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Weighted HRF (No Hemodynamic Delay)

Source: R/hrf-functions.R
hrf_weighted.Rd

Creates a flexible weighted HRF starting at t=0 with user-specified weights. Unlike traditional HRFs, this has no built-in hemodynamic delay - it directly maps weights to time points, allowing for arbitrary temporal response shapes.

Usage

hrf_weighted(
  weights,
  width = NULL,
  times = NULL,
  method = c("constant", "linear"),
  normalize = FALSE
)

Arguments

weights

Numeric vector of weights. Required.

width

Total duration of the window in seconds. If provided without times, weights are evenly spaced from 0 to width.

times

Numeric vector of time points (in seconds, relative to t=0) where weights are specified. Must be strictly increasing and start at 0 for consistency with other HRFs. If provided, width is ignored.

method

Interpolation method between time points:

"constant"

Step function - weight is constant until the next time point (default). Good for discrete time bins.

"linear"

Linear interpolation between points. Good for smooth weight transitions.

normalize

Logical; if TRUE, weights are scaled so they sum to 1 (for method = "constant") or integrate to 1 (for method = "linear"). This makes the regression coefficient interpretable as a weighted mean. Default is FALSE.

Value

An HRF object that can be used with regressor() and other fmrihrf functions.

Details

This is useful for extracting weighted averages of data at specific time points. When normalize = TRUE and the HRF is used in a GLM, the estimated coefficient represents a weighted mean of the data at the specified times.

There are two ways to specify the temporal structure:

  1. width + weights: Weights are evenly spaced from 0 to width

  2. times + weights: Explicit time points for each weight (relative to t=0)

For delayed windows (not starting at t=0), use lag_hrf to shift the weighted HRF in time.

Note on durations

The temporal structure (width or times) is fixed when the HRF is created. The duration parameter in regressor() does not modify the weighted HRF's structure—it controls how long the neural input is sustained (which then gets convolved with this HRF). For trial-varying weighted HRFs, use a list of HRFs:


hrf_early <- hrf_weighted(width = 6, weights = c(1, 1, 0, 0), normalize = TRUE)
hrf_late <- hrf_weighted(width = 6, weights = c(0, 0, 1, 1), normalize = TRUE)
reg <- regressor(onsets = c(0, 20), hrf = list(hrf_early, hrf_late))

See also

hrf_boxcar for simple uniform boxcars, lag_hrf to shift the window in time, empirical_hrf for HRFs from measured data

Other hrf_functions: hrf_basis_lwu(), hrf_boxcar(), hrf_bspline(), hrf_gamma(), hrf_gaussian(), hrf_inv_logit(), hrf_lwu(), hrf_mexhat(), hrf_sine(), hrf_spmg1(), hrf_time()

Examples

# Simple: 6s window with 4 evenly-spaced weights (at 0, 2, 4, 6s)
hrf1 <- hrf_weighted(width = 6, weights = c(0.2, 0.5, 0.8, 0.3))
#> Warning: Parameters times, weights, width, method, normalize are not arguments to function weighted[4 pts, constant] and will be ignored
t <- seq(-1, 10, by = 0.1)
plot(t, evaluate(hrf1, t), type = "s", main = "Weighted HRF (width + weights)")


# Explicit times for precise control
hrf2 <- hrf_weighted(
  times = c(0, 1, 3, 5, 6),
  weights = c(0.1, 0.5, 0.8, 0.5, 0.1),
  method = "linear"
)
#> Warning: Parameters times, weights, width, method, normalize are not arguments to function weighted[5 pts, linear] and will be ignored
plot(t, evaluate(hrf2, t), type = "l", main = "Smooth Weighted HRF")


# Normalized weights - coefficient estimates weighted mean of signal
hrf3 <- hrf_weighted(
  width = 8,
  weights = c(1, 2, 2, 1),
  normalize = TRUE
)
#> Warning: Parameters times, weights, width, method, normalize are not arguments to function weighted[4 pts, constant] and will be ignored

# Trial-varying weighted HRFs
hrf_early <- hrf_weighted(width = 6, weights = c(1, 1, 0, 0), normalize = TRUE)
#> Warning: Parameters times, weights, width, method, normalize are not arguments to function weighted[4 pts, constant] and will be ignored
hrf_late <- hrf_weighted(width = 6, weights = c(0, 0, 1, 1), normalize = TRUE)
#> Warning: Parameters times, weights, width, method, normalize are not arguments to function weighted[4 pts, constant] and will be ignored
reg <- regressor(onsets = c(0, 20), hrf = list(hrf_early, hrf_late))

# For delayed windows, use lag_hrf
hrf_delayed <- lag_hrf(hrf_weighted(width = 5, weights = c(1, 2, 1)), lag = 10)
#> Warning: Parameters times, weights, width, method, normalize are not arguments to function weighted[3 pts, constant] and will be ignored