PhD proposal : Brain development analysis using MRI and physical modeling
Lab: LaTIM, INSERM, IMT Atlantique, Brest France
Starting date : Fall 2017
Funding : FRM (Fédération pour la recherche médicale)
With a prevalence of 1/2,000 to 1/4,000 live births, perinatal ischemic stroke is the most frequent form of childhood stroke and constitutes the leading cause of unilateral cerebral palsy in term-born children. Perinatal ischemic stroke is an umbrella term including several conditions that differ in pathophysiology, timing and thus in outcomes. Neonatal arterial ischemic stroke (NAIS) refers to a perinatal ischemic stroke syndrome with neonatal signs (mainly iterative focal seizures in the first days of life) related to an arterial infarct as revealed by brain imaging.
Every case of NAIS is unique to the individual. Considering for instance NAIS leading to unilateral cerebral palsy, one person may have total paralysis and require constant care, while another with partial paralysis might have slight movement tremors but require little assistance. This is due in part to the type of injury and the timing of the injury to the developing brain. The prediction of long-term motor outcome (and the associated treatment or therapy) requires new personalized approaches, i.e. patient-specific techniques to understand the causes of the observed disabilities.
This PhD thesis will focus on finding the causes of abnormal brain development patterns from synergies between MRI and physical modeling. MRI data are usually used as in vivo observations only to estimate correlations between cognitive or motor score with quantitative image-based measurements. However, correlation is not causality, meaning that MRI data analysis cannot provide explanations alone. The objective is to work on the determination and understanding of the critical brain developmental stages and of the causes of abnormal developments from the joint use of advanced physical models and accurate in vivo observations.
Major advances have been obtained from the so-called “detection-attribution” approach for the determination of the causal links between physical processes and the observation of extreme events. The underlying statement of the detection-attribution principle is as follows: can we modify locally the parameters of the physical model in such a way that it will simulate the morphological changes induced by NAIS ? More specifically, we plan to use exemplar-driven filtering methods (also known as data assimilation schemes) by running the physical model backward in time using the observations occurring at the end of the sequence.
Candidates are invited to email (to François Rousseau, Mickaël Dinomais and Julien Lefèvre) a motivation letter and CV detailing in full your academic background, including all modules taken and grades assigned.
(c) GdR 720 ISIS - CNRS - 2011-2015.