Data Analysis and Statistical Modelling of Dynamical Processes in Neurology GEM mit TI 315/4-1

使用 TI 315/4-1 进行神经病学 GEM 动态过程的数据分析和统计建模

• 批准号: 5444126
• 负责人: Professor Dr. Andreas Hetzel
• 金额: --
• 依托单位: Abteilung Dynamische Prozesse in den Lebenswissenschaften
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2005
• 资助国家: 德国
• 起止时间: 2004-12-31 至 2010-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5444126?language=en
• 关键词: Data; Analysis; Statistical; Modelling; Dynamical

Numerous clinical phenomena in neurology are of dynamic oscillatory nature and are caused by interacting sub-systems. The analysis of multivariate time series of the dynamics of the subsystem allows for an understanding of the underlying processes in the healthy and the diseased state. The aim of this project is to develop and apply mathematical methods in order to infer from time series the dynamical properties of the subsystem and their interactions. There are different modes of interaction, ranging from signal propagation via resonance to synchronization. Methods will be developed to discriminate between these different types of relationship for bi- and multivariate time series. In close collaborations with clinicians, the methods will be tested for their appropriateness and utilized in basic and clinical research in three neurologic fields, i.e. generating mechanisms of pathological tremors, prediction of epileptic seizures, and diagnosis of cerebrovascular diseases. The development of the mathematical methods will be driven by the challenges in data analysis posed by the clinical applications. On the other land, the application of the developed methods are expected to enhance clinical understanding, provide diagnostic and prognostic factors and eventually guide treatment strategies.

神经病学中的许多临床现象具有动态振荡性质，并且由相互作用的子系统引起。多变量时间序列的子系统的动态分析允许在健康和患病状态的基本过程的理解。该项目的目的是发展和应用数学方法，以便从时间序列中推断出子系统及其相互作用的动态特性。有不同的相互作用模式，从通过共振的信号传播到同步。将开发的方法来区分这些不同类型的双和多变量时间序列的关系。在与临床医生的密切合作中，将测试这些方法的适当性，并将其用于三个神经学领域的基础和临床研究，即病理性震颤的产生机制，癫痫发作的预测和脑血管疾病的诊断。数学方法的发展将受到临床应用所带来的数据分析挑战的推动。在另一方面，所开发的方法的应用有望提高临床理解，提供诊断和预后因素，并最终指导治疗策略。