of the type `res[[p]][x,y,z]`

, where `p`

represents the column position in
the covariates matrix and `[x,y,z]`

represent the voxel position in the brain image.

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.

```
## Not run:
fMRI.data <- get_example_fMRI_data()
data("covariates", package="BayesDLMfMRI")
res <- ffdEvidenceFETS(ffdc = fMRI.data, covariates = Covariates,
m0 = 0, Cova = 100, delta = 0.95, S0 = 1,
n0 = 1, Nsimu1 = 100, Cutpos1 = 30,
r1 = 2, Test = "JointTest", Ncores = 1)
str(res)
## End(Not run)
```

[Package *BayesDLMfMRI* version 0.0.3 Index]