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
• 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