PhD Position F/M HARNESSING LIKELIHOOD-FREE INFERENCE TO LINK DIFFUSION MRI IMAGING WITH CYTOARCHITECTURE CHANGES IN THE MAMMALIAN BRAIN

Inria
September 22, 2022
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2022-05282 - PhD Position F/M HARNESSING LIKELIHOOD-FREE INFERENCE TO LINK DIFFUSION MRI IMAGING WITH CYTOARCHITECTURE CHANGES IN THE MAMMALIAN BRAIN

Contract type : Fixed-term contract

Level of qualifications required : Graduate degree or equivalent

Fonction : PhD Position

About the research centre or Inria department

Located at the heart of the main national research and higher education cluster, member of the Université Paris Saclay, a major actor in the French Investments for the Future Programme (Idex, LabEx, IRT, Equipex) and partner of the main establishments present on the plateau, the centre is particularly active in three major areas: data and knowledge; safety, security and reliability; modelling, simulation and optimisation (with priority given to energy).

The 450 researchers and engineers from Inria and its partners who work in the research centre's 28 teams, the 60 research support staff members, the high- level equipment at their disposal (image walls, high-performance computing clusters, sensor networks), and the privileged relationships with prestigious industrial partners, all make Inria Saclay Île-de-France a key research centre in the local landscape and one that is oriented towards Europe and the world

Context

Within the framework of a partnership (you can choose between)

  • not applicable,
  • Is regular travel foreseen for this post ? Regular travel forseen to conferences and visits to instittues around the world

    Assignment

    Assignments :

    Sensing microstructural characteristics of human brain tissue with clinical MRI scanners has been an area of heated debate in the diffusion MRI (dMRI) community 1–3. We have recently presented evidence that, if we focus on the cortex, specifically in the insula and anterior cingulate cortex (ACC), the unique characteristics of the cellular populations in these gyri allow us to use clinical-grade scanners to sense the presence of Von Economo neurons (VENs) and link their presence to cognitive function 4. VENs, uniquely localized in the insula and ACC, are large neurons and their particular size is what enables their quantification through dMRI. However, the inverse problem relating microstructural characteristics to microstructural configurations is plagued by indeterminacies 5. Furthermore, required dMRI imaging protocols to invert such models are extremely demanding in terms of acquisition time and gradient strength. These combined difficulties point to a lack of computational tools to expand microstructural studies on the mammalian cortex to a wider variety of neuronal populations and cortical areas. Sensing microstructural characteristics of human brain tissue with clinical MRI scanners has been an area of heated debate in the diffusion MRI (dMRI) community 1–3. We have recently presented evidence that, if we focus on the cortex, specifically in the insula and anterior cingulate cortex (ACC), the unique characteristics of the cellular populations in these gyri allow us to use clinical-grade scanners to sense the presence of Von Economo neurons (VENs) and link their presence to cognitive function 4. VENs, uniquely localized in the insula and ACC, are large neurons and their particular size is what enables their quantification through dMRI. However, the inverse problem relating microstructural characteristics to microstructural configurations is plagued by indeterminacies 5. Furthermore, required dMRI imaging protocols to invert such models are extremely demanding in terms of acquisition time and gradient strength. These combined difficulties point to a lack of computational tools to expand microstructural studies on the mammalian cortex to a wider variety of neuronal populations and cortical areas.

    We propose a project that pushes on a thriving axis of dMRI for microstructure quantification by focusing on the cortex, rather than the heretofore much studied white matter areas. Our main goal is to hedge and deepen our recent advancements in microstructure quantification through likelihood-free inference (LFI) 6, 7 to bypass model indeterminacy issues; gain precision; and increase the specificity of current links between cognition, development, pathology and cytoarchitecture.

