Package 'BayesfMRI'

the vector of initial values for theta

a list containing the sparse matrix elements Cmat, Gmat, and GtCinvG

the vector of response values

the sparse matrix of the data values

a sparse matrix of the prior precision found using the initial values of the hyperparameters

a sparse matrix representation of the basis function mapping the data locations to the mesh vertices

a precomputed matrix crossprod(X%*%Psi)

the number of columns for the random matrix used in the Hutchinson estimator

a value for the tolerance used for a stopping rule (compared to the squared norm of the differences between theta(s) and theta(s-1))

(logical) Should intermediate output be displayed?

a length-p vector where p is the AR order

(integer) the length of the time series that is being prewhitened

a scalar value of the residual variances of the AR model

a vector of length two containing the range and scale parameters kappa2 and phi, in that order

a list containing the sparse matrix elements Cmat, Gmat, and GtCinvG

the beta_hat estimates for a single task

the number of sessions

the stopping rule tolerance

(logical) Should intermediate output be displayed?

a scalar

a list with elements Cmat, Gmat, and GtCinvG

the integer number of sessions

Result of BayesGLM or fit_bayesglm model call, of class "BGLM" or "fit_bglm".

Use spatial Bayesian modeling to identify activations based on the joint posterior distribution? Default: TRUE. If FALSE, activations will be based on classical (massive univariate) GLM model, with multiple comparisons correction (see correction). Note that TRUE is only applicable if x includes Bayesian results (i.e. x <- BayesGLM(..., Bayes = TRUE) was run.)

Activation threshold, for example 1 for 1 percent signal change if scale_BOLD=="mean" during model estimation. Setting a gamma is required for the Bayesian method; NULL (default) will use a gamma of zero for the classical method.

Significance level. Default: 0.05.

For the classical method only: Type of multiple comparisons correction: "FWER" (Bonferroni correction, the default), "FDR" (Benjamini Hochberg), or "none".

The field(s) to identify activations for. Give either the name(s) as a character vector, or the numerical indices. If NULL (default), analyze all fields.

The session(s) to identify activations for. Give either the name(s) as a character vector, or the numerical indices. If NULL (default), analyze the first session.

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.

(For prewhitening) Use the Akaike information criterion (AIC) to select AR model orders between 0 and ar_order? Default: FALSE.

(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.

(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.

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.)

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.

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).

Which subcortical ROIs should be analyzed?

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.

(Optional) A $T \times N$ 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.

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.

Temporal resolution of the data, in seconds.

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.

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.

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.

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).

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.

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.

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.)

Should informative or default non-informative hyperpriors be assumed on SPDE hyperparameters?

(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.

(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.

(For prewhitening) Use the Akaike information criterion (AIC) to select AR model orders between 0 and ar_order? Default: FALSE.

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 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.

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.

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.

Either (1) a length $N$ list of "BGLM" objects, or (2) a length $N$ character vector of files storing "BGLM" objects saved with saveRDS. "fit_bglm" objects also are accepted.

(Optional) A list of contrast vectors that specify the group-level summaries of interest. If NULL (DEFAULT), use contrasts that compute the average of each field (field HRF) across all subjects/sessions.

Each contrast vector is length $KSN$ specifying a group-level summary of interest, where $K$ is the number of fields in the first-level design matrices, $S$ is the number of sessions, and $N$ is the number of subjects. The vector is grouped by fields, then sessions, then subjects.

For a single session/subject, the contrast vector for the first field would be:

c0 <- c(1, rep(0, K-1)) #indexes the first field for a single session

so the full contrast vector for the group average over all sessions/subjects for the first field would be:

contrasts = rep(c0, S*N) /(S*N).

To obtain the group average for the first field, for just the first session, input zeros for the remaining sessions:

c2 <- c(c0, rep(0, K*(S-1))) contrasts = rep(c2, N) /N.

To obtain the group mean difference between two sessions ($S=2$) for the first field:

c3 <- c(c0, -c0) contrasts = rep(c3, N) / N.

To obtain the mean over sessions of the first field, just for the first subject:

c4 <- rep(c0, S) c(c4, rep(0, K*S*(N-1))) / S.

(Optional) Vector of posterior quantiles to return in addition to the posterior mean.

(For inference only) The type of excursion function for the contrast (">", "<", "!="), or a vector thereof (each element corresponding to one contrast). If NULL, no inference performed.

(Optional) Names of contrasts.

(For inference only) Activation threshold for the excursion set, or a vector thereof (each element corresponding to one contrast). Default: 0.

(For inference only) Significance level for activation for the excursion set, or a vector thereof (each element corresponding to one contrast). Default: .05.

Number of theta values to sample from posterior. Default: 50.

Number of beta vectors to sample conditional on each theta value sampled. Default: 100.

The number of cores to use for sampling betas in parallel. If NULL (default), do not run in parallel.

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.

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.

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).

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).

A numeric matrix, or a vector which will be converted to a single-column matrix.

List of contrast vectors to be passed to inla::inla.

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.

(logical) Should the EM implementation of the Bayesian GLM be used? Default: FALSE. This method is still in development.

