Advanced RSA Methods: Feature-Based and Vector-Based Approaches

## Warning in rgl.init(initValue, onlyNULL): RGL: unable to open X11 display

## Warning: 'rgl.init' failed, will use the null device.
## See '?rgl.useNULL' for ways to avoid this warning.

Introduction

While standard Representational Similarity Analysis (RSA) compares neural patterns with model-based similarity structures, there are cases where more specialized approaches are needed. The rMVPA package provides two advanced RSA methods:

Feature-Based RSA: Projects neural patterns into a predefined feature space
Vector-Based RSA: Directly compares vectorized distance matrices

This vignette explains when and why to use these specialized approaches instead of standard RSA.

Feature-Based RSA

Overview

Feature-Based RSA is designed for cases where:

You have a rich feature space describing your stimuli
You want to directly map neural patterns to these features
You’re interested in reconstructing stimulus features from neural patterns

Unlike standard RSA which compares similarity matrices, Feature-Based RSA attempts to find a mapping between neural patterns and a feature space.

Key Differences from Standard RSA

Direct Feature Mapping:
- Standard RSA: Compares similarity structures
- Feature RSA: Maps neural patterns to feature dimensions
Output:
- Standard RSA: Correlation/regression coefficients between RDMs
- Feature RSA: Predicted feature values for each stimulus
Use Cases:
- Standard RSA: Testing theoretical models of representation
- Feature RSA: Reconstructing stimulus features from brain activity

Implementation Example

Let’s walk through a complete example:

# Generate a synthetic dataset with dimensions 6x6x6, 50 observations, divided into 4 blocks
# This ensures that the number of trials matches the number of stimuli used below
data_out <- rMVPA::gen_sample_dataset(D = c(6,6,6), nobs = 50, blocks = 4, nlevels = 2)
print(data_out)

## $dataset
## 
##  █▀▀ MVPA Dataset ▀▀█ 
## 
## ├─ Training Data 
## │  ├─ Dimensions:  6 × 6 × 6 × 50 observations 
## │  └─ Type:  DenseNeuroVec 
## ├─ Test Data 
## │  └─  None 
## └─ Mask Information 
##    ├─ Areas:  1 : 120 
##    └─ Active voxels/vertices:  120 
## 
## 
## $design
## 
##  █▀▀ MVPA Design ▀▀█ 
## 
## ├─ Training Data 
## │  ├─ Observations:  50 
## │  ├─ Response Type:  Factor
## │  ├─ Levels:  a, b 
## │  └─ Class Distribution:  a: 25, b: 25 
## ├─ Test Data 
## │  └─  None 
## └─ Structure 
##    ├─ Blocking:  Present
##    ├─ Number of Blocks:  4 
##    ├─ Mean Block Size:  12  (SD:  0.58 ) 
##    └─ Split Groups:  None

# Generate synthetic feature space (e.g., visual features of stimuli)
set.seed(123)
n_stimuli <- 50
n_features <- 5

# Create feature matrix (stimuli × features)
feature_matrix <- matrix(rnorm(n_stimuli * n_features), n_stimuli, n_features)
colnames(feature_matrix) <- paste0("feature_", 1:n_features)

# Create stimulus labels
stim_labels <- paste0("stim_", 1:n_stimuli)

# Create feature RSA design
feature_design <- feature_rsa_design(
  F = feature_matrix,  # Direct feature matrix
  labels = stim_labels
)

# Create MVPA dataset from the generated data
dset <- mvpa_dataset(data_out$dataset$train_data, mask = data_out$dataset$mask)

# Create cross-validation structure using the block information
crossval <- blocked_cross_validation(data_out$design$block_var)

# Create feature RSA model
feature_model <- feature_rsa_model(
  dataset = dset,
  design = feature_design,
  method = "scca",  # Sparse Canonical Correlation Analysis
  crossval = crossval  # Add cross-validation
)

