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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739019
• 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).

拟议的研究旨在开发用于分析时变多变量系统的最先进方法，这些方法特别适用于生理系统和功能神经成像。虽然设想的方法的贡献是普遍适用的，我们将把它们应用到建模系统和区域脑血流动力学以及随时间变化的脑功能连接。具体目标是：（i）开发一个建模时变多变量系统，可能表现出非线性行为的方法框架。在这个目标中，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。(ii)通过收集和分析多模态神经成像数据（同步EEG-fMRI和TCD-fNIRS数据），获得区域fMRI HRF的改进定量描述。在此目标下，我们还将生成逼真的生物物理模拟数据，以将通过神经激活获得的经验HRF与不同的EEG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，并设计算法来预测昏厥发作的发生，从长远来看，将应用于从可穿戴设备收集的数据。(iv)从多模态神经成像测量中量化随时间变化的功能性脑连接。在此目标下，我们的目标是量化的贡献，神经与生理因素的出现时变静息态功能磁共振成像功能连接使用同步脑电图功能磁共振成像数据。我们的目标还在于追求使用时变连接作为无创脑刺激（例如经颅直流电刺激）的标记，以促进使用EEG/MEG数据的运动学习，以及使用长时间EEG数据设计更准确的癫痫发作检测/预测算法。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，这些设备有可能改变现代医学。它还有望获得基于时变功能连接的更敏感的生物标志物，以及使用后者在闭环环境（生物反馈）中识别非侵入性方案的治疗靶点。