By name: fmri-dictionary-learning-optimal-transport description: "Novel approach to dictionary learning on fMRI data that explicitly accounts for individual brain geometry variability using optimal transport (Fused Gromov-Wasserstein distance) with amortized optimization for computational efficiency." arxiv_id: "2605.20883" published: "2026-05-20" authors: "Sonia Mazelet, Rémi Flamary, Bertrand Thirion" tags: [dictionary-learning, fmri, optimal-transport, brain-geometry, fgw-distance, individual-variability, functional-alignment]
Learning fMRI Activation Dictionaries Across Individual Geometries via Optimal Transport
Core Concept
Introduces a novel dictionary learning approach for fMRI data that explicitly accounts for individual brain geometry variability using optimal transport (Fused Gromov-Wasserstein distance) instead of projecting all brains onto a common template. Uses amortized optimization to learn a neural network that predicts optimal transport plans at substantially reduced computational cost.
Key Contributions
Geometry-Aware Dictionary Learning: Unlike standard approaches that warp individual brains to a common template (losing subject-specific information), this method preserves individual geometry by comparing brains via optimal transport.
Fused Gromov-Wasserstein Distance: Uses FGW distance to compare fMRI graphs with different geometries and features, balancing feature alignment (node attributes) with structural consistency (graph topology).
Amortized Optimization: Learns a neural network to predict approximations of optimal transport plans, making FGW-based dictionary learning computationally feasible for large-scale fMRI graphs.
Controllable Geometric Abstraction: Dictionary atoms can be learned at different levels of geometric variability by varying the FGW trade-off parameter, controlling the balance between feature alignment and structural consistency.
Methodology
Framework
- Input: fMRI data from multiple subjects with individual brain geometries (3D coordinates, connectivity)
- Graph Construction: Each subject's brain → graph with node features (fMRI activations) and edge structure (spatial/functional connectivity)
- FGW Dictionary Learning:
- Compare graphs across individuals using FGW distance
- Learn dictionary atoms that balance feature alignment vs. structural consistency
- Trade-off parameter α controls this balance
- Amortized Transport: Neural network predicts optimal transport plans, avoiding expensive per-pair FGW computations
Key Innovation
Instead of: Project individual → common template → dictionary learning The method does: Dictionary learning directly on individual geometries, connected via optimal transport
Validation
- Dataset: HCP (Human Connectome Project) dataset
- Results: Captures different levels of geometric variability; preserves essential information for downstream tasks
Applications
- Population-level fMRI analysis: Identifying shared activation patterns while preserving individual geometry
- Brain state classification: Learned dictionaries provide interpretable representations
- Individual differences research: Understanding how brain geometry variability relates to functional differences
- Multi-subject fMRI alignment: Alternative to standard template-based registration
Relationship to Existing Methods
- Vs. Template-based registration: Preserves individual geometry rather than discarding it
- Vs. Hyperalignment: Different mathematical foundation (optimal transport vs. Procrustes)
- Vs. Shared response model: More flexible geometry handling
Activation Keywords
- fmri-dictionary-learning, optimal-transport-fmri, fgw-brain, individual-brain-geometry, fmri-alignment, amortized-transport, dictionary-learning-fmri, brain-graph-optimal-transport, fmri-subject-variability, fused-gromov-wasserstein-brain