Advanced signal processing and systems identification for investigating the brain’s resting-state

用于研究大脑静息状态的先进信号处理和系统识别

• 批准号: RGPIN-2014-05931
• 负责人: Mitsis, Georgios
• 金额: $ 2.26万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637019
• 关键词: Advanced; signal; processing; systems; identification

The proposed research aims to develop state-of-the art methods for the analysis of physiological signals and systems, focusing on methods that are data-driven, i.e. they do not rely on prior assumptions about system structure, and are able to quantify dynamic nonlinearities and/or time-varying behavior. While the envisioned methodological contributions are of general applicability, we will specifically consider customizing them for application to cerebral hemodynamics and brain resting-state network data analysis. We will mostly concentrate on modeling approaches that employ the concept of basis expansions, as such approaches significantly reduce the required number of free parameters (especially for nonlinear systems) and that they have proven to be successful in bioengineering-related application. Specifically they have been widely used for modeling biological systems, including those studied in the rapidly developing field of functional neuroimaging. The main aims of the proposed research program are: (i) to optimize basis expansions techniques for modeling nonlinear and/or nonstationary systems by combining principles from machine learning and more traditional systems identification, such as Bayesian estimation, neural network architectures and recursive estimation schemes respectively. In the longer term we aim to provide a general framework that will link nonlinear systems identification with statistical/ machine learning techniques. (ii) to apply time-varying techniques to investigate the effect of nonstationarities on cerebral hemodynamics and human brain resting-state network analyses. To this end we will use already existing data from multiple modalities (fMRI, simultaneous EEG-fMRI, MEG) as well as collect long duration data at the Brain Imaging Center of the Montreal Neurological Institute, including simultaneous EEG-fMRI as well as simultaneous EEG-MEG. (iii) to investigate the characteristics of resting-state networks over multiple scales by using the aforementioned multimodal data, which exploit the advantages of each modality (space resolution of fMRI with time resolution of EEG and MEG), as well as simulations from biophysical (forward models) (iv) integrate measures of hemodynamics and autoregulation provided by transcranial Doppler ultrasound with functional neuroimaging (particularly fMRI) by using biophysical and data-driven models as well as by collecting relevant physiological signals (pressure, pulse wave velocity, cerebral blood flow velocity) in the MR scanner. This is anticipated to enhance the interpretation of both aspects of cerebral hemodynamics (systemic as yielded by Doppler ultrasound and regional as yielded by fMRI) and to elucidate the functional role of resting-state network activity in a clearer manner, by further disentangling it into autonomic and cognitive factors. The impact of the proposed research is expected to be significant as it will provide better understanding of cerebral hemodynamics and resting-state brain functional networks, with both exhibiting significant potential as biomarkers in clinical studies.

拟议的研究旨在开发用于分析生理信号和系统的最先进方法，重点是数据驱动的方法，即它们不依赖于有关系统结构的先验假设，并且能够量化动态非线性和/或时变行为。虽然设想的方法的贡献是普遍适用的，我们将专门考虑定制它们的应用程序，脑血流动力学和大脑静息状态网络数据分析。我们将主要集中在采用基展开概念的建模方法上，因为这种方法显着减少了所需的自由参数（特别是对于非线性系统），并且它们已被证明在生物工程相关应用中是成功的。具体而言，它们已被广泛用于生物系统建模，包括在快速发展的功能性神经成像领域中研究的生物系统。拟议的研究计划的主要目标是：（i）通过结合机器学习和更传统的系统识别原理，如贝叶斯估计，神经网络架构和递归估计方案，优化用于建模非线性和/或非平稳系统的基扩展技术。从长远来看，我们的目标是提供一个通用框架，将非线性系统识别与统计/机器学习技术联系起来。(ii)应用时变技术研究非平稳性对脑血流动力学和人脑静息状态网络分析的影响。为此，我们将使用来自多种模式（fMRI，同步EEG-fMRI，MEG）的现有数据，并在蒙特利尔神经学研究所的脑成像中心收集长期数据，包括同步EEG-fMRI以及同步EEG-MEG。(iii)通过使用上述多模态数据（利用每种模态的优势）来研究多个尺度上的静止状态网络的特征（fMRI的空间分辨率与EEG和MEG的时间分辨率），以及生物物理学的模拟（前向模型）（iv）将经颅多普勒超声提供的血流动力学和自动调节措施与功能性神经成像相结合通过使用生物物理和数据驱动的模型以及通过在MR扫描仪中收集相关的生理信号（压力、脉搏波速度、脑血流速度），来实现功能磁共振成像（特别是功能磁共振成像）。预计这将增强对脑血流动力学两个方面的解释（多普勒超声产生的系统性和功能磁共振成像产生的区域性），并通过进一步将其分解为自主和认知因素，以更清晰的方式阐明静息状态网络活动的功能作用。预计拟议研究的影响将是显著的，因为它将提供对脑血流动力学和静息态脑功能网络的更好理解，两者都表现出作为临床研究中生物标志物的显著潜力。