References¶

Publications that use or describe the CNeuroMod datasets.

[1]

A L G Vicente. Codificação de Dados fMRI com Grandes Modelos de Linguagem: Avaliação de Estratégia Multissujeito e Mapeamento Intersujeito. PhD thesis, Universidade de São Paulo, 2025.

[2]

B Banić. From Brain Signal Structure to Function Attribution: An Examination of Schaefer Parcel Dynamics under Multimodal Naturalistic Stimulation. PhD thesis, University of Zagreb, 2026.

[3]

Marie St-Laurent, Basile Pinsard, Oliver Contier, Elizabeth DuPre, Katja Seeliger, Valentina Borghesani, Julie A Boyle, Lune Bellec, and Martin N Hebart. CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience. Sci. Data, 13(1):141, January 2026.

[4]

Subha Nawer Pushpita and Leila Wehbe. Two cortical mechanisms for natural audiovisual processing. bioRxiv, 2025.

[5]

Andrea Corsico, Giorgia Rigamonti, Simone Zini, Luigi Celona, and Paolo Napoletano. Network-specific models for multimodal brain response prediction. arXiv e-prints, pages arXiv–2508, 2025.

[6]

Daniel Carlström Schad, Shrey Dixit, Janis Keck, Viktor Studenyak, Aleksandr Shpilevoi, and Andrej Bicanski. VIBE: video-input brain encoder for fMRI response modeling. arXiv [cs.LG], July 2025.

[7]

Cesar Kadir Torrico Villanueva, Jiaxin Cindy Tu, Mihir Tripathy, Connor Lane, Rishab Iyer, and Paul S Scotti. Predicting brain responses to natural movies with multimodal LLMs. arXiv [cs.CV], July 2025.

[8]

Hamid Abdollahi, Amir Hossein Mansouri Majoumerd, Amir Hossein Bagheri Baboukani, Amir Abolfazl Suratgar, and Mohammad Bagher Menhaj. Probing multimodal fusion in the brain: the dominance of audiovisual streams in naturalistic encoding. arXiv [cs.CV], July 2025.

[9]

Xuanhua Yin, Runkai Zhao, and Weidong Cai. Improving multimodal brain encoding model with dynamic subject-awareness routing. arXiv [cs.AI], February 2026.

[10]

Daniel Carlström Schad, Shrey Dixit, Janis Keck, Viktor Studenyak, Aleksandr Shpilevoi, and Andrej Bicanski. VIBE: video-input brain encoder for fMRI response modeling. arXiv [cs.LG], July 2025.

[11]

[12]

Qianyi He and Yuan Chang Leong. A multimodal Seq2Seq transformer for predicting brain responses to naturalistic stimuli. arXiv [cs.CV], July 2025.

[13]

Xuanhua Yin, Runkai Zhao, Lina Yao, and Weidong Cai. BrainVista: modeling naturalistic brain dynamics as multimodal next-token prediction. arXiv [q-bio.NC], February 2026.

[14]

Robert Scholz, Kunal Bagga, Christine Ahrends, and Carlo Alberto Barbano. Stacked regression using off-the-shelf, stimulus-tuned and fine-tuned neural networks for predicting fMRI brain responses to movies (algonauts 2025 report). arXiv [eess.IV], October 2025.

[15]

Andrea Corsico, Giorgia Rigamonti, Simone Zini, Luigi Celona, and Paolo Napoletano. The ISLab solution to the algonauts challenge 2025: a multimodal deep learning approach to brain response prediction. arXiv [q-bio.NC], October 2025.

[16]

Semih Eren, Deniz Kucukahmetler, and Nico Scherf. Multimodal recurrent ensembles for predicting brain responses to naturalistic movies (algonauts 2025). arXiv [q-bio.NC], October 2025.

[17]

Prachi Jindal, Anant Khandelwal, Manish Gupta, Bapi S Raju, Subba Reddy Oota, and Tanmoy Chakraborty. How does longer temporal context enhance multimodal narrative video processing in the brain? arXiv [q-bio.NC], February 2026.

[18]

Pravish Sainath. Modeling functional brain activity of human working memory using deep recurrent neural networks. PhD thesis, Université de Montréal, 2020.

[19]

Subba Reddy Oota, Khushbu Pahwa, Mounika Marreddy, Maneesh Singh, Manish Gupta, and Bapi S Raju. Multi-modal brain encoding models for multi-modal stimuli. arXiv [q-bio.NC], May 2025.

[20]

Viacheslav Fokin and Arefeh Sherafati. Functional connectivity localizes a distributed supramodal core for naturalistic viewing. bioRxiv, 2025.

[21]

A Kemtur. AI-based modeling of brain and behavior: Combining neuroimaging, imitation learning and video games. PhD thesis, Université de Montréal, 2023.

[22]

Robert Scholz, Austin Benn, Victoria Shevchenko, Ulysse Klatzmann, Wei Wei, Francesco Alberti, Rocco Chiou, Xi-Han Zhang, Robert Leech, Jonathan Smallwood, and Daniel Margulies. Individual brain activity patterns during task are predicted by distinct resting-state networks that may reflect local neurobiological features. bioRxiv, 2024.

