higher-order-brain-networks

---
name: higher-order-brain-networks
description: "Higher-order brain network analysis using topological signal processing. Captures circulatory and multi-node interactions beyond pairwise graph models.. Activation: higher-order networks, topological signal processing, brain connectomics."
version: 1.0.0
author: Research Synthesis
license: MIT
metadata:
  hermes:
    tags: ["higher-order networks", "topological signal processing", "brain connectomics", "simplicial complexes", "multimodal analysis"]
    source_paper: "Multimodal Higher-Order Brain Networks: A Topological Signal Processing Perspective (arXiv:2604.29903v1)"
    published: "2026-03-31"
    category: "neuroscience"
---

# Higher-Order Brain Network Analysis

## Overview
Higher-order brain network analysis using topological signal processing. Captures circulatory and multi-node interactions beyond pairwise graph models.

This skill is based on the research paper "Multimodal Higher-Order Brain Networks: A Topological Signal Processing Perspective" published on arXiv (2604.29903v1).

## Activation Keywords
- higher-order networks
- topological signal processing
- brain connectomics
- simplicial complexes
- multimodal analysis

## Core Concepts
- Higher-order interactions (beyond pairwise)
- Simplicial complexes for brain networks
- Topological signal processing
- Circulatory flow patterns
- Multi-node functional coupling

## Applications
- Brain connectomics
- Higher-order connectivity analysis
- Neuroimaging data processing
- Network neuroscience

## Implementation Guidelines

### When to Use This Skill
- Research involving higher-order networks
- Projects related to topological signal processing
- Analysis requiring brain connectomics

### Key Methodologies
1. **Data Preparation**: Prepare your neural data according to the paper specifications
2. **Model Setup**: Configure the appropriate architecture for your use case
3. **Training/Inference**: Follow the paper's methodology for optimal results
4. **Evaluation**: Use relevant metrics to assess performance

### Tools Typically Used
- **Python**: NumPy, SciPy for numerical computations
- **Neuroimaging**: MNE, Nilearn, Brain Connectivity Toolbox
- **Machine Learning**: PyTorch, TensorFlow for model implementation
- **Visualization**: Matplotlib, Seaborn, Plotly for results

## References

### Source Paper
- **Title**: Multimodal Higher-Order Brain Networks: A Topological Signal Processing Perspective
- **arXiv**: [2604.29903v1](https://arxiv.org/abs/2604.29903v1)
- **PDF**: [Download](https://arxiv.org/pdf/2604.29903v1)
- **Published**: 2026-03-31

### Related Skills
- Other neuroscience research skills in the collection
- Brain connectivity analysis tools
- Neural dynamics modeling frameworks

## Notes

This skill was automatically generated from arXiv research as part of the neuroscience literature review workflow. For the most up-to-date information, refer to the original paper.

_Last updated: 2026-03-31_