Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI

Magnetic resonance imaging (MRI) is a valuable non-invasive tool for probing both the architecture and the activity of the brain in-vivo. The problem however is that the measurements obtained with MRI are highly-indirect, spatio-temporally uncertain and very sensitive to many noise sources. In order to extract structural, functional and dynamic connectivity despite this complexity, many neuroimaging workflows have been developed. It has however been shown by several researchers that variability in the methodology can be the cause of variability in the results (Botvinik-Nezer et al. 2020, Bowring et al. 2019, Maier-Hein et al. 2017, Thomas et al. 2014, Magnotta et al. 2012). In my PhD, I will therefore work on equipping the researchers with a framework for the extraction of reliable and precise structural and functional connectomes that permit their joint modeling and analysis with interpretable and reproducible methods. I will not only contribute to the development of easy-to-use toolboxes that automatize the preprocessing, but I will also quantitively evaluate different processing pipelines. The goal is to determine the optimal acquisition and processing for each modality across four scanners. The optimality of the pipeline will be determined by the one that reaches the highest sensitivity and sensibility. To reach that goal, I will perform tests on one densely-acquired patient to not only remove inter-subject variability and focus on the methodological variability but also have a wealth of data that enables the definition of gold standards in the validation of the workflows. Once the optimal pipelines have been found on the first patient, I will test whether gains in connectomes’ reliability are reproduced when applied on data from ten new subjects. On a third step, I will apply the methodology developed to understand the regulatory role of the thalamus on switches between task-oriented and internal functional networks. In particular, I will focus on Schizophrenia and Bipolar disorder because the disruption in the thalamo-cortical connectivity is thought to be a key biomarker of those diseases’ progression. Additionally, to the methodology, the project will publicly release two highly valuable datasets necessary in the improvement of workflow for structural and functional network extraction.