Representational Similarity Analysis (RSA) in rMVPA
Bradley Buchsbaum
2025-09-28
RSA.RmdIntroduction to Representational Similarity Analysis
Representational Similarity Analysis (RSA) compares neural activity
patterns with computational models by measuring pattern similarities,
matching them against model predictions, and quantifying how well models
explain the neural data. The rMVPA package implements this
technique for neuroimaging analysis.
Basic concepts
Dissimilarity matrices
A dissimilarity matrix represents pairwise differences between conditions or stimuli. Each cell (i, j) quantifies how different two conditions are, and the matrix can be derived from either neural data or theoretical models. Common measures include correlation distance (1 − correlation) and Euclidean distance.
Step-by-Step Example
1. Creating Sample Data
Let’s create a simple example dataset with known structure:
# Generate a sample dataset (20x20x8 volume, 80 observations, 4 blocks)
dataset <- rMVPA::gen_sample_dataset(D=c(20,20,8), nobs = 80, blocks=4)3. Creating an RSA Design
The RSA design specifies how to compare neural and model dissimilarity patterns:
# Basic design with one model RDM
basic_design <- rsa_design(
formula = ~ model_rdm,
data = list(model_rdm = model_rdm),
block_var = factor(dataset$design$block_var)
)
# Design with multiple model RDMs
model_rdm2 <- dist(matrix(rnorm(80*10), 80, 10))
complex_design <- rsa_design(
formula = ~ model_rdm + model_rdm2,
data = list(
model_rdm = model_rdm,
model_rdm2 = model_rdm2
),
block_var = factor(dataset$design$block_var),
keep_intra_run = FALSE # Exclude within-run comparisons
)4. Creating and Running an RSA Model
The rsa_model() function supports different methods for
computing neural dissimilarities and analyzing relationships:
# Create MVPA dataset
dset <- mvpa_dataset(dataset$dataset$train_data, mask=dataset$dataset$mask)
# Create RSA model with different options
rsa_spearman <- rsa_model(
dataset = dset,
design = basic_design,
distmethod = "spearman", # Method for computing neural dissimilarities
regtype = "spearman" # Method for comparing neural and model RDMs
)
# Run searchlight analysis
results <- run_searchlight(
rsa_spearman,
radius = 4,
method = "standard"
)## INFO [2025-09-28 22:05:10] Running standard searchlight with radius = 4
## INFO [2025-09-28 22:05:10] creating standard searchlight
## INFO [2025-09-28 22:05:10] running standard searchlight iterator
## INFO [2025-09-28 22:05:10] Processing batch 1/11 (24 ROIs in this batch)
## INFO [2025-09-28 22:05:11] Processing batch 2/11 (24 ROIs in this batch)
## INFO [2025-09-28 22:05:13] Processing batch 3/11 (24 ROIs in this batch)
## INFO [2025-09-28 22:05:14] Processing batch 4/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:15] Processing batch 5/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:17] Processing batch 6/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:18] Processing batch 7/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:19] Processing batch 8/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:21] Processing batch 9/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:22] Processing batch 10/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:23] Processing batch 11/11 (23 ROIs in this batch)
## INFO [2025-09-28 22:05:24]
## MVPA Iteration Complete
## - Total ROIs: 256
## - Processed: 256
## - Skipped: 0Advanced Features
Multiple Comparison Methods
rMVPA supports several methods for comparing neural and
model RDMs:
# Pearson correlation
rsa_pearson <- rsa_model(dset, basic_design,
distmethod = "pearson",
regtype = "pearson")
# Linear regression
rsa_lm <- rsa_model(dset, basic_design,
distmethod = "spearman",
regtype = "lm")
# Rank-based regression
rsa_rfit <- rsa_model(dset, basic_design,
distmethod = "spearman",
regtype = "rfit")Handling Run Structure
RSA can account for the run/block structure of fMRI data. A critical consideration in fMRI analysis is whether to include comparisons between patterns from the same run.
Understanding keep_intra_run
The keep_intra_run = FALSE parameter tells RSA to
exclude comparisons between patterns within the same run/block. This is
important because:
- Temporal Autocorrelation: BOLD responses within the same run are temporally autocorrelated
- Scanner Drift: Within-run patterns may share scanner drift effects
- Physiological Noise: Within-run patterns may share structured noise from breathing, heart rate, etc.
Here’s a visualization of what keep_intra_run = FALSE
does:
# Create a small example with 2 runs, 4 trials each
mini_data <- matrix(1:8, ncol=1) # Trial numbers 1-8
run_labels <- c(1,1,1,1, 2,2,2,2) # Two runs with 4 trials each
# Create distance matrix
d <- dist(mini_data)
d_mat <- as.matrix(d)
# Show which comparisons are kept (TRUE) or excluded (FALSE)
comparison_matrix <- outer(run_labels, run_labels, "!=")
# Only show lower triangle to match distance matrix structure
comparison_matrix[upper.tri(comparison_matrix)] <- NA
# Display the matrices
cat("Trial numbers:\n")## Trial numbers:## [1] 2 3 4 5 6 7 8 3 4 5 6 7 8 4 5 6 7 8 5 6 7 8 6 7 8 7 8 8
cat("\nRun comparisons (TRUE = across-run, FALSE = within-run):\n")##
## Run comparisons (TRUE = across-run, FALSE = within-run):## [1] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE
## [13] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [25] FALSE FALSE FALSE FALSEWhen we create an RSA design with
keep_intra_run = FALSE:
# Create design excluding within-run comparisons
blocked_design <- rsa_design(
formula = ~ model_rdm,
data = list(model_rdm = model_rdm),
block_var = factor(dataset$design$block_var),
keep_intra_run = FALSE # Exclude within-run comparisons
)This creates an RSA design that includes only between‑run comparisons, excludes within‑run pairs, and focuses the analysis on more reliable across‑run similarities.
When to use keep_intra_run = FALSE
Set keep_intra_run = FALSE when your design includes
multiple runs and you want to control for temporal autocorrelation and
run‑specific noise; this is the conservative choice for most
confirmatory analyses. Keeping within‑run comparisons
(keep_intra_run = TRUE, the default) can be reasonable in
short‑run designs, when sample size is limited, or for exploratory work
where maximizing comparisons is more important than strict control of
temporal structure.
Visualizing Results
You can examine and visualize the RSA results:
# Extract the searchlight map
rsa_map <- results$results$model_rdm
# Compute range of correlation values
rsa_values <- neuroim2::values(rsa_map)
range(rsa_values, na.rm = TRUE)## [1] -0.03370494 0.03541486
# Basic summary of the searchlight result
print(results)##
## Searchlight Analysis Results
##
## - Coverage
## - Voxels/Vertices in Mask: 3,200
## - Voxels/Vertices with Results: 256
## - Output Maps (Metrics)
## - model_rdm (Type: DenseNeuroVol )
# Save results (commented out)
# neuroim2::write_vol(rsa_map, "RSA_results.nii.gz")Summary
The rMVPA package provides a comprehensive RSA implementation with flexible model specification, multiple dissimilarity computation methods, and support for complex experimental designs with run/block structures. It integrates seamlessly with searchlight analysis and offers various statistical approaches including correlation, regression, and rank-based methods.
When using RSA in rMVPA, carefully consider your experimental design when setting block variables and intra-run parameters, choose distance methods that match your theoretical framework, and select statistical approaches appropriate for your analysis goals.