BayesGLM: BayesGLM for CIFTI

In BayesfMRI: Spatial Bayesian Methods for Task Functional MRI Studies

BayesGLM for CIFTI

Description

Performs spatial Bayesian GLM for task fMRI activation with CIFTI-format data. The cortex is modeled as a surface mesh, and subcortical structures are modeled as distinct volumetric regions. Includes the pre-processing steps of nuisance regression, prewhitening, scaling, and variance normalization. Supports both single- and multi-session analysis. Can also compute just the classical (spatially-independent)

Usage

BayesGLM(
  BOLD,
  brainstructures = c("left", "right"),
  subROI = c("Amygdala-L", "Amygdala-R", "Caudate-L", "Caudate-R", "Hippocampus-L",
    "Hippocampus-R", "Thalamus-L", "Thalamus-R"),
  design,
  nuisance = NULL,
  scrub = NULL,
  hpf = NULL,
  TR = NULL,
  surfL = NULL,
  surfR = NULL,
  resamp_res = 10000,
  nbhd_order = 1,
  buffer = c(1, 1, 3, 4, 4),
  session_names = NULL,
  scale_BOLD = c("mean", "sd", "none"),
  Bayes = TRUE,
  hyperpriors = c("informative", "default"),
  ar_order = 6,
  ar_smooth = 5,
  aic = FALSE,
  n_threads = 4,
  return_INLA = c("trimmed", "full", "minimal"),
  verbose = 1,
  meanTol = 1e-06,
  varTol = 1e-06
)

