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fMRI brown bag: November 18, 2022

Pierre-Louis Bellec

Dr. Pierre-Louis Bellec is an Associate Professor in the Department of Psychology at the University of Montreal. Dr. Bellec's main research interest is to train artificial neural networks to mimic human brain activity and behavior, at the level of individuals. To achieve that goal, he leads an intensive effort of neuroimaging data collection; the Courtois project on neuronal modeling (https://cneuromod.ca). 

 

The Courtois project: a deep fMRI dataset to build individual brain models with naturalistic tasks

Summary 

The Courtois project on neuronal modeling (CNeuroMod) aims to improve the task flexibility of artificial neural networks using activity recorded from biological neural networks. CNeuroMod has collected the largest “deep” functional magnetic resonance imaging (fMRI) dataset currently available, with up to 150 hours of fMRI data per subject (N=6) [1]. The project covers a wide range of tasks, broadly categorized in domains such as vision, audition, language, memory, emotions and videogames. In this talk, I will provide an overview of the Courtois NeuroMod dataset, and then present initial results in the videogame domain.  We first built and validated an MR-compatible game controller [2], enabling participants to play challenging retro games from the era of NES and Genesis consoles. We then successfully trained artificial agents to imitate the actions of humans playing the game “Shinobi III: revenge of the ninja master” and found that the internal representations of the agents could be used to effectively predict individual brain activity measured with functional magnetic resonance imaging. This work could open new avenues to train robust AI video game characters, and provide new insights in brain representations for active and complex stimuli.
[1] https://www.docs.cneuromod.ca
[2] Harel, Yann, André Cyr, Julie A. Boyle, Basile Pinsard, Karim Jerbi, and Pierre Bellec. 2022. “Gamer in the Scanner: Open Design and Validation of a Video Game Controller for MRI and MEG.” PsyArXiv. September 16. https://doi.org/10.31234/osf.io/m2x6y  
[3] Kemtur, A., Paugam, F., Pinsard, B., Sainath, P., Harel, Y., Le Clei, M., Boyle, J.A., Jerbi, K., Bellec, P., 2022. AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games. In: 2022 Conference on Cognitive Computational Neuroscience. Presented at the Cognitive Computational Neuroscience, p. 1303. https://doi.org/10.32470/CCN.2022.1303-0