ffdGroupEvidenceFFBS {BayesDLMfMRI} | R Documentation |

## ffdGroupEvidenceFFBS

### Description

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

### Usage

```
ffdGroupEvidenceFFBS(
ffdGroup,
covariates,
m0 = 0,
Cova = 100,
delta = 0.95,
S0 = 1,
n0 = 1,
N1 = FALSE,
Nsimu1 = 100,
Cutpos = 30,
r1,
mask,
Ncores = NULL
)
```

### Arguments

`ffdGroup` |
list of N elements, each being 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 between pair of 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. |

`Cutpos` |
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` |
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 |

`mask` |
a 3D array that works as a brain of reference (MNI atlas) for the group analysis. |

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

### Details

A multivariate dynamic linear model is fitted in the same fashion as at the individual level for every subject in the sample. However, at this stage, the posterior distributions from all the subjects are combined to build a single one, which is then employed to compute the activation evidence maps for the group using the Forward Filtering Backward Sampling (FFBS) 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.

### Value

It returns a list of the form `[[k]][p,x,y,z]`

, where k defines the type of test
(`k = 1`

for Marginal effect, `k = 2`

for Joint effect, and `k = 3`

for Average cluster effect), `p`

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

represent the voxel position in the brain image.

### 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.
DatabaseGroup <- get_example_fMRI_data_group()
data("covariates", package="BayesDLMfMRI")
res <- ffdGroupEvidenceFFBS(ffdGroup = DatabaseGroup, covariates = Covariates,
m0=0, Cova=100, delta = 0.95,
S0 = 1, n0 = 1, N1 = FALSE, Nsimu1 = 100,
Cutpos = 30, r1 = 1, mask = MASK, Ncores = 7)
str(res)
## End(Not run)
```

*BayesDLMfMRI*version 0.0.3 Index]