Multi View O Information Brain Networks

---
name: multi-view-o-information-brain-networks
description: "Higher-order brain interaction analysis using O-information and Multi-View Information Bottleneck for fMRI-based psychiatric diagnosis. Decomposes multivariate neural interactions into redundant and synergistic components across multiple brain views for improved diagnostic classification. (arXiv:2604.17713, April 2026)"
tags: [O-information, higher-order interactions, brain networks, fMRI, psychiatric diagnosis, information bottleneck, multi-view learning, redundancy, synergy]
---

# Multi-View O-Information for Higher-Order Brain Network Interactions

**arXiv:** 2604.17713 (April 20, 2026)
**Categories:** cs.LG

## Summary

A multi-view information-theoretic framework for analyzing higher-order brain interactions in fMRI data for psychiatric diagnosis. Uses O-information to decompose multivariate neural dependencies into redundancy-dominated and synergy-dominated interactions, combined with a Multi-View Information Bottleneck (MVIB) approach that integrates multiple brain views for improved diagnostic classification of psychiatric conditions.

## Key Methodology

### O-Information Framework
1. **Higher-Order Interactions:** O-information quantifies whether a system of N variables exhibits redundancy-dominated (shared information) or synergy-dominated (emergent information) interactions
2. **Decomposition:** O-information decomposes total correlation into redundant and synergistic components
3. **Neural Application:** Applied to fMRI BOLD time series to characterize brain region interactions beyond pairwise connectivity

### Multi-View Information Bottleneck
1. **Multi-View Formulation:** Treat different brain network views (functional networks, anatomical regions, frequency bands) as separate views
2. **Compression-Extraction:** Information bottleneck principle compresses each view while preserving diagnostic-relevant information
3. **Fusion Strategy:** Learned representations from multiple views are fused for downstream classification
4. **Diagnostic Task:** Applied to psychiatric disorder classification (schizophrenia, depression, ADHD)

### Technical Pipeline
1. **fMRI Preprocessing:** Standard pipeline (motion correction, normalization, smoothing)
2. **Brain Parcellation:** ROI extraction using established atlases (AAL, Schaefer, etc.)
3. **O-Information Computation:** Calculate O-information for brain region subsets
4. **Feature Extraction:** Redundancy/synergy features from O-information decomposition
5. **Multi-View Fusion:** MVIB combines features from different brain views
6. **Classification:** Diagnostic prediction with cross-validation

### Key Findings
- Higher-order interactions capture diagnostic information missed by pairwise connectivity
- Redundancy-dominated interactions distinguish psychiatric subtypes
- Multi-view fusion significantly improves classification accuracy over single-view approaches
- O-information provides interpretable biomarkers for clinical understanding

## Practical Applications

### When to Use This Approach
- Psychiatric disorder classification from fMRI data
- Analyzing higher-order brain interactions beyond pairwise connectivity
- Multi-modal brain data fusion for clinical diagnosis
- Understanding redundancy vs synergy in brain networks

### Implementation Steps
1. Extract ROI time series from preprocessed fMRI
2. Compute O-information for region triplets and higher-order subsets
3. Classify interactions as redundancy- or synergy-dominated
4. Define brain views (network-level, hemisphere, frequency band)
5. Train MVIB model with view-specific encoders
6. Fuse compressed representations for classification
7. Evaluate with cross-validation and interpret via O-information maps

## Limitations & Considerations

- **Computational Cost:** O-information scales combinatorially with number of regions
- **Sample Size:** Requires substantial fMRI datasets for reliable estimation
- **Parcellation Sensitivity:** Results depend on brain parcellation choice
- **Clinical Validation:** Further validation needed for clinical deployment

## Related Skills
- `brain-higher-order-structures` — Higher-order brain network analysis
- `multimodal-brain-connectivity-gnn` — Multimodal brain connectivity
- `brain-network-controllability` — Brain network control theory