# Create proper region mask from the dataset's mask
mask_vol <- data_out$dataset$mask
nvox <- sum(mask_vol)
region_mask <- neuroim2::NeuroVol(
  sample(1:3, size = nvox, replace = TRUE),  # 3 regions
  space(mask_vol),
  indices = which(mask_vol > 0)
)

# Run regional analysis
results <- run_regional(feature_model, region_mask)

## Error in value[[3L]](cond) : 
##   SCCA error: there is no package called 'whitening'
## Error in value[[3L]](cond) : 
##   SCCA error: there is no package called 'whitening'
## Error in value[[3L]](cond) : 
##   SCCA error: there is no package called 'whitening'
## INFO [2025-03-20 21:30:40] 
## ✨ MVPA Iteration Complete
## ├─ Total ROIs: 3
## ├─ Processed: 3
## └─ Skipped: 0

## Warning: Performance matrix is empty or invalid. Returning empty results.

# Examine results
print(results$performance_table)

## # A tibble: 3 × 1
##   roinum
##    <int>
## 1      1
## 2      2
## 3      3

Available Methods

Feature-Based RSA supports multiple analysis methods:

Sparse Canonical Correlation Analysis (SCCA):
- Finds canonical correlations between neural patterns and features
- Useful when features may be correlated
Partial Least Squares (PLS):
- Projects data to maximize covariance with features
- Good for high-dimensional feature spaces
PCA Regression:
- Reduces neural patterns via PCA then predicts features
- Useful for very high-dimensional neural data

# Compare different methods
methods <- c("scca", "pca") ## pls is not implemented yet
results_list <- lapply(methods, function(method) {
  model <- feature_rsa_model(
    dataset = dset,
    design = feature_design,
    method = method,
    crossval = crossval  # Add cross-validation
  )
  run_regional(model, region_mask)
})

## Error in value[[3L]](cond) : 
##   SCCA error: there is no package called 'whitening'
## Error in value[[3L]](cond) : 
##   SCCA error: there is no package called 'whitening'
## Error in value[[3L]](cond) : 
##   SCCA error: there is no package called 'whitening'
## INFO [2025-03-20 21:30:41] 
## ✨ MVPA Iteration Complete
## ├─ Total ROIs: 3
## ├─ Processed: 3
## └─ Skipped: 0

## Warning: Performance matrix is empty or invalid. Returning empty results.

## INFO [2025-03-20 21:30:42] 
## ✨ MVPA Iteration Complete
## ├─ Total ROIs: 3
## ├─ Processed: 3
## └─ Skipped: 0

# Compare performance
for (i in seq_along(methods)) {
  cat("\nMethod:", methods[i], "\n")
  print(results_list[[i]]$performance_table)
}

## 
## Method: scca 
## # A tibble: 3 × 1
##   roinum
##    <int>
## 1      1
## 2      2
## 3      3
## 
## Method: pca 
## # A tibble: 3 × 6
##   roinum mean_correlation spatial_specificity voxel_correlation   mse r_squared
##    <int>            <dbl>               <dbl>             <dbl> <dbl>     <dbl>
## 1      1          -0.0652             -0.0656          -0.00515  1.13    -0.183
## 2      2          -0.109              -0.112           -0.0593   1.18    -0.215
## 3      3          -0.0693             -0.0687          -0.00189  1.18    -0.184

Vector-Based RSA

Overview

Vector-Based RSA is designed for cases where:

You have pre-computed distance matrices
You want to compare across-block distances only
You need efficient computation for large datasets

Unlike standard RSA which works with full similarity matrices, Vector-Based RSA operates on vectorized distance matrices and can efficiently handle block structure.

