sbind-spatiotemporal-brain-imaging-neural-dynamics

name: sbind-spatiotemporal-brain-imaging-neural-dynamics description: Source: arXiv:2509.18507 (ICML 2025)

SBIND: Spatiotemporal Brain Imaging Neural Dynamics

Source: arXiv:2509.18507 (ICML 2025) Utility: 0.94 Created: 2026-03-25

Activation Keywords

• SBIND
• spatiotemporal neural imaging
• behaviorally relevant dynamics
• widefield calcium imaging
• functional ultrasound imaging
• neural-behavioral prediction
• disentangle neural dynamics

Description

A deep learning framework for modeling spatiotemporal dependencies in neural imaging data and disentangling behaviorally relevant dynamics from irrelevant neural activity.

Core Methodology

1. Problem: High-Dimensional Neural Imaging

Challenges:

• High dimensionality of imaging data
• Complex spatiotemporal dependencies
• Behaviorally irrelevant dynamics obscure signal
• Existing preprocessing discards relevant information

Modalities:

• Widefield calcium imaging
• Functional ultrasound imaging (fUS)

2. SBIND Framework

Key Components:

1. Spatiotemporal Modeling

   • Captures local and long-range spatial dependencies
   • Models temporal dynamics across brain regions

2. Behavioral Disentanglement

   • Separates behaviorally relevant dynamics
   • Filters out behaviorally irrelevant activity

3. End-to-End Learning

   • No separate preprocessing/dimensionality reduction
   • Preserves behaviorally relevant information

3. Architecture

# Conceptual SBIND architecture
class SBIND(nn.Module):
    """
    Spatiotemporal Brain Imaging Neural Dynamics model
    
    Key features:
    - Spatial attention for long-range dependencies
    - Temporal convolution for dynamics
    - Behavioral disentanglement module
    """
    
    def __init__(self, spatial_dim, temporal_dim, behavioral_dim):
        super().__init__()
        
        # Spatial encoder with attention
        self.spatial_encoder = SpatialAttentionEncoder(spatial_dim)
        
        # Temporal dynamics module
        self.temporal_module = TemporalConvNet(temporal_dim)
        
        # Behavioral disentanglement
        self.disentangle = BehavioralDisentanglement(behavioral_dim)
        
        # Prediction head
        self.predictor = BehavioralPredictor()
    
    def forward(self, neural_images):
        """
        Args:
            neural_images: [B, T, H, W] spatiotemporal imaging data
        Returns:
            behavioral_prediction: [B, T, behavioral_dim]
            relevant_dynamics: disentangled behaviorally relevant activity
        """
        # Extract spatiotemporal features
        features = self.spatial_encoder(neural_images)
        dynamics = self.temporal_module(features)
        
        # Disentangle behavioral relevance
        relevant, irrelevant = self.disentangle(dynamics)
        
        # Predict behavior
        prediction = self.predictor(relevant)
        
        return prediction, relevant

Applications

1. Neural-Behavioral Prediction

• Predict behavior from neural activity
• Outperforms existing models
• Works across imaging modalities

2. Functional Ultrasound Imaging

• First dynamical model for fUS
• Extends naturally to new modalities
• No modality-specific engineering

3. Mechanism Investigation

• Identify behaviorally relevant brain regions
• Discover spatiotemporal patterns
• Understand neural encoding of behavior

Key Results

Implementation

# Install SBIND
pip install sbind

# Or from source
git clone https://github.com/ShanechiLab/SBIND/
cd SBIND
pip install -e .

When to Use

• Analyzing widefield calcium imaging data
• Working with functional ultrasound imaging
• Need to disentangle behavioral relevance
• High-dimensional neural imaging analysis
• Neural-behavioral prediction tasks

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: Spatiotemporal Modeling

Step 2: Behavioral Disentanglement

Step 3: End-to-End Learning

Step 4: Understand the Request

Step 5: Search for Information

When to Apply

• Analyzing widefield calcium imaging data
• Working with functional ultrasound imaging
• Need to disentangle behavioral relevance

Examples

Example 1: Basic Application

User: I need to apply SBIND: Spatiotemporal Brain Imaging Neural Dynamics to my analysis.

Agent: I'll help you apply sbind-spatiotemporal-imaging. First, let me understand your specific use case...

Context: Problem: High-Dimensional Neural Imaging

Example 2: Advanced Scenario

User: Analyzing widefield calcium imaging data

Agent: Based on the methodology, I'll guide you through the advanced application...

Example 2: Advanced Application

User: What are the key considerations for sbind-spatiotemporal-imaging?

Agent: Let me search for the latest research and best practices...

Related Skills

• eeg-brain-connectivity-bci - EEG analysis
• time-varying-brain-connectivity - Dynamic connectivity
• dnn-neural-decoding - Neural decoding

References

• Hosseini, S.M., et al. "Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data." ICML 2025.
• Code: https://github.com/ShanechiLab/SBIND/