| 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
- Data Preparation: Prepare your neural data according to the paper specifications
- Model Setup: Configure the appropriate architecture for your use case
- Training/Inference: Follow the paper's methodology for optimal results
- 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
- PDF: Download
- 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