Arguments

BOLD	fMRI timeseries data in CIFTI format ("*.dtseries.nii"). For single-session analysis this can be a file path to a CIFTI file or a "xifti" object from the ciftiTools package. For multi-session analysis this can be a vector of file paths or a list of "xifti" objects. If BOLD is a "xifti" object(s), the surfaces, if any, will be used for the spatial model. However, if surfL and surfR are provided, they will override any surfaces in BOLD.
brainstructures	Character vector indicating which brain structure(s) of BOLD to analyze: "left" cortex; "right" cortex; and/or "subcortical" structures. Or "all" to model all three. Default: c("left","right") (cortex only).
subROI	Which subcortical ROIs should be analyzed? Can be "all" to analyze all subcortex ROIs. See the ciftiTools_Name column of ciftiTools:::substructure_table() for a list of possible subcortical ROIs.
design	A numeric matrix or data.frame, or a "BayesfMRI_design" object from make_design. Can also be an array where the third dimension is the same length as the number of data locations, to model each location with its own design.
nuisance	(Optional) A T \times N_{nuis} matrix of nuisance signals, where T is the number of timepoints and N is the number of nuisance signals, or a list of these for multi-session analysis. Nuisance signals are regressed from the fMRI data and design matrix prior to GLM computation. Nuisance signals can include motion regressors, HRF derivatives not being modeled as tasks, and other sources of noise. Detrending/high-pass filtering is accomplished by adding DCT bases to the nuisance matrix; see the parameters hpf and DCT. Do not add spike regressors for scrubbing to the nuisance matrix. Rather, provide these in scrub so that their corresponding timepoints are also removed from the BOLD data after nuisance regression.
scrub	(Optional) A T \times N_{scrub} matrix of spike regressors (one 1 value at the timepoint to scrub, and 0 for all other values), or a logical vector indicating the timepoints to scrub (TRUE to scrub, and FALSE to keep). For multi-session data, a session-length list of such matrices or logical vectors. The spike regressors will be included in the nuisance regression, and afterwards the timepoints indicated in scrub will be removed from the BOLD data and design matrix.
hpf	Add DCT bases to nuisance to apply a temporal high-pass filter to the data, for detrending? hpf is the filter frequency. Use NULL to skip detrending. Detrending is strongly recommended for fMRI data, to help reduce the autocorrelation in the residuals, so NULL will induce a warning. Use "already" to disable the warning while skipping highpass filtering. Using at least two DCT bases is as sufficient for detrending as using linear and quadratic drift terms in the nuisance matrix. So if DCT detrending is being used here, there is no need to add linear and quadratic drift terms to nuisance.
TR	Temporal resolution of the data, in seconds.
surfL, surfR	For cortex spatial model. Left and right cortex surface geometry in GIFTI format ("*.surf.gii"). These can be a file path to a GIFTI file or a "surf" object from ciftiTools. Surfaces can alternatively be provided through the $surf metadata in BOLD if it is "xifti" data. If neither are provided, by default the HCP group-average fs_LR inflated surfaces included in ciftiTools will be used for the cortex spatial model.
resamp_res	For cortex spatial model. The number of vertices to which each cortical surface should be resampled, or NULL to not resample. For computational feasibility, a value of 10000 (default) or lower is recommended for Bayesian spatial modeling. If Bayes=FALSE, resamp_res can be set to NULL for full-resolution classical modeling.
nbhd_order	For volumetric model. What order neighborhood around data locations to keep? 0 for no neighbors, 1 for 1st-order neighbors, 2 for 1st- and 2nd-order neighbors, etc. Smaller values will provide greater computational efficiency at the cost of higher variance around the edge of the data.
buffer	For volumetric model. The number of extra voxel layers around the bounding box. Set to NULL for no buffer. (We recommend not changing buffer unless you know what you're doing. Instead, to reduce the number of boundary voxels, adjust nbhd_order).
session_names	The names of the task-fMRI BOLD sessions, for multi-session analysis. If not provided here, will be inferred from names(BOLD), inferred from names(design), or generated automatically, in that order.
scale_BOLD	Controls scaling the BOLD response at each location. "mean": Scale the data to percent local signal change. "sd": Scale the data by local standard deviation. "none": Center the data but do not scale it.
Bayes	Perform spatial Bayesian modeling? Default: TRUE. If FALSE, only perform classical (massive univariate) modeling. (The classical GLM result is always returned, whether Bayes is TRUE or FALSE.)
hyperpriors	Should informative or default non-informative hyperpriors be assumed on SPDE hyperparameters?
ar_order	(For prewhitening) The order of the autoregressive (AR) model to use for prewhitening. If 0, do not prewhiten. Default: 6. For multi-session modeling, note that a single AR model is used; its coefficients will be the average estimate from each session.
ar_smooth	(For prewhitening) The FWHM parameter for spatially smoothing the coefficient estimates for the AR model to use for prewhitening. Recall that \sigma = \frac{FWHM}{2*sqrt(2*log(2)}. Set to 0 to not smooth the estimates. Default: 5.
aic	(For prewhitening) Use the Akaike information criterion (AIC) to select AR model orders between 0 and ar_order? Default: FALSE.
n_threads	The maximum number of threads to use for parallel computations: prewhitening parameter estimation, and the inla-program model estimation. Default: 4. Note that parallel prewhitening requires the parallel package.
return_INLA	Return the INLA model object? (It can be large.) Use "trimmed" (default) returns the results sufficient for activations and BayesGLM2; "minimal" returns enough for BayesGLM2 but not activations; "full" returns the full inla output.
verbose	1 (default) to print occasional updates during model computation; 2 for occasional updates as well as running INLA in verbose mode (if Bayes), or 0 for no printed updates.
meanTol, varTol	Tolerance for mean and variance of each data location. Locations which do not meet these thresholds are masked out of the analysis. Default: 1e-6 for both.

Details

To use BayesGLM, the design matrix must first be constructed with make_design.

Value

An object of class "BayesGLM": a list with elements

betas_Bayesian

The field coefficients for the Bayesian model.

betas_classical

The field coefficients for the classical model.

GLMs_Bayesian

The entire list of GLM results, except for parameters estimated for the classical model.

GLMs_classical

Parameters estimated for the classical model from the GLM.

brainstructures

data.frame summarizing the spatial features of each brain structure modeled.

sessions

data.frame with the name and nTime of each BOLD session.

fields

data.frame with the name, related task, and HRF_order of each field.

Connectome Workbench Requirement

This function uses a system wrapper for the 'wb_command' executable. The user must first download and install the Connectome Workbench, available from https://www.humanconnectome.org/software/get-connectome-workbench .

INLA Requirement

This function requires the INLA package, which is not a CRAN package. See https://www.r-inla.org/download-install for easy installation instructions.

INLA Latent Fields Limit

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 dHRF_as="nuisance", rather than modeling the temporal derivatives as fields.

BayesfMRI documentation built on April 4, 2025, 1:58 a.m.