neuroSCC (version 1.0.0)

getPoints: Extract Significant SCC Points from an SCC Comparison Object

Description

Identifies and extracts coordinates where differences fall outside the simultaneous confidence corridors (SCCs), indicating statistically significant regions. This function processes the results from ImageSCC::scc.image() and returns the voxel locations that represent either hypo- or hyperactivity.

Interpretation depends on the order of inputs in the SCC computation. If SCC was computed as scc.image(Ya = Y_AD, Yb = Y_CN, ...) (i.e., the Control group is the second argument).

• positivePoints — Regions where Control minus Pathological is significantly above the SCC. These correspond to areas where the Pathological group (AD) is hypoactive relative to Controls.

• negativePoints — Regions where Control minus Pathological is significantly below the SCC. These correspond to areas where the Pathological group is hyperactive relative to Controls.

Always confirm the order of Ya and Yb in the SCC computation to interpret the directionality correctly.

Usage

getPoints(sccResult)

Value

A named list with two elements

• positivePoints — A data frame with coordinates where the first group (Ya) shows significantly lower activity than the second group (Yb).

• negativePoints — A data frame with coordinates where the first group (Ya) shows significantly higher activity than the second group (Yb).

Arguments

sccResult

A list of SCC computation results, as returned by ImageSCC::scc.image. Must include the following components

• Z.band — A matrix specifying grid positions.

• ind.inside.cover — Indices of grid points within the confidence band.

• scc — A 3D array containing the computed SCC values.

See Also

ImageSCC::scc.image for SCC computation.

Examples

# Load precomputed SCC example
data("SCCcomp", package = "neuroSCC")

# Extract significant SCC points
significantPoints <- getPoints(SCCcomp)

# Show extracted points (interpretation depends on SCC setup; see description)
head(significantPoints$positivePoints)  # Pathological hypoactive vs. Control
head(significantPoints$negativePoints)  # Pathological hyperactive vs. Control