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

这项拟议的研究旨在开发用于分析生理信号和系统的最先进的方法，侧重于数据驱动的方法，即它们不依赖于关于系统结构的先前假设，并且能够量化动态非线性和/或时变行为。虽然设想的方法学贡献具有普遍适用性，但我们将特别考虑定制它们，以应用于脑血流动力学和脑静息状态网络数据分析。我们将主要集中于使用基扩展概念的建模方法，因为这种方法显著减少了所需的自由参数的数量(特别是对于非线性系统)，并且它们已被证明在与生物工程相关的应用中是成功的。具体地说，它们已被广泛用于生物系统的建模，包括那些在快速发展的功能神经成像领域中研究的系统。该研究计划的主要目的是：(1)将机器学习的原理与更传统的系统辨识方法(如贝叶斯估计、神经网络结构和递归估计)相结合，优化用于非线性和/或非平稳系统建模的基扩展技术。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，我们将使用已有的多种模式(fMRI、同步EEG-fMRI、脑磁图)的数据，并在蒙特利尔神经研究所脑成像中心收集长持续时间的数据，包括同步EEG-fMRI以及同步EEG-MEG。(Iii)利用上述多模式数据，利用每种模式(fMRI的空间分辨率与脑电和脑磁图的时间分辨率)以及生物物理模拟(正演模型)的优势，研究多尺度上静息状态网络的特征；(Iv)利用生物物理和数据驱动模型，将经颅多普勒超声提供的血流动力学和自动调节测量与功能神经成像(尤其是fMRI)相结合，并在MR扫描仪中收集相关生理信号(压力、脉搏波速度、脑血流速度)。这有望加强对脑血流动力学的两个方面的解释(多普勒超声产生的全身性和功能磁共振产生的局部)，并通过进一步将其与自主神经和认知因素分开，以更清晰的方式阐明静息状态网络活动的功能作用。这项拟议研究的影响预计将是重大的，因为它将提供对脑血流动力学和静息状态脑功能网络的更好了解，这两者在临床研究中都显示出作为生物标志物的巨大潜力。

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.