Équipe IMIS - Imagerie Multimodale Intégrative en Santé

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Gender dimorphism inearly Alzheimer’s pathology: Brain connectivity with MRI and behavioralanalysis in a mouse humanized for the genes of App and MAPT
Co-directed Interdisciplinary PhDproject –ICube Lab / LNCA; University of Strasbourg/CNRS
Project funded by FRM (Fondation pour la Recherche Medicale)

Research project:

Women have a higher rate of cognitive decline than men at a preclinical stage of Alzheimer’s disease (AD). Moreover –during the AD progression, cognitive decline, brain atrophy and tau pathology, are more pronounced in women (1). Finding gender-specific biomarkers that predict AD onset and evolutionis absolutely critical for accurate diagnosis and personalized therapies (2).AIM and approaches: In this project we will combinefunctional and structural brain Magnetic Resonance Imaging(MRI), network analysis approaches and behavioral phenotyping to investigate thegender specific connectivity signatures of AD. We will use in a longitudinal design a new preclinical AD model-the AppNL-F/MAPT double knock-in (dKI) mouse; humanized for the genes of App and MAPT (3). In this model, LNCA(Laboratory of Cognitive and AdaptiveNeurosciences)recently identifiedanearly vulnerability of the females for cognitive deficits; howeverthe circuitries involved in this early phenotype are not known.Meanwhile, in another model of AD, a tauopathymouse model -the ICube/LNCAlabsrecently demonstrated using resting state functional MRI (rsfMRI) thatremodeling ofbrain networks architecture precede behavioral deficits, and moreover can highlight compensatory pathways(4).

Based on these findings we will apply multi-variate analysis approachesto study gender dimorphism in early Alzheimer’s pathology:(i) resting state functional MRI to characterize the dynamics of functional network architecture and to identify sex specific network signatures and networks’hubs, critical for memory deficits occurring overtime. Open-ended (whole brain) (5, 6) and hypothesis-driven analyses will be developed to elucidate circuits underlying the pathology.(ii) High angular resolution diffusion imaging (HARDI) and fiber tractography (7) to explore whole brain microstructure and the dynamics of fiber density alterations at different time points over life span. Brain tractography will be associated to anatomical imaging for brain morphometry. (iii) Behavioral testsand histopathological analysis (LNCA expertise) to characterize the cognitive phenotype.


The imaging will be carried-out within the“Integrative Multi-modal Imaging in Healthcare -IMIS”Team, led by Laura-Adela Harsan at ICube (https://icube.unistra.fr/equipes/, Strasbourg). The IMISteam includes experts in MR techniques and signal modeling, brain networks analysis,neurobiology and preclinical models of brain disorders. The project will use the ICube lab imaging platform facilities (7T MRI animal scanner; bioluminescenceand microscopytools). The expertise in animal behavior and histopathology will beprovided viaco-supervision with Chantal Mathis, leading the ENGRAM team at LNCA.

Candidate profile:

The candidate shouldhave background in Neuroscience and/orMR technologies and MR data processing; and be highly motivatedto workwithin an interdisciplinary context foroptimizing/validating/applyingMR methodology in preclinical environment.The selected candidate should have knowledge regarding brain anatomy and function and animal physiology. Programming skills (MATLAB, Phyton) for MRI data processing in correlation with behavioral results are appreciated, as well asgood track record and good proficiency in english.


Laura-Adela Harsan;0368854037; Engineering science, computer science and imaging laboratory
Chantal Mathis, Laboratory of Cognitive and Adaptive Neuroscience

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Refs:(1)Duarte-Guterman et al, bioRxiv, online Aug. 23, 2019; (2) Ferretti et al, Nat Rev Neurol 14:457, 2018; (3) Saito et al, J Biol Chem 294:12754, 2019; (4) Degiorgis L et al., Brain, In press; (5)Mechling et al, PNAS 113:11603, 2016; (6) Arefin et al, Brain Connect 7:526, 2017; (7) Harsan et al, PNAS 110: E1797, 201