GLM HRF Convolution

Compute causal same-length discrete convolution with explicit Δt scaling for a sampled stimulus-rate function; event impulses must be converted to rate samples and no GLM is fitted.

Description

A causal stimulus-HRF convolution accumulates contributions only from present and earlier stimulus samples.

A causal stimulus-HRF convolution accumulates contributions only from present and earlier stimulus samples. Multiplication by sample spacing approximates a continuous-time convolution integral.

The tool returns convolvedSignal (convolved signal). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.

When to use GLM HRF Convolution

Use GLM HRF Convolution when you already have stimulusSamples, hrfSamples, sampleIntervalSeconds and want convolvedSignal. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.

How to use GLM HRF Convolution and understand its results

Enter stimulusSamples, hrfSamples, sampleIntervalSeconds using the field table below. Match the units, ordering and data boundaries described in the calculation.
Run the tool after checking the stated bounds and matching paired observations or categories. A malformed request gives a tool error rather than a usable measurement.
Read convolvedSignal. Use the calculation and interpretation below to distinguish amounts, ratios, signs and decision flags where applicable.
Review the supplied parameters and assumptions before using the result in a report or comparing it with another observation.

Input field	Meaning	Value type and bounds	Requirement
stimulusSamples	Stimulus samples	list of number	Required
hrfSamples	Hrf samples	list of number	Required
sampleIntervalSeconds	Sample interval seconds	number	Required

Use nonempty vectors within the pairwise work budget and a positive common interval in seconds. Return the same number of samples as the stimulus, assuming zeros before its start.

output[i] = dt × Σk≤i stimulus[i−k]×HRF[k]

The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.

json

{
  "input": {
    "stimulusSamples": [
      1,
      0,
      1
    ],
    "hrfSamples": [
      1,
      0.5
    ],
    "sampleIntervalSeconds": 1
  },
  "output": {
    "convolvedSignal": [
      1,
      0.5,
      1
    ]
  }
}

Limitations and assumptions

The late convolution tail is truncated. No HRF normalization, event sampling or GLM fitting occurs.

Numerical inputs must be finite. The tool checks its declared input shape and calculation constraints; those checks do not verify the quality, calibration or representativeness of the supplied observations.

Alternative or Complementary methods

HRF Peak: Evaluate a supplied double-gamma HRF and identify its unique sampled peak; integer gamma shapes and caller scale/undershoot ratio define the model, not a universal response. BOLD Percent Signal Change: Compute BOLD percent signal change from explicit disjoint sampled baseline/task windows; motion, drift and hemodynamic modeling are not performed.

Methodological background: fMRI model specification: haemodynamic response functions and derivatives.^[1]

References

fMRI model specification: haemodynamic response functions and derivatives — SPM developers, University College London

Similar or alternative tools

HRF Peak
Evaluate a supplied double-gamma HRF and identify its unique sampled peak; integer gamma shapes and caller scale/undershoot ratio define the model, not a universal response.
BOLD Percent Signal Change
Compute BOLD percent signal change from explicit disjoint sampled baseline/task windows; motion, drift and hemodynamic modeling are not performed.