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Pre-defined Hemodynamic Response Function Objects

HRF_objects.Rd

A collection of pre-defined HRF objects for common fMRI analysis scenarios. These objects can be used directly in model specifications or as templates for creating custom HRFs.

Usage

HRF_GAMMA(t, shape = 6, rate = 1)

HRF_GAUSSIAN(t, mean = 6, sd = 2)

HRF_SPMG1(t, P1 = 5, P2 = 15, A1 = 0.0833)

HRF_SPMG2(t)

HRF_SPMG3(t)

HRF_BSPLINE(t)

HRF_FIR(t)

Arguments

t

Numeric vector of time points (in seconds) at which to evaluate the HRF

shape, rate

Parameters for gamma distribution HRF (default: shape=6, rate=1)

mean, sd

Parameters for Gaussian HRF (default: mean=6, sd=2)

P1, P2

Shape parameters for SPM canonical HRF (default: P1=5, P2=15)

A1

Amplitude parameter for SPM canonical HRF (default: 0.0833)

Value

When called as functions, return numeric vectors or matrices of HRF values. When used as objects, they are HRF objects with class c("HRF", "function").

Canonical HRFs

HRF_SPMG1

SPM canonical HRF (single basis function)

HRF_SPMG2

SPM canonical HRF with temporal derivative (2 basis functions)

HRF_SPMG3

SPM canonical HRF with temporal and dispersion derivatives (3 basis functions)

HRF_GAMMA

Gamma function-based HRF

HRF_GAUSSIAN

Gaussian function-based HRF

Flexible Basis Sets

HRF_BSPLINE

B-spline basis HRF (5 basis functions)

HRF_FIR

Finite Impulse Response (FIR) basis HRF (12 basis functions)

Creating Custom Basis Sets

The pre-defined objects above have fixed numbers of basis functions. To create basis sets with custom parameters (e.g., different numbers of basis functions), use one of these approaches:

Using getHRF():

  • getHRF("fir", nbasis = 20) - FIR basis with 20 functions

  • getHRF("bspline", nbasis = 10, span = 30) - B-spline with 10 functions

  • getHRF("fourier", nbasis = 7) - Fourier basis with 7 functions

  • getHRF("daguerre", nbasis = 5, scale = 3) - Daguerre basis

Using generator functions directly:

  • hrf_fir_generator(nbasis = 20, span = 30)

  • hrf_bspline_generator(nbasis = 10, span = 30)

  • hrf_fourier_generator(nbasis = 7, span = 24)

  • hrf_daguerre_generator(nbasis = 5, scale = 3)

Usage

All HRF objects can be:

  • Called as functions with time argument: HRF_SPMG1(t)

  • Used in model specifications: hrf(condition, basis = HRF_SPMG1)

  • Evaluated with evaluate() method

  • Combined with decorators like lag_hrf() or block_hrf()

See also

evaluate.HRF for evaluating HRF objects, gen_hrf for creating HRFs with decorators, list_available_hrfs for listing all HRF types, getHRF for creating HRFs by name with custom parameters, hrf_fir_generator, hrf_bspline_generator, hrf_fourier_generator, hrf_daguerre_generator for creating custom basis sets directly

Other hrf: penalty_matrix()

Examples

# Evaluate HRFs at specific time points
times <- seq(0, 20, by = 0.5)

# Single basis canonical HRF
canonical_response <- HRF_SPMG1(times)
plot(times, canonical_response, type = "l", main = "SPM Canonical HRF")


# Multi-basis HRF with derivatives
multi_response <- HRF_SPMG3(times)  # Returns 3-column matrix
matplot(times, multi_response, type = "l", main = "SPM HRF with Derivatives")


# Gamma and Gaussian HRFs
gamma_response <- HRF_GAMMA(times)
gaussian_response <- HRF_GAUSSIAN(times)

# Compare different HRF shapes
plot(times, canonical_response, type = "l", col = "blue", 
     main = "HRF Comparison", ylab = "Response")
lines(times, gamma_response, col = "red")
lines(times, gaussian_response, col = "green")
legend("topright", c("SPM Canonical", "Gamma", "Gaussian"), 
       col = c("blue", "red", "green"), lty = 1)


# Create custom FIR basis with 20 bins
custom_fir <- getHRF("fir", nbasis = 20, span = 30)
fir_response <- evaluate(custom_fir, times)
matplot(times, fir_response, type = "l", main = "Custom FIR with 20 bins")


# Create custom B-spline basis  
custom_bspline <- hrf_bspline_generator(nbasis = 8, span = 25)
bspline_response <- evaluate(custom_bspline, times)
matplot(times, bspline_response, type = "l", main = "Custom B-spline with 8 basis functions")