Perform LSS using Voxel-wise HRFs

Computes trial-wise beta estimates using voxel-specific HRFs.

Usage

lss_with_hrf(
  Y,
  events,
  hrf_estimates,
  nuisance_regs = NULL,
  engine = "R",
  chunk_size = 5000,
  verbose = TRUE,
  backing_dir = NULL
)

Arguments

Y

Numeric matrix of BOLD data (time x voxels).

events

Data frame with onset, duration and condition columns.

hrf_estimates

A VoxelHRF object returned by estimate_voxel_hrf.

nuisance_regs

Optional numeric matrix of nuisance regressors.

engine

Computational engine: "R" for pure R implementation (default), "C++" for optimized C++ (experimental).

chunk_size

Number of voxels to process per batch (C++ engine only).

verbose

Logical; display progress bar.

backing_dir

Directory for bigmemory backing files. If NULL, a temporary directory is used (C++ engine only).

Value

An object of class LSSBeta for C++ engine, or a numeric matrix (n_trials x n_vox) for R engine.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
Y <- matrix(rnorm(100), 50, 2)
events <- data.frame(onset = c(5, 25), duration = 1,
                     condition = "A")
basis <- fmrihrf::hrf_gamma()
sframe <- fmrihrf::sampling_frame(blocklens = nrow(Y), TR = 1)
times <- fmrihrf::samples(sframe, global = TRUE)
rset <- fmrihrf::regressor_set(onsets = events$onset,
                               fac = factor(1:nrow(events)),
                               hrf = basis, duration = events$duration,
                               span = 30)
X <- fmrihrf::evaluate(rset, grid = times, precision = 0.1, method = "conv")
coef <- matrix(rnorm(ncol(X) * ncol(Y)), ncol(X), ncol(Y))
Y <- X %*% coef + Y * 0.1
est <- estimate_voxel_hrf(Y, events, basis)
betas <- lss_with_hrf(Y, events, est, verbose = FALSE, engine = "R")
dim(betas)
} # }