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npx versuz@latest install hiyenwong-ai-collection-collection-skills-fusion-searchlight-fmrigit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-fusion-searchlight-fmri/SKILL.md---
name: fusion-searchlight-for-rs-fmri-data-integration
description: **Source:** arXiv:2412.10161
---
# Fusion Searchlight for rs-fMRI Data Integration
**Source:** arXiv:2412.10161
**Utility:** 0.95
**Created:** 2026-03-25
## Activation Keywords
- fusion searchlight
- FuSL
- rs-fMRI data integration
- multi-metric brain state classification
- resting-state fMRI fusion
- pharmacological prediction
## Description
A framework that integrates complementary information from multiple resting-state fMRI metrics using searchlight-based analysis, enhancing brain state classification accuracy with explainable AI for metric contribution analysis.
## Core Methodology
### 1. Problem: Independent Metric Analysis
**Issue:** Various rs-fMRI metrics are typically analyzed independently:
- Local brain connectivity
- Global brain connectivity
- Low-frequency amplitude fluctuations (ALFF/fALFF)
**Limitation:** Overlooks interrelations between metrics, limiting analytical sensitivity.
### 2. Fusion Searchlight (FuSL) Framework
**Approach:** Integrate multiple metrics within local searchlight regions
**Key Features:**
- Combines complementary information
- Preserves spatial specificity
- Enables explainable metric contributions
### 3. Classification Enhancement
**Application:** Pharmacological treatment prediction
- Enhanced accuracy vs. single-metric approaches
- Identified additional brain regions affected by alprazolam sedation
### 4. Explainable AI Integration
**Purpose:** Delineate differential contributions of each metric
**Benefits:**
- Improves spatial specificity
- Provides interpretable results
- Shows which metrics drive predictions
## Implementation Framework
```python
# Conceptual pipeline
class FusionSearchlight:
def __init__(self, metrics, radius=10):
self.metrics = metrics # List of rs-fMRI metrics
self.radius = radius
def extract_features(self, fmri_data, center_voxel):
"""Extract multi-metric features within searchlight sphere"""
sphere_mask = create_sphere(center_voxel, self.radius)
features = []
for metric in self.metrics:
# Compute metric within sphere
metric_values = compute_metric(fmri_data, sphere_mask, metric)
features.append(metric_values)
return np.concatenate(features)
def fit(self, fmri_data, labels):
"""Train classifier with fused features"""
# Extract fused features at each voxel
for voxel in all_voxels:
features = self.extract_features(fmri_data, voxel)
predictions[voxel] = self.classifier.fit(features, labels)
return self
def explain(self, voxel):
"""Explain metric contributions for predictions"""
return self.explainer.attribute(voxel)
```
## Applications
1. **Pharmacological Prediction**
- Drug effect detection
- Sedation state classification
- Treatment response prediction
2. **Clinical Brain State Classification**
- Disease detection
- Biomarker identification
- Individual differences
3. **Multi-modal Integration**
- Combine fMRI with other modalities
- Cross-condition fusion
## When to Use
- Analyzing resting-state fMRI with multiple metrics
- Need to integrate complementary information
- Brain state classification tasks
- When interpretability of metric contributions matters
- Multi-modal or multi-condition fusion
## Key Benefits
- **Enhanced sensitivity** - Leverages interrelations between metrics
- **Spatial specificity** - Searchlight preserves local information
- **Interpretability** - Explainable AI reveals metric contributions
- **Versatility** - Adaptable to other modalities and conditions
## Tools Used
- `read` - Read documentation and references
- `web_search` - Search for related information
- `web_fetch` - Fetch paper or documentation
## Instructions for Agents
Follow these steps when applying this skill:
### Step 1: Pharmacological Prediction
### Step 2: Clinical Brain State Classification
### Step 3: Multi-modal Integration
### Step 4: Understand the Request
### Step 5: Search for Information
### When to Apply
- Analyzing resting-state fMRI with multiple metrics
- Need to integrate complementary information
- Brain state classification tasks
## Examples
### Example 1: Basic Application
**User:** I need to apply Fusion Searchlight for rs-fMRI Data Integration to my analysis.
**Agent:** I'll help you apply fusion-searchlight-fmri. First, let me understand your specific use case...
**Context:** Problem: Independent Metric Analysis
### Example 2: Advanced Scenario
**User:** Analyzing resting-state fMRI with multiple metrics
**Agent:** Based on the methodology, I'll guide you through the advanced application...
### Example 2: Advanced Application
**User:** What are the key considerations for fusion-searchlight-fmri?
**Agent:** Let me search for the latest research and best practices...
## Related Skills
- `multimodal-brain-connectivity-gnn` - Multi-modal fusion with GNN
- `functional-connectome-fingerprint` - Individual connectivity patterns
- `lightweight-dynamic-brain-connectivity` - Dynamic connectivity analysis
## References
- Wein, S., et al. "Data Integration with Fusion Searchlight: Classifying Brain States from Resting-state fMRI." arXiv:2412.10161 (2024)
- Searchlight analysis methodology
- Explainable AI for neuroimaging