Multiview Information Bottleneck Brain Hoi

---
name: multiview-information-bottleneck-brain-hoi
description: "Multi-view Information Bottleneck framework for modeling higher-order interactions (HOIs) in resting-state fMRI for psychiatric diagnosis. Captures complex brain dynamics beyond pairwise connectivity without predefined hyperedges."
---

# Multi-View Information Bottleneck for Higher-Order Brain Interactions

> Information-theoretic approach for learning higher-order brain interactions from multi-view fMRI data using the Information Bottleneck principle.

## Metadata
- **Source**: arXiv:2604.17713v1
- **Title**: Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis
- **Authors**: Kunyu Zhang, Qiang Li, Vince D. Calhoun, et al.
- **Published**: 2026-04-20
- **Category**: Neuroscience/fMRI Analysis

## Core Methodology

### Problem Context
Resting-state fMRI is crucial for psychiatric diagnosis, but most approaches rely on pairwise connectivities that overlook higher-order interactions (HOIs) central to complex brain dynamics. Hypergraph methods require predefined hyperedges.

### Information Bottleneck Solution
**Multi-View IB Framework**:
1. **Information Bottleneck principle**: Trade-off between compression and prediction
2. **Multi-view learning**: Multiple perspectives on brain data
3. **HOI discovery**: Learn higher-order interactions from data
4. **No predefined hyperedges**: Flexible structure learning

### Key Innovation
- **IB-based HOI modeling**: Information-theoretic higher-order interaction discovery
- **Multi-view integration**: Combines multiple fMRI representations
- **Flexible structure**: No fixed hypergraph topology required
- **Psychiatric diagnosis**: Application to mental health classification

## Technical Framework

### Information Bottleneck Principle
```
Objective: max I(T; Y) - β·I(T; X)

Where:
- X: Input fMRI data (multi-view)
- T: Compressed representation (bottleneck)
- Y: Target label (diagnosis)
- β: Compression-prediction trade-off
```

### Multi-View Architecture
```
fMRI Data View 1 ──┐
fMRI Data View 2 ──┼──> Information Bottleneck ──> HOI Representation ──> Classification
fMRI Data View 3 ──┘
```

### Higher-Order Interaction Learning
- **Synergistic information**: Information in joint distribution
- **Redundant information**: Shared across views
- **Unique information**: View-specific contributions
- **HOI extraction**: Beyond pairwise correlations

## Implementation Guide

### Prerequisites
- Resting-state fMRI data
- Multiple preprocessing pipelines (for multi-view)
- Python with PyTorch/TensorFlow
- nilearn, scikit-learn for neuroimaging

### Steps
1. **Data preparation**: Multi-view fMRI preprocessing
2. **View creation**: Different atlases, bandpass filters, parcellations
3. **IB optimization**: Train bottleneck representation
4. **HOI extraction**: Compute higher-order interaction strengths
5. **Classification**: Psychiatric diagnosis using HOI features

### Code Structure
```python
import torch
from information_bottleneck import MultiViewIB

# Initialize multi-view IB
mib = MultiViewIB(
    n_views=3,
    input_dims=[116, 200, 400],  # Different parcellations
    hidden_dim=128,
    beta=0.5
)

# Multi-view fMRI data
views = [fmri_view1, fmri_view2, fmri_view3]

# Forward pass
bottleneck, hoi_features = mib(views)

# Classification
logits = classifier(hoi_features)
loss = classification_loss + mib.information_loss

# HOI analysis
hoi_strength = mib.extract_hoi_interactions()
```

## Applications
- Psychiatric diagnosis (depression, schizophrenia, ADHD)
- Brain connectivity analysis
- Higher-order interaction discovery
- Multi-modal neuroimaging fusion
- Mental health biomarker discovery

## Information-Theoretic Metrics
- **Multi-information**: Total correlation in HOI
- **Synergy**: Information gain from joint consideration
- **Redundancy**: Overlapping information across views
- **Integration**: Measures of HOI strength

## Related Skills
- multi-view-o-information-brain-dynamics
- multi-view-o-information-brain-hoi
- functional-connectivity-graph-neural-networks

## References
- arXiv:2604.17713v1
- Tishby et al. Information Bottleneck method