The stopping tolerance for the EM algorithm. Default: 1e-3.

An $F \times 3$ matrix, where each row contains the vertex indices for a given triangular face in the mesh. $F$ is the number of faces in the mesh.

(Optional) Names of fields represented in design matrix.

Session-length list of numeric matrices/arrays, each with volumes along the first dimension.

Gives the spatial information:

surf

A list of two: vertices $V \times 3$ numeric matrix of vertex locations in XYZ coordinate space, and faces, $F \times 3$ matrix of positive integers defining the triangular faces.

mask

Mask of locations with valid data.

For voxel data, a list of six:

label

3D array of labeled locations to include in the model.

trans_mat

Projection matrix to convert voxel indices to XYZ position. Can be NULL.

trans_units

XYZ units. Can be NULL.

nbhd_order

See documentation for BayesGLM.

buffer

See documentation for BayesGLM.

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.

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.)

Should informative or default non-informative hyperpriors be assumed on SPDE hyperparameters?

(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.

(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.

(For prewhitening) Use the Akaike information criterion (AIC) to select AR model orders between 0 and ar_order? Default: FALSE.

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 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.

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.

Tolerance for mean, variance and SNR of each data location. Locations which do not meet these thresholds are masked out of the analysis. Default: 1e-6 for mean and variance, 50 for SNR.

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.

time vector (in units of seconds)

0 (default) for the HRF, 1 for the delay derivative of the HRF, or 2 for the dispersion derivative of the HRF.

delay of response. Default: 6

response dispersion. Default: 1

delay of undershoot. Default: 16/6 * a1 * sqrt(b1) = 16

dispersion of undershoot. Default: b1 = 1

scale of undershoot. Default: 1/6

onset of response. Default: 0

time vector (in seconds). Must be equally spaced.

delay of response. Default: 6

response dispersion. Default: 1

delay of undershoot. Default: 16/6*a1 = 16

dispersion of undershoot. Default: b1 = 1

scale of undershoot. Default: 1/6

onset of response (in seconds). Default: 0

time vector

0 (default) for the HRF, 1 for the first derivative of the HRF, or 2 for the second derivative of the HRF.

delay of response. Default: 6

response dispersion. Default: 0.9

delay of undershoot. Default: 12

dispersion of undershoot. Default: 0.9

scale of undershoot. Default: 0.35

The explanatory variables i.e. the task stimulus information, from which a design matrix will be constructed. This is a list where each entry represents a task as a matrix of onsets (first column) and durations (second column) for each stimuli (each row) of the task, in seconds. List names should be the task names. nTime and TR are required.

An example of a properly-formatted EVs is: on_s1 <- list(taskA=cbind(on=c(1,9,17), dr=rep(1,3)), taskB=cbind(on=c(3,27), dr=rep(5,2))). In this example, there are two tasks: the first has three 1s-long stimuli, while the second has two 5s-long stimuli. with on_s2 formatted similarly to on_s1.

the number of timepoints (volumes) in the task fMRI data.

the temporal resolution of the data, in seconds.

Controls the extent of HRF derivatives modeling.

Set to 0 to only model the main HRF regressor (default), and not include its derivatives; set to 1 to model the temporal derivative too; or, set to 2 to model both the temporal and dispersion derivatives. If dHRF==0, there is one design column (field) per task. If dHRF==1, there are two fields per task. And if dHRF==2, there are three fields per task.

If there are several tasks and dHRF>0, the total number of design matrix columns may exceed five, which may require large computation times with INLA. The analysis can be adjusted by modeling the derivatives as nuisance signals rather than as fields. To do so, move the corresponding columns from the design matrix to the nuisance argument for BayesGLM.

Upsample factor for convolving stimulus boxcar or stick function with canonical HRF. Default: 100.

Add task regressors indicating the onset and/or offset of each event block? Provide the names of the tasks as a character vector. All onsets (or offsets) across the specified tasks will be represented by one additional column in the design matrix. The task names must match the names of EVs. Can also be "all" to use all tasks.

Onsets/offset modeling is only compatible with a block design experiment. An error will be raised if the events in EVs do not have duration greater than one second.

Scale the columns of the design matrix? Default: TRUE.

Model the onsets (onsets_sep) or offsets (offsets_sep) separately for each task? Default: FALSE, to model all onsets together, or all offsets together, as a single field in the design.

Print diagnostic messages? Default: TRUE.

Additional arguments to HRF_calc.

A session-length list of $T \times V$ numeric BOLD data.

Tolerance for mean and variance of each data location. Locations which do not meet these thresholds are masked out of the analysis. Defaults: 1e-6.

Print messages counting how many locations are removed? Default: TRUE.

A $V \times 3$ matrix, where each row contains the Euclidean coordinates at which a given vertex in the mesh is located. $V$ is the number of vertices in the mesh

An $F \times 3$ matrix, where each row contains the vertex indices for a given triangular face in the mesh. $F$ is the number of faces in the mesh.

A length $V$ logical vector indicating if each vertex is within the input mask.

The maximum number of threads to use in the inla-program for model estimation. 0 (default) will use the maximum number of threads allowed by the system.

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.

