Energy landscape analysis based on the Ising model: Tutorial review

Area of Science:

• Computational Neuroscience
• Data Analysis
• Statistical Physics

Background:

• Energy landscape analysis methods are crucial for understanding complex system dynamics.
• The Ising model provides a framework for representing interactions within systems.
• Multivariate time series data capture dynamic processes across various scientific domains.

Purpose of the Study:

• To provide a comprehensive tutorial on energy landscape analysis using the Ising model.
• To explain the underlying concepts, terminology, and validation procedures.
• To highlight emerging applications beyond functional magnetic resonance imaging (fMRI) and neuroscience.

Main Methods:

• Utilizes the Ising model to estimate energy landscapes from multivariate time series data.
• Models data dynamics as trajectories moving between basins on the landscape.
• Reviews computational steps, theoretical underpinnings, and validation techniques.

Main Results:

• The Ising model-based energy landscape analysis offers a robust method for capturing system dynamics.
• The approach facilitates understanding transitions between different states or 'basins'.
• Demonstrates applicability to diverse datasets, including but not limited to fMRI data.

Conclusions:

• This review serves as a guide for researchers applying energy landscape analysis to new data types.
• The method's versatility supports its adoption in fields beyond neuroscience.
• Understanding energy landscapes is key to interpreting complex temporal data patterns.