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

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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.