An "inla.mesh" object (see make_mesh for surface data)

An "inla.mesh" object (see make_mesh for surface data).

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.

A 3D numeric array that is locations by fields by designs.

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).

Temporal resolution of the data, in seconds.

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.

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.

(Optional) A $T \times N$ 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.

TO DO

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.

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.

Session-length list of numeric matrices/arrays, each with volumes along the first dimension.

TO DO

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.

Tolerance for mean, variance and SNR of each data location. Locations which do not meet these thresholds are masked out of the analysis. Default: 1e-6 for mean and variance, 50 for SNR.

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.

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.

(Optional) A $T \times N$ 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.

The timepoints by fields design matrix or data.frame.

"lineplot" (default) or "imageplot".

Additional arguments to plot_design_line or plot_design_image.

The name of a ColorBrewer palette (see RColorBrewer::brewer.pal.info and colorbrewer2.org), the name of a viridisLite palette, or a character vector of colors. Default: "Set1".

Parameters for ggplot2::geom_line. Defaults: "solid" linetype, 0.7 linewidth and 0.8 alpha. linetype can also be a vector of options with length matching the number of fields in design.

An object of class "act_BGLM"

Which field should be plotted? Give the numeric indices or the names. NULL (default) will show all fields. This argument overrides the idx argument to view_xifti.

If NULL, the field names associated with idx will be used.

Which session should be plotted? NULL (default) will use the first.

Additional arguments to view_xifti

An object of class "BfMRI_design".

Additional arguments to plot_design.

An object of class "BGLM"

TRUE for plotting Bayesian results, FALSE for plotting classical GLM results. Default: NULL, which will use the Bayesian results if available and the classical results if not.

Which field should be plotted? Give the numeric indices or the names. NULL (default) will show all fields. This argument overrides the idx argument to view_xifti.

If NULL, the field names associated with idx will be used.

Which session should be plotted? NULL (default) will use the first.

Overrides the zlim argument for view_xifti. Default: c(-1, 1).

Additional arguments to view_xifti

An object of class "BGLM2"

Which contrast should be plotted? Give the numeric indices or the names. NULL (default) will show all contrasts. This argument overrides the idx argument to view_xifti.

Estimates of the "contrasts" (default), or their thresholded "activations".

Overrides the zlim argument for view_xifti. Default: c(-1, 1).

Additional arguments to view_xifti

An object of class "prev_BGLM"

Which task should be plotted? Give the numeric indices or the names. NULL (default) will show all tasks. This argument overrides the idx argument to view_xifti.

Which session should be plotted? NULL (default) will use the first.

Color locations without any activation across all results (zero prevalence) the same color as the medial wall? Default: NULL to drop the zeros if only one idx is being plotted.

See view_xifti.

Additional arguments to view_xifti

List of activations from activations. All should have the same sessions, fields, and brainstructures.

If activations at multiple thresholds were computed, which threshold should be used for prevalence? Default: the first (lowest).

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.

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.

fMRI data as a locations by time ($V \times T$) numeric matrix.

Option for scaling the BOLD response.

Original means of the BOLD data. ONLY provide if data has already been centered.

\code{"mean"} scaling will scale the data to percent local signal change.

\code{"sd"} scaling will scale the data by local standard deviation.

\code{"none"} will only center the data, not scale it.

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.

Random seed (optional). Default: NULL.

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.

Object of class "act_BGLM".

further arguments passed to or from other methods.

Object of class "summary.act_BGLM".

Object of class "act_fit_bglm".

further arguments passed to or from other methods.

Object of class "summary.act_fit_bglm".

Object of class "BfMRI_design".

further arguments passed to or from other methods.

Object of class "summary.BfMRI_design".

Object of class "BGLM".

further arguments passed to or from other methods.

Object of class "summary.BGLM".

Object of class "BGLM2".

further arguments passed to or from other methods.

Object of class "summary.BGLM2".

Object of class "fit_bglm".

further arguments passed to or from other methods.

Object of class "summary.fit_bglm".

Object of class "fit_bglm2".

further arguments passed to or from other methods.

Object of class "summary.fit_bglm2".

Object of class "prev_BGLM".

further arguments passed to or from other methods.

Object of class "summary.prev_BGLM".

Object of class "prev_fit_bglm".

further arguments passed to or from other methods.

Object of class "summary.prev_fit_bglm".

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.

Temporal resolution of the data, in seconds.

(logical) should the INLA_model_obj within the result be trimmed to only what is necessary to use activations? Default: TRUE.

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.

An "inla.mesh" object (see make_mesh for surface data).

A $V \times 3$ matrix, where each row contains the Euclidean coordinates at which a given vertex in the mesh is located. $V$ is the number of vertices in the mesh

An array of 0s and 1s representing a volumetric mask

The spatial resolution in each direction, in mm. For example, c(2,2,2) indicates 2mm isotropic voxels.

For volumetric data, what order neighborhood around data locations to keep? (0 = no neighbors, 1 = 1st-order neighbors, 2 = 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.

For volumetric data, size of extra voxels layers around the bounding box, in terms of voxels. Set to NULL for no buffer.