Vous êtes ici : Accueil > Entités de recherche > NeuroSpin > BAOBAB > SIGNATURE team

NEUROIMAGING SIGNATURES OF BRAIN DISORDERS

SIGNATURE team

​My research focuses on the design of cutting-edge machine learning and statistical models to uncover neural signatures that can predict psychiatric disorders. Our goal is to harness the power of data by overseeing the effective management of multiple large-scale national and European initiatives. This will allow us to train and refine AI models to their fullest potential. ​

Publié le 26 juin 2023

​Princi​pal inv​​estigator: Edouard Duchesnay

​​Artificial Intelligence (AI) combined with neuroimaging opens up possibilities for personalized medicine. With this long-term objective, we developed four lines of research:

MODELS TO PRODUCE INT​​​ERPRETABLE BRAIN SIGNATURES OF DISORDERS

We investigated new predictive linear models that integrate prior biological knowledge to force the solution to adhere to biological priors, producing more plausible interpretable signatures. These models have been used to uncover an anatomical pattern of schizophrenia and a functional pattern for hallucinations. We embraced the applied mathematic challenge of creating scalable optimization solvers [for high-dimensional neuroimaging data while being flexible enough to integrate various priors.

MODELS TO ​​BRIDGE THE GAP BETWEEN BIG AND SMALL DATA

Thanks to the award of a Chair in AI (2020-2025), we proposed new weakly-supervised deep neural networks that are pre-trained on large datasets of controls, using auxiliary information such as “age” to improve the
embedded representation of the general variability. Models are then transferred to smaller samples of patients to reveal the specific signal associated with psychiatric disorders .

MODELS FOR PA​​​TIENTS’ STRATIFICATION ​​INTO HOMOGENEOUS SUBGROUPS

with shared etiologies for individualized therapeutic strategy.

UNLOCKING TH​​​E DATA ACCESS

Learning models require collecting more and better data (wide and deep phenotyping). First, we tackled the “big data challenge” by aggregating open datasets (UKB, ABCD, HBN) into an interoperable database. Second, we actively contributed and will continue to play a major role to the emergence of deeply phenotyped datasets by leading the data management and analysis of several large European and national projects( PEPR PROPSY, RHUs FAME and PsyCARE, European project R-LiNK). ​

Tranfert learning strategy to bridge the gap between big and small data.​