    Likelihood-free inference is a recent tool combining Bayesian analysis and Deep Learning for probabilistic model inversion 8. As opposed to other approaches, LFI-based algorithms yield estimations of model parameters along with a full posterior distribution over the parameter space. This enables model inversion results encompassing indicators linking dMRI data with confidence intervals, along with the estimation of higher moments of the posterior distribution. An excelling characteristic of our LFI-based approach is to pinpoint cortical areas where a proposed microstructural model and the data harmonize together to provide an accurate model inversion. Consequently, LFI-based models provide enriched parameter estimations which also indicate which microstructural parameters, such as neuronal soma size or neurite density, can be confidently used in a specific dataset to link cytoarchitecture with cognition and pathology. In this PhD project, we will push forward our current advances in LFI-based cytoarchitecture quantification for dMRI. In particular, we will explore the design of summary statistic networks for LFI and simulation-based inference through the use of hierarchical modelling of cytoarchitecture-induced diffusion MRI signals. Harnessing our collaborations with the Institut Pasteur in Paris (cf. R. Toro) and Stanford Medical School (cf. V. Menon) we will apply our advancements to the analysis of mammalian brain development in ferrets and cognitive function modelling in adolescents. We foresee the following challenges in this project:

    1) Design and production of a forward probabilistic model of brain tissue cytoarchitecture which can articulate with LFI algorithms and pushes precision beyond what we have achieved through simple geometrical models; 2) Conceive LFI techniques to invert designed probabilistic models and develop a non-linear-based model linking estimated parameter posteriors with development and cognition;

    3) Widen the applicability of our cytoarchitecture-detection methods to more general dMRI acquisitions and open access databases such as the Adolescent Brain Cognitive Development.

    We have assembled a team that is capable of overcoming the aforementioned challenges. The Parietal team (https:// team.inria.fr/parietal) from INRIA (https:// www. inria.fr) and Neurospin which has a provable experience in developing and applying machine learning models to dMRI and linking dMRI with cognition and pathology; the Unit of applied and theoretical neuroanatomy from Institut Pasteur (https: // neuroanatomy.github.io) which has extensive experience in animal model studies for the study of mammalian neuroanatomy 9, 10; and the Stanford Cognitive and Systems Neuroscience Laboratory, USA (https:// med.stanford.edu/scsnl.html) with an outstanding track record in imaging-based cognitive analysis.

    Steering/Management :

    The person recruited will be in charge of performing research and development in the frontier of neuroscience, machine learning, and computer sciences.

    Main activities

    Main activities :

  • Basic and applied research in neuroscience and knowledge representation
  • high-quality coding within a quality/assured framework
  • inter-disciplinary research
  • Skills

    Technical skills and level required : Discrete mathematics, numerical model implementations, data analytics. Python programming and knowledge in biology will be appreciated

    Languages : Excelent English conversational and written skills

    Relational skills : good Interpersonal Abilities and Problem- Solving/Reasoning/Creativity

    Benefits package
  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage
  • Remuneration

    1st and 2nd year :1982 euros/Month

    3rd year : 2.085 euros/Month

    General Information
  • Theme/Domain : Computational Neuroscience and Medicine
  • Town/city : Palaiseau
  • Inria Center : CRI Saclay - Île-de-France
  • Starting date : 2022-10-01
  • Duration of contract : 2 years
  • Deadline to apply : 2022-09-22
  • Contacts
  • Inria Team : PARIETAL
  • PhD Supervisor : Wassermann Demian / demian.wassermann@inria.fr
  • The keys to success
  • Good skills in computer science, probabilites, and or physics,
  • Teamwork preference,
  • Vocation for cross-disciplinary research
  • Essential qualities in order to fulfil this assignment are feeling at ease in an environment of scientific dynamics and wanting to learn and listen.
  • Passionate about innovation, with expertise in Pythondevelopment and strong influencing skills. A master thesis in the field of neuroimaging is a real asset.
  • About Inria

    Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.

    Instruction to apply

    Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.

    Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities.

    Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.

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