BOLD Percent Signal Change

Compute BOLD percent signal change from explicit disjoint sampled baseline/task windows; motion, drift and hemodynamic modeling are not performed.

Description

BOLD percent signal change compares task and baseline mean signals.

BOLD percent signal change compares task and baseline mean signals. Window selection determines which samples contribute to the contrast.

The tool returns baselineMean (baseline mean); taskMean (task mean); percentSignalChange (percent signal change). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.

When to use BOLD Percent Signal Change

Use BOLD Percent Signal Change when you already have timesSeconds, signals, baselineStartSeconds, baselineEndSeconds, taskStartSeconds, taskEndSeconds and want baselineMean, taskMean, percentSignalChange. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.

How to use BOLD Percent Signal Change and understand its results

Enter timesSeconds, signals, baselineStartSeconds, baselineEndSeconds, taskStartSeconds, taskEndSeconds 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 baselineMean, taskMean, percentSignalChange. 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
timesSeconds	Times seconds	list of number	Required
signals	Signals	list of number	Required
baselineStartSeconds	Baseline start seconds	number	Required
baselineEndSeconds	Baseline end seconds	number	Required
taskStartSeconds	Task start seconds	number	Required
taskEndSeconds	Task end seconds	number	Required

Use increasing aligned timestamps and nonempty inclusive baseline and task windows, with baseline ending before task starts. Require positive baseline mean.

percent change = 100×(taskMean−baselineMean)/baselineMean

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

json

{
  "input": {
    "timesSeconds": [
      0,
      1,
      2,
      3
    ],
    "signals": [
      100,
      100,
      110,
      110
    ],
    "baselineStartSeconds": 0,
    "baselineEndSeconds": 1,
    "taskStartSeconds": 2,
    "taskEndSeconds": 3
  },
  "output": {
    "baselineMean": 100,
    "taskMean": 110,
    "percentSignalChange": 10
  }
}

Limitations and assumptions

This is a descriptive window contrast, without haemodynamic fitting, drift correction, motion correction or statistical significance.

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

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. GLM t-Statistic: Evaluate a GLM contrast using the full already-scaled coefficient covariance; no additional residual variance multiplier is applied.

Methodological background: Digital Library of Mathematical Functions: Quadrature.^[1]

References

Digital Library of Mathematical Functions: Quadrature — National Institute of Standards and Technology

Similar or alternative tools

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.
GLM t-Statistic
Evaluate a GLM contrast using the full already-scaled coefficient covariance; no additional residual variance multiplier is applied.