MaTiLDA:一个集成的机器学习和拓数据分析平台,用于大脑网络动态
Katrina Prantzalos1, Dipak Upadhyaya, Nassim Shafiabadi
1Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA, Katrina.prantzalos@case.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 31, 2023
概括
MaTiLDA简化了使用拓数据分析 (TDA) 和机器学习 (ML) 来分析神经疾病中的大脑相互作用. 这个平台使复杂的计算神经科学可用于研究.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 在等神经系统疾病中研究复杂的大脑相互作用往往需要先进的计算和数学专业知识.
- 拓数据分析 (TDA) 和机器学习 (ML) 为理解这些交互提供了强大的工具,但对许多研究人员来说,这是一个很大的进入障碍.
研究的目的:
- 推出MaTiLDA,一个集成的网络平台,旨在降低使用TDA和ML分析神经生理数据的可访问性门.
- 为了使临床和计算神经科学家能够直观地应用TDA方法和ML模型来描述大脑相互作用模式.
主要方法:
- 开发MaTiLDA,一个用户友好的网络平台,将TDA方法 (例如,持久同源) 与ML模型集成在一起.
- 应用MaTiLDA来分析患者的高分辨率内脑电图 (EEG) 数据.
主要成果:
- 通过使用TDA和ML,MaTiLDA成功地使复杂的大脑相互作用模式的直观表征成为可能.
- 该平台使用内脑电图数据,促进了对耐性患者的发作传播阶段的分析.
结论:
- MaTiLDA显著减少了在神经科学研究中应用先进的TDA和ML技术所需的技术专业知识.
- 该平台为神经系统疾病提供了宝贵的见解,以其在描述发作动态的应用为例.
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