The Courtois project on neuronal modelling: scaling up AI models of individual brains in a massive individual fMRI dataset
Creators: name: Bellec, Pierre
name: Boyle, Julie A
name: Pinsard, Basile
name: Fortier, Eddy
name: Zhang, Yu
name: Borghesani, Valentina
name: Rastegarnia, Shima
name: Paugam, François
Publisher: name: Zenodo
Year: 2023
Subjects: neuroimaging, modelling, brain networks, fMRI, deep individual sampling

description: The Courtois project on neuronal modelling (CNeuroMod) aims to improve the task flexibility of artificial neural networks using activity recorded from biological neural networks. CNeuroMod has collected an unprecedented “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 categorised in domains such as vision, audition, language, memory, emotions and videogames. In this talk, I will provide an overview of the contents of the Courtois NeuroMod database, and then present initial results on scaling up AI models of individual brains. I will first discuss some recent works on brain decoding using the human connectome project (HCP) task battery. We found a marked advantage of deep graph convolutional networks for group level decoding on that benchmark using the full original HCP sample (N=1200) [2,3]. Using Courtois Neuromod’s <em>hcptrt</em> dataset, where we repeated HCP’s task battery up to 15 times per subject, we successfully trained brain decoding models on single individuals that matched the performance of the group decoder, using two orders of magnitude less data [4]. I will also discuss a recent benchmark of auto-regressive models for individual video-watching fMRI data (i.e. <em>movie10 and friends d</em>ataset), where we looked at over 10 different time series models, and up to 10 hours of training data [5]. We found that graph convolutional networks had the best performance overall, and performance scaled up with the amount of data without reaching a ceiling at 10 hours. Overall, these results demonstrate that large individual fMRI dataset can be used to efficiently train purely individual AI models of brain activity, and that massive amounts of individual data are beneficial to this endeavour. <strong>References</strong> [1] https://www.docs.cneuromod.ca [2] Zhang, Y., Tetrel, L., Thirion, B., Bellec, P., 2021. Functional annotation of human cognitive states using deep graph convolution. Neuroimage 231, 117847. https://doi.org/10.1016/j.neuroimage.2021.117847 [3] Zhang, Y., Farrugia, N., Bellec, P., 2022. Deep learning models of cognitive processes constrained by human brain connectomes. Med. Image Anal. 80, 102507. https://doi.org/10.1016/j.media.2022.102507 [4] Rastegarnia, S., Tetrel, L., Pinsard, B., DuPre, E., Zhang, Y., &amp; Bellec, P. (2022, September 16). Brain decoding of the Human Connectome Project Tasks in a Dense Individual fMRI Dataset. Psyarxiv preprint https://doi.org/10.31234/osf.io/9t5nh [5] Paugam, F., Pinsard, B., Lajoie, G., &amp; Bellec, P. (2023, February 17). A benchmark of individual auto-regressive models in a massive fMRI dataset. Psyarxiv preprint https://doi.org/10.31234/osf.io/pvx3d
description: The Courtois project on neural modelling was made possible by a generous donation from the Courtois foundation, administered by the Fondation Institut Gériatrie Montréal at CIUSSS du Centre-Sud-de-l'île-de-Montréal and University of Montreal. The Courtois NeuroMod team is based at "Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal", with several other institutions involved. See the cneuromod documentation for an up-to-date list of contributors (https://docs.cneuromod.ca).