Multiphysics image-based AnalysIs for premature brAin development understanding
Description: Each year in France, 55 000 children are born prematurely, i.e., before the 37th week of gestation. In this context, extreme prematurity (less than 32 weeks) covers 10 000 newborns a year. Long-term studies of the outcome of prematurely born infants have clearly documented that the majority of such infants may have significant motor, cognitive, and behavioral deficits. However, there is a limited understanding of the nature of the cerebral abnormality underlying these adverse neurologic outcomes. The French EPIPAGE study shows that nearly a third of these former preterm infants still require specific cares at 5 years-old. Researchers conclude that “prevention of the learning disabilities associated with cognitive deficiencies [...] is an important goal for modern perinatal care for children who are born very preterm and for their families”. A first answer to this challenge was proposed by 3D morphological imaging, namely Magnetic Resonance Imaging (MRI). It led to recent breakthroughs, related to the potential correlations between specific tissue lesions, or volumetric variations of given cerebral structures, on the one hand, and some disabilities, mainly motor, on the other hand. Nevertheless, the understanding of the intrinsic mechanisms resulting in cognitive disabilities remains limited without having access to functional information. In this context, the emergence of new modalities of 3D functional MRI (e.g., Arterial Spin Labeling, ASL), or optical imaging technologies (e.g., Near InfraRed Spectroscopy, NIRS), bring new perspectives for extracting cognitive information, via metabolic activity measures. Other classical technics devoted to cerebral signal measurement, such as ElectroEncephaloGraphy (EEG), provide cognitive information at the cortical level. Each of these various non-invasive imaging technologies brings substantial and specific information for the understanding of newborn brain development. However, the induced data of preterm brain are voluminous, noisy and highly heterogeneous, in terms of nature (signal, image), dimensions (1D, 2D, 3D, time) and semantics (morphological, physiological, functional). As a consequence, establishing correspondences between these data and cross-analysing their underlying information remains so far a challenging task. In order to tackle these challenges, this project aims at developing innovative approaches for multi-image / multi-signal analysis, in order to improve neurodevelopment understanding methods. To reach such goal – that requires to handle fundamental, methodological and technological issues – both pluri- / interdisciplinary and mixed academic / industrial interactions are mandatory. From a fundamental point of view, mathematics and computer science have to be considered in association with imaging physics and medicine, to deal with open issues of signal and image analysis from heterogeneous data (image, signal), considered in the multiphysics contexts related to data acquisition (magnetic, optic, electric signals) and biophysics modeling of the newborn brain. A sustained synergy between all these scientific domains is then necessary. Finally, the sine qua non condition to reach a better understanding of the coupled morphological-cognitive development of premature newborns, is the development of effective software tools, and their distribution to the whole medical community. The very target of this project will be the design of such software tools for medical image / signal analysis, actually operational in clinical routine, and freely available. Academic researchers and industrial partners will work in close collaboration to reach that ambitious goal.
Funder: Agence Nationale de la Recherche
Amount: €577,500
Funding type: research
Funder scheme: AAPG2015
Start date: 2015-10-01
Investigator: Fabrice WALLOIS (lead)
Institutions: Groupe de Recherches sur l’Analyse Multimodale de la Fonction Cérébrale, Groupe de REcherches sur l'Analyse Multimodale de la Fonction Cérébrale
Funded outputs count: 28
Funder award ID: ANR-15-CE23-0009
OpenAlex ID: G3934713232