.distanceNeighbors

internal

The 'BayesDLMfMRI' package performs statistical analysis for task-based functional magnetic resonance imaging (fMRI) data at both individual and group levels. The analysis to detect brain activation at the individual level is based on modeling the fMRI signal using Matrix-Variate Dynamic Linear Models (MDLM). The analysis for the group stage is based on posterior distributions of the state parameter obtained from the modeling at the individual level. In this way, this package offers several R functions with different algorithms to perform inference on the state parameter to assess brain activation for both individual and group stages. Those functions allow for parallel computation when the analysis is performed for the entire brain as well as analysis at specific voxels when it is required.
References: Cardona-Jiménez (2021) <doi:10.1016/j.csda.2021.107297>;
Cardona-Jiménez (2021) <arXiv:2111.01318>.

Carlos Pérez

BayesDLMfMRI

Statistical Analysis for Task-Based Fmri Data

Johnatan Cardona-Jiménez

Isabel Ramírez

.distanceNeighbors function

<dl><dt>posi.refer</dt>
<dd>the position of the voxel in the brain image.</dd>
<dt>r1</dt>
<dd>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 r values: r = 0, 1, 2, 3, 4, which leads to q = 1, 7, 19, 27, 33, where q is the size of the cluster.</dd></dl>

Arguments

.distanceNeighbors — .distanceNeighbors

<dl>

<dt>posi.refer</dt>
<dd>the position of the voxel in the brain image.</dd>


<dt>r1</dt>
<dd>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 r values: r = 0, 1, 2, 3, 4, which leads to q = 1, 7, 19, 27, 33, where q is the size of the cluster.</dd>

</dl>

.distanceNeighbors: .distanceNeighbors

Description

Usage

Arguments

Details

References