INLA_Latent_Fields_Limit_Description

Performs a spatial Bayesian general linear model (GLM) for task
functional magnetic resonance imaging (fMRI) data on the cortical surface.
Additional models include group analysis and inference to detect thresholded
areas of activation. Includes direct support for the 'CIFTI' neuroimaging
file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F.
Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D.
Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022)
<doi:10.1016/j.neuroimage.2022.118908>.

Amanda Mejia

BayesfMRI

Spatial Bayesian Methods for Task Functional MRI Studies

Damon Pham

David Bolin

Yu (Ryan) Yue

Daniel Spencer

Sarah Ryan

INLA_Latent_Fields_Limit_Description function

INLA computation times increase greatly when the number of columns in the
design matrix exceeds five: when there are more than five tasks, or more
than three tasks each with a temporal derivative modeled as a field. In
cases like the latter, we recommend modeling the temporal derivatives as
nuisance signals using the option <code>dHRF_as="nuisance"</code>, rather than
modeling the temporal derivatives as fields.

INLA Latent Fields Limit

INLA Latent Fields — INLA_Latent_Fields_Limit_Description

INLA computation times increase greatly when the number of columns in the
design matrix exceeds five: when there are more than five tasks, or more
than three tasks each with a temporal derivative modeled as a field. In
cases like the latter, we recommend modeling the temporal derivatives as
nuisance signals using the option <code>dHRF_as="nuisance"</code>, rather than
modeling the temporal derivatives as fields.

INLA Latent Fields

INLA_Latent_Fields_Limit_Description: INLA Latent Fields

Description

Arguments

INLA Latent Fields Limit