TFCE Value
Approximate nonnegative TFCE by bounded midpoint height integration over face-connected voxel clusters; extent is voxel count, not physical volume, and inference requires an external permutation null.
Description
Threshold-free cluster enhancement combines statistic height and connected extent over many levels.
Threshold-free cluster enhancement combines statistic height and connected extent over many levels. Numerical integration approximates that accumulation without choosing one cluster-forming threshold.
The tool returns tfceScores (tfce scores); heightStep (height step). Input measurements and model parameters are supplied by the caller; the tool computes the stated summary rather than acquiring those measurements.
When to use TFCE Value
Use TFCE Value when you already have voxelStatistics, dimensions, extentExponent, heightExponent, integrationSteps and want tfceScores, heightStep. This is useful for checking a supplied calculation, documenting a measurement convention, or comparing observations prepared on the same basis.
How to use TFCE Value and understand its results
- Enter voxelStatistics, dimensions, extentExponent, heightExponent, integrationSteps 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 tfceScores, heightStep. 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 |
|---|---|---|---|
| voxelStatistics | Voxel statistics | list of number | Required |
| dimensions | Dimensions | list of number | Required |
| extentExponent | Extent exponent | number | Required |
| heightExponent | Height exponent | number | Required |
| integrationSteps | Integration steps | integer | Required |
Use midpoint heights from zero to the maximum nonnegative statistic. At each height find six-connected clusters including values at least h, and add their contribution to every member. At most 128 levels and a bounded voxel-level product are allowed.
TFCE[v] ≈ Σ extent(h)^E × h^H × Δh The symbols refer to the supplied quantities or generated values described above; unit conversions and comparison boundaries follow the stated implementation.
{
"input": {
"voxelStatistics": [
2,
2
],
"dimensions": [
2,
1,
1
],
"extentExponent": 1,
"heightExponent": 0,
"integrationSteps": 2
},
"output": {
"tfceScores": [
4,
4
],
"heightStep": 1
}
}Limitations and assumptions
Scores are one-sided and numerical; no permutation distribution or corrected significance is produced. All-zero input gives all-zero scores.
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
Cluster Mass: Compute raw statistic mass, threshold-excess mass and peak for face-connected suprathreshold clusters; conventions are explicit and no null distribution is invented. Permutation Threshold: Calculate upper-tail max-statistic permutation p-values with the plus-one correction and a separately labeled interpolated reference quantile; decisions use p-values, not an incompatible quantile cutoff.
Methodological background: Threshold-free cluster enhancement.1
References
-
Threshold-free cluster enhancement — Smith and Nichols; FMRIB technical report
Similar or alternative tools
- Cluster Mass
Compute raw statistic mass, threshold-excess mass and peak for face-connected suprathreshold clusters; conventions are explicit and no null distribution is invented.
- Permutation Threshold
Calculate upper-tail max-statistic permutation p-values with the plus-one correction and a separately labeled interpolated reference quantile; decisions use p-values, not an incompatible quantile cutoff.