Convenience function for seed-based PLS analysis, commonly used for functional connectivity. This is Behavior PLS (method 3) where the "behavior" data is the time series from a seed region.
The result shows which voxels correlate/covary with the seed region, potentially differently across conditions.
Usage
seed_pls(
datamat_lst,
seed_data,
num_subj_lst,
num_cond,
cormode = 0L,
nperm = 1000,
nboot = 500,
...
)Arguments
- datamat_lst
List of data matrices (one per group). Each matrix has rows = subjects x conditions, columns = voxels/features.
- seed_data
Matrix of seed region data. Rows must match stacked datamat (subjects x conditions across all groups), columns are seed regions/voxels.
- num_subj_lst
Integer vector with number of subjects per group.
- num_cond
Number of conditions.
- cormode
Correlation mode:
- 0
Pearson correlation (default)
- 2
Covariance
- 4
Cosine angle
- 6
Dot product
- nperm
Number of permutations (default 1000).
- nboot
Number of bootstrap samples (default 500).
- ...
Additional arguments passed to
pls_analysis().
Details
Seed PLS identifies brain patterns that maximally correlate with activity in a seed region. Unlike standard functional connectivity, it can reveal condition-specific connectivity patterns.
To run seed PLS: 1 . Extract time series from your seed region(s) 2. Organize as a matrix: rows = observations (matching datamat), columns = seed(s) 3. Pass to this function
Examples
if (FALSE) { # \dontrun{
# Extract seed time series (e.g., from PCC)
seed_ts <- extract_roi(bold_data, pcc_mask)
# Run seed PLS
result <- seed_pls(
datamat_lst = list(brain_data),
seed_data = seed_ts,
num_subj_lst = 25,
num_cond = 3
)
# View connectivity patterns
plot_brain(result, lv = 1, what = "bsr", threshold = 3)
} # }