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). ​

Members

Principal Investigators

• Benoit Dufumier - Research Scientist


PhD Students

• Pierre Auriau - PhD 2025 (prepared under co-superv. E Duchesnay, JF Mangin, P Gorin, A Grigis)

• Sara Petiton - PhD 2025 (prepared under co-superv. E Duchesnay, A Grigis, J Bourgin)

• Thibault Dupont - PhD 2026 (prepared under superv. E Duchesnay, J Bourgin, E Azoulay)

• Raphael Vock - PhD 2027 (prepared under co-superv. E Duchesnay, A Grigis, J Bourgin)


Alumni

• Cecilia Damon - PhD 2011 (prepared under co-superv. E Duchesnay, J Poline, B Thirion)

• Edith Lefloch - PhD 2012 (prepared under co-superv. E Duchesnay, V Frouin)

• Jinpeng Li - Research Engineer 2013-2014 (under superv. E Duchesnay)

• Fouad Hadj Selem - PostDoc 2013-2015 (under superv. E Duchesnay)

• Mathieu Dubois - PostDoc 2013-2014 (under superv. E Duchesnay)

• Pauline Favre - PostDoc 2016 (under co-superv. E Duchesnay, J. Houenou, and JF Mangin)

• Pietro Gori - PostDoc 2016 (under superv. E Duchesnay)

• Tommy Lofstedt - PostDoc 2013-2015 (under superv. E Duchesnay)

• Clémence Pinaud - Engineer 2014 (under superv. E Duchesnay)

• Amicie de Pierrefeu - PhD 2019 (prepared under co-superv. E Duchesnay, P Ciuciu)

• Anton Iftimovici - PhD 2021 (prepared under co-superv. E Duchesnay, MO Krebs, J Bourgin)

• Benoit Dufumier - PhD 2022 (prepared under co-superv. E Duchesnay, A Tenenhaus, P Gori and A Grigis)

• Robin Louiset - PhD 2024 (prepared under co-superv. E Duchesnay, P Gori, A Grigis)


Gallery

Transfer learning strategy to bridge the gap between big and small data

Exploring the potential of representation and transfer learning for anatomical neuroimaging - application to psychiatry.