Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: An application to task-based fMRI data analysis

Jiménez, Johnatan Cardona; Pereira, Carlos Alberto

doi:10.1016/j.csda.2021.107297

Cited by 2 publications

(11 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The information obtained from the individual stage can be combined in different ways to produce several measures of evidence for group activation. For a better understanding of the method implemented in this package for the group stage, see [16]. In Table 1, the package's functions for individual and group analysis, along with a brief description, are presented.…”

Section: Methodsmentioning

confidence: 99%

“…For this example, we are setting a "vague" prior distribution according to [25], where m0 = 0 defines a null matrix with zero values in all its entries. And both Cova = 100 and S0 = 1 define respectively the values for the diagonal matrices C 0 and S 0 as defined in [16]. r1 is the euclidean distance, which defines the size of the cluster of voxels jointly modeled.…”

Section: ++++++++++++++++++++++++++++++++++++++++++++++++++| 100% Ela...mentioning

confidence: 99%

“…Online theta are the draws from the Marginal effect distribution, and Online theta mean are the average on-line simulated trajectories obtained from the joint effect distribution. In Figure 3 are presented the simulated BOLD responses for active and non-active voxels, and in Figure 4 are shown the online trajectories to understand the significance of brain activation as it is addressed in [16].…”

Section: R> Dim(res[[1]]) [1] 91 109 91mentioning

confidence: 99%

“…Now we illustrate how to run an fMRI group analysis as described in [16]. In this first version of the BayesDLMfMRI package, we implement functions to detect brain activation for single groups.…”

Section: Group Analysismentioning

confidence: 99%

“…In this work, we introduce an R package called BayesDLMfMRI, which implements the method presented in [16]. The BayesDLMfMRI permits to perform individual, and group task-based fMRI analysis based on the Matrix-Variate Dynamic Linear Model (MDLM) proposed by [17].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

BayesDLMfMRI: an R package for task-based fMRI modeling based on Bayesian matrix-variate dynamic linear models

Jiménez

Pérez

Ramírez

2023

Preprint

View full text Add to dashboard Cite

Background: Functional magnetic resonance imaging (fMRI) is a technique that has caught the attention of scientists since its appearance over thirty years ago. This tool allows for analyzing human brain activity through a sequence of magnetic resonance (MR) images. One important stage in the application of the fMRI technique is the statistical analysis of the sequence of post-processed images. The General Linear Model (GLM) is perhaps the most common statistical model applied for task-based fMRI analysis. However, in the majority of applications where this model is used, the spatiotemporal structures usually present in this type of data are commonly ignored. Thus, in the package introduced in this article, we have implemented a statistical method based on Matrix-Variate Dynamic Linear Models (MDLM) that allows taking into account the spatiotemporal structures in the modeling of task-based fMRI data. Results: The BayesDLMfMRI package performs statistical analysis for task-based 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 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. Conclusions: In summary, we present a software implementation of a well-tested statistical method for task-based fMRI analysis that would complement analysis performed with more popular packages such as FSL and SPM.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: ++++++++++++++++++++++++++++++++++++++++++++++++++| 100% Ela...mentioning

confidence: 99%

Section: R> Dim(res[[1]]) [1] 91 109 91mentioning

confidence: 99%

Section: Group Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations