Creating subject-specific mathematical models to understand the brain

创建特定于主题的数学模型来理解大脑

• 批准号: DP200102600
• 负责人: Prof David Grayden
• 金额: $ 29.49万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-02-28 至 2023-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200102600
• 关键词: Creating; subject; specific; mathematical; models

This project aims to develop a mathematical framework that bridges the different scales of brain activities to provide a new tool for understanding the brain. Methods will be developed that unify individual neural activity with large scale brain activity. The approach will be validated by comparing predictions of interconnected models of neural populations (called mean-field models) to experimental data. The creation of subject-specific models from data is important, as there is large variability in neural circuits between individuals despite seemingly similar network activity. The intended outcome is new insights into the processes that govern brain function and methods for improving functional imaging of, and interfacing to, the brain.

该项目旨在开发一个数学框架，将大脑活动的不同尺度联系起来，为理解大脑提供新的工具。将开发将个体神经活动与大规模大脑活动统一起来的方法。该方法将通过将神经种群的互连模型（称为平均场模型）的预测与实验数据进行比较来验证。从数据中创建特定于受试者的模型非常重要，因为尽管网络活动看似相似，但个体之间的神经回路存在很大的差异。预期的结果是对控制大脑功能的过程和改善大脑功能成像和与大脑接口的方法的新见解。