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

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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).