Identification of time-varying multivariate physiological systems and applications to cerebrovascular regulatory mechanisms and dynamic brain functional connectivity from multimodal measurements

通过多模态测量识别时变多元生理系统及其在脑血管调节机制和动态脑功能连接中的应用

• 批准号: RGPIN-2019-06638
• 负责人: Mitsis, Georgios
• 金额: $ 2.04万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682434
• 关键词: Identification; time; varying; multivariate; physiological

The proposed research aims to develop state-of-the art methods for the analysis of time-varying multivariate systems, which are particularly well-suited for physiological systems and functional neuroimaging. While the envisioned methodological contributions are of general applicability, we will apply them to modeling systemic and regional cerebral hemodynamics as well as time-varying brain functional connectivity. The specific objectives are to: (i) develop a methodological framework for modeling time-varying multivariate systems that may exhibit nonlinear behavior. Within this objective, we will investigate the combination of recursive and linear parameter-varying estimation to improve performance, as well as genetic algorithm/regularization methods to select model order, particularly in the case of nonlinear systems. (ii) obtain improved quantitative descriptions of the regional fMRI HRF, by collecting and analyzing multimodal neuroimaging data (simultaneous EEG-fMRI and TCD-fNIRS data). Within this objective, we will also generate realistic biophysical simulation data to link the empirical HRFs obtained by neural activation and different EEG signal features, aiming to obtain better understanding of the underlying neurovascular coupling mechanisms (iii) investigate time-varying hemodynamics in vasovagal syncope patients and design algorithms to predict the occurrence of fainting episodes that, in the longer term, will be applied to data collected from wearable devices. (iv) quantify time-varying functional brain connectivity from multimodal neuroimaging measurements. Within this objective, we aim to quantify the contribution of neural vs. physiological factors in the emergence of time-varying resting-state fMRI functional connectivity by using simultaneous EEG-fMRI data. We also aim to pursue the use of time-varying connectivity as a marker for noninvasive brain stimulation (e.g. transcranial direct current stimulation) to promote motor learning using EEG/MEG data, as well as to design more accurate detection/prediction algorithms for epileptic seizures using long-duration EEG data. The proposed research yields great promise for real-time monitoring applications using (among others) wearable devices, which have the potential to transform modern medicine. It also yields promise for obtaining more sensitive biomarkers based on time-varying functional connectivity, as well as using the latter for identifying therapeutic targets for noninvasive protocols in a closed-loop context (biofeedback).

这项拟议的研究旨在开发最先进的方法来分析时变的多变量系统，这些方法特别适合于生理系统和功能神经成像。虽然设想的方法论贡献具有普遍适用性，但我们将把它们应用于建模全身和局部脑血流动力学以及时变的脑功能连通性。具体目标是：(I)开发一个方法框架，用于对可能表现出非线性行为的时变多变量系统进行建模。在这一目标中，我们将研究递归和线性变参数估计的组合以提高性能，以及遗传算法/正则化方法来选择模型阶数，特别是在非线性系统的情况下。(2)通过收集和分析多模式神经成像数据(同时EEG-fMRI和TCD-fNIRS数据)，改进对区域fMRI HRF的定量描述。在这一目标下，我们还将生成真实的生物物理模拟数据，将神经激活获得的经验高频与不同的脑电信号特征联系起来，旨在更好地了解潜在的神经血管耦合机制(Iii)研究血管迷走性晕厥患者的时变血流动力学，并设计算法来预测晕厥发作的发生，从长远来看，这些算法将应用于从可穿戴设备收集的数据。(Iv)从多模式神经成像测量中量化随时间变化的脑功能连接性。在这一目标中，我们的目标是通过使用同步的EEG-fMRI数据来量化神经因素与生理因素在时变静息状态fMRI功能连接的出现中的作用。我们还致力于利用时变连接作为非侵入性脑刺激(如经颅直流电刺激)的标志，以利用EEG/MEG数据促进运动学习，并利用长持续时间的EEG数据设计更准确的癫痫发作检测/预测算法。这项拟议的研究为使用(除其他外)可穿戴设备的实时监测应用带来了巨大的希望，这些设备具有改变现代医学的潜力。它还产生了基于时变功能连接获得更敏感的生物标记物的前景，以及使用后者在闭环系统(生物反馈)中识别非侵入性方案的治疗靶点。