neuroim2 (version 0.8.5)

cluster_searchlight_series: Cluster-centroid searchlight over cluster time-series

Description

Iterate over clusters by their centroids and, for each seed cluster, return the time-series of the K nearest clusters (or those within a radius). This enables searchlight analysis at the cluster level rather than individual voxels.

Usage

cluster_searchlight_series(x, cvol = NULL, k = 10L, radius = NULL, label = "")

Value

A list of ROIVec objects, one per cluster, where each ROIVec contains:

values

A TxN matrix where T is the number of timepoints and N is the number of neighboring clusters (including the seed itself)

coords

The centroid coordinates of the neighboring clusters

The seed cluster's time-series is always the first column in each ROIVec.

Arguments

x

A `ClusteredNeuroVec` object or a `NeuroVec` plus `cvol`

cvol

A `ClusteredNeuroVol` (required if `x` is a `NeuroVec`)

k

Integer, number of nearest clusters including the seed (default: 10). Will be capped at the total number of clusters if specified value exceeds it.

radius

Numeric distance in mm. If given, use all clusters within this radius instead of k-nearest neighbors. Cannot be used together with k.

label

Optional character label for the returned windows

Details

The function creates a searchlight around each cluster's centroid, selecting either:

• The k nearest clusters (when k is specified)

• All clusters within a given radius (when radius is specified)

This is particularly useful for cluster-level connectivity analyses or when working with parcellated data where voxel-level searchlights would be redundant.

See Also

ClusteredNeuroVec for creating clustered neuroimaging vectors, searchlight for voxel-level searchlight analysis, ROIVec for the structure of returned windows

Examples

# Create synthetic 4D data (8x8x8 volume, 10 timepoints)
sp4 <- NeuroSpace(c(8,8,8,10), c(1,1,1))
arr <- array(rnorm(8*8*8*10), dim=c(8,8,8,10))
vec <- NeuroVec(arr, sp4)

# Create a mask covering most of the volume
sp3 <- NeuroSpace(c(8,8,8), c(1,1,1))
mask_arr <- array(FALSE, dim=c(8,8,8))
mask_arr[2:7, 2:7, 2:7] <- TRUE
mask <- LogicalNeuroVol(mask_arr, sp3)

# Assign voxels to 10 clusters
n_voxels <- sum(mask_arr)
clusters <- sample(1:10, n_voxels, replace=TRUE)
cvol <- ClusteredNeuroVol(mask, clusters)

# Create clustered representation
cv <- ClusteredNeuroVec(vec, cvol)

# Get cluster searchlight with 3 nearest neighbors
windows <- cluster_searchlight_series(cv, k = 3)
length(windows)  # 10 windows (one per cluster)

# Check first window
roi1 <- windows[[1]]
dim(values(roi1))  # 10 x 3 (timepoints x neighbors)

# Use radius-based neighborhoods (5mm radius)
windows_radius <- cluster_searchlight_series(cv, radius = 5)
# Each window may have different number of neighbors