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Visualizing the Developing Brain in Living Zebrafish using Brainbow and Time-lapse Confocal Imaging

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Structure and function in artificial, zebrafish and human neural networks.

Peng Ji¹, Yufan Wang², Thomas Peron³

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai 200433, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.

Physics of Life Reviews

|May 14, 2023

Summary

This summary is machine-generated.

Network science and artificial intelligence offer powerful tools to link brain structure and function. This review explores their application in understanding human and zebrafish brain dynamics and inferring neural connections.

Keywords:

Brain networks Network reconstruction Network science Zebrafish brain

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Area of Science:

Neuroscience
Network Science
Artificial Intelligence

Background:

Understanding the relationship between a system's structure and its dynamics is a fundamental challenge.
In neuroscience, low spatio-temporal resolution and lack of universal inference frameworks hinder progress in studying structure-dynamics interplay.
Network science and artificial intelligence offer promising approaches to analyze complex neural systems.

Purpose of the Study:

To review recent applications of network science and artificial intelligence in understanding the interplay between brain structure and functional dynamics.
To bridge the gap between structural network properties and observed neural dynamics.
To provide insights for neuroscientists and network researchers on novel application scenarios.

Main Methods:

Characterization of brain networks using topological features.
Inference of functional connections from neural data.
Dynamical modeling of brain activity.
Application of these methods to human and zebrafish brain data.

Main Results:

Network science and AI methods enable prediction of dynamical states from network structure and vice versa.
These approaches facilitate the discovery of network connections and causal relations from experimental recordings.
Successful applications in both human and zebrafish brains demonstrate the versatility of these methods.

Conclusions:

Network science and AI are crucial for advancing the study of brain structure-function relationships.
These integrated approaches offer powerful tools for analyzing complex neural dynamics and inferring connectivity.
The review highlights the potential for novel discoveries in neuroscience through interdisciplinary collaboration.