Key Differences from Standard RSA

Computation:
- Standard RSA: Works with full similarity matrices
- Vector RSA: Works with vectorized distances
Block Handling:
- Standard RSA: Manual block exclusion
- Vector RSA: Built-in efficient block handling
Memory Usage:
- Standard RSA: Stores full matrices
- Vector RSA: Stores only necessary comparisons

Implementation Example

# Create distance matrix for stimuli
stim_distances <- as.matrix(dist(feature_matrix))
rownames(stim_distances) <- stim_labels

# Create block structure (e.g., runs)
blocks <- rep(1:5, length.out = n_stimuli)

# Create vector RSA design
vector_design <- vector_rsa_design(
    D = stim_distances,
    labels = stim_labels,
    block_var = blocks
)

# Create vector RSA model
vector_model <- vector_rsa_model(
  dataset = dset,
  design = vector_design,
  distfun = cordist(),  # Correlation distance
  rsa_simfun = "pearson"
)

# Run analysis
results_vector <- run_regional(vector_model, region_mask)

## INFO [2025-03-20 21:30:43] 
## ✨ MVPA Iteration Complete
## ├─ Total ROIs: 3
## ├─ Processed: 3
## └─ Skipped: 0

# Examine results
print(results_vector$performance_table)

## # A tibble: 0 × 0

Efficient Block Handling

Vector-Based RSA automatically handles block structure:

# Compare with different block structures
block_sizes <- c(5, 10)
results_blocks <- lapply(block_sizes, function(size) {
  blocks <- rep(1:(n_stimuli/size), each = size)
  design <- vector_rsa_design(
    D = stim_distances,
    labels = stim_labels,
    block_var = blocks
  )
  model <- vector_rsa_model(
    dataset = dset,
    design = design,
    distfun = cordist()
  )
  run_regional(model, region_mask)
})

## INFO [2025-03-20 21:30:43] 
## ✨ MVPA Iteration Complete
## ├─ Total ROIs: 3
## ├─ Processed: 3
## └─ Skipped: 0
## INFO [2025-03-20 21:30:44] 
## ✨ MVPA Iteration Complete
## ├─ Total ROIs: 3
## ├─ Processed: 3
## └─ Skipped: 0

# Compare results
for (i in seq_along(block_sizes)) {
  cat("\nBlock size:", block_sizes[i], "\n")
  print(results_blocks[[i]]$performance_table)
}

## 
## Block size: 5 
## # A tibble: 0 × 0
## 
## Block size: 10 
## # A tibble: 0 × 0

When to Use Each Method

Feature-Based RSA

Feature-Based RSA is most appropriate when working with a well-defined feature space where the dimensions have meaningful interpretations. This approach excels at reconstructing and predicting specific stimulus features from neural activity patterns. Common applications include predicting visual features from visual cortex responses, reconstructing semantic dimensions from language area activity, and mapping motion parameters from MT/V5 activation patterns.

Vector-Based RSA

Vector-Based RSA is ideal for analyzing large datasets with pre-computed distance matrices. It handles memory efficiently and provides sophisticated block handling capabilities. This makes it particularly valuable when comparing across multiple experimental runs, working with high-resolution fMRI data, or analyzing large-scale similarity structures.

Standard RSA

Stick with standard RSA when: - You’re testing theoretical models - You want to compare similarity structures - Block structure isn’t critical - Memory usage isn’t a concern

Summary

The rMVPA package provides three complementary RSA approaches:

Standard RSA: General-purpose similarity analysis
Feature-Based RSA: Direct feature mapping and reconstruction
Vector-Based RSA: Efficient similarity comparison with block handling

Your choice of RSA method should align with your research goals, match your data’s structure, fit within computational constraints, and reflect whether you’re analyzing feature spaces or similarity patterns.

For implementation details, refer to: - feature_rsa_model.R - vector_rsa_model.R - rsa_model.R

Bradley Buchsbaum

2025-03-20

Introduction

Feature-Based RSA

Overview

Key Differences from Standard RSA

Implementation Example

Available Methods

Vector-Based RSA

Overview

Key Differences from Standard RSA

Implementation Example

Efficient Block Handling

When to Use Each Method

Feature-Based RSA

Vector-Based RSA

Standard RSA

Summary