[23]

Pravish Sainath, Guillaume Lajoie, and Lune Bellec. Task-optimized artificial neural networks align with human brain activity in a visual working memory task. PsyArXiv, 2025.

[24]

M Freteault. Neuro-inspired artificial intelligence applied to auditory perception and imagination of natural scenes. PhD thesis, Université de Montréal, 2025.

[25]

Yann Harel. Du flux cérébral au flow psychologique: vers une étude de la fluctuation des états attentionnels par la neuroimagerie du jeu vidéo. PhD thesis, Université de Montréal, 2024.

[26]

Isil Poyraz Bilgin, Marie St-Laurent, Lune P Bellec, and Leila Wehbe. Brain-informed language model training enables scalable and generalizable alignment with human brain activity. openreview.net, October 2025.

[27]

Andrea Corsico, Giorgia Rigamonti, Simone Zini, Luigi Celona, and Paolo Napoletano. Decoding affective states from fMRI using automatically labeled multi-modal movie stimuli. In Lecture Notes in Computer Science, Lecture Notes in Computer Science, pages 89–100. Springer Nature Switzerland, Cham, 2026.

[28]

Mariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, and Leila Wehbe. Same cause; different effects in the brain. Proc. Mach. Learn. Res., 177:787–825, April 2022.

[29]

Nicole Rogalla, Yuzhen Qin, Mario Senden, Ahmed El-Gazzar, and Marcel van Gerven. Probabilistic prediction of neural dynamics via autoregressive flow matching. arXiv [q-bio.NC], April 2026.

[30]

Nico Policzer, Cameron Braunstein, and Mariya Toneva. The one where they brain-tune for social cognition: multi-modal brain-tuning on friends. arXiv [cs.AI], November 2025.

[31]

Dota Tianai Dong and Mariya Toneva. Interpreting multimodal video transformers using brain recordings. In ICLR 2023 Workshop on Multimodal Representation Learning: Perks and Pitfalls. May 2023.

[32]

Julie A Boyle and Basile Pinsard. CNeuroMod data collection complete: 200h of individual fMRI across diverse naturalistic and controlled tasks to build NeuroAI models. https://2025.ccneuro.org/abstract_pdf/Boyle_2025_CNeuroMod_Data_Collection_Complete_200h_individual.pdf. Accessed: 2026-5-4.

[33]

Eddy Fortier, Pierre Bellec, Julie A Boyle, and Adrian Fuente. MRI noise and auditory health: can one hundred scans be linked to hearing loss? the case of the courtois NeuroMod project. PLoS One, 20(1):e0309513, January 2025.

[34]

Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit, Hubert Banville, and Jean-Rémi King. TRIBE: TRImodal brain encoder for whole-brain fMRI response prediction. In Proceedings of the 14th International Conference on Learning Representations 2026. April 2026.

[35]

Yann Harel, Lune P Bellec, François Paugam, Hugo Delhaye, and Audrey Durand. Human-AI alignment of learning trajectories in video games: a continual RL benchmark proposal. In Reinforcement Learning and Video Games Workshop @ RLC 2025. July 2025.

[36]

François Paugam, Basile Pinsard, Marie St-Laurent, Guillaume Lajoie, and Lune Bellec. Training neural networks from scratch in a videogame leads to brittle brain encoding. bioRxiv, December 2025.

[37]

Yann Harel, Marie St-Laurent, and Lune Bellec. Brittle brain encoding: poor out-of-distribution generalization shows the human brain is neither a nintendo entertainment system nor a four-layer convolutional neural network.

[38]

Paul S Scotti and Mihir Tripathy. Insights from the algonauts 2025 winners. arXiv [q-bio.NC], August 2025.

[39]

Mathieu Boudreau, Agah Karakuzu, Arnaud Boré, Basile Pinsard, Kiril Zelenkovski, Eva Alonso-Ortiz, Julie Boyle, Lune Bellec, and Julien Cohen-Adad. Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers. Imaging Neurosci. (Camb.), January 2025.

[40]

Maelle Freteault, Maximilien Le Clei, Loic Tetrel, Pierre Bellec, and Nicolas Farrugia. Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalizable improvements in brain encoding and downstream tasks. Imaging Neuroscience, March 2025.

[41]

Sana Ahmadi, Francois Paugam, Tristan Glatard, and Pierre Lune Bellec. Training compute-optimal vision transformers for brain encoding. arXiv [eess.IV], October 2024.

[42]

François Paugam, Basile Pinsard, Guillaume Lajoie, and Pierre Bellec. A benchmark of individual auto-regressive models in a massive fMRI dataset. Imaging Neuroscience, 2:1–23, July 2024.

[43]

Mariya Toneva, Tom M Mitchell, and Leila Wehbe. Combining computational controls with natural text reveals aspects of meaning composition. Nat Comput Sci, 2(11):745–757, November 2022.

[44]

V Borghesani, J Armoza, M N Hebart, P Bellec, and S M Brambati. The three terms task - an open benchmark to compare human and artificial semantic representations. Scientific Data, 10(1):1–13, March 2023.

[45]

Anirudha Kemtur, Francois Paugam, Basile Pinsard, Pravish Sainath, Maximilien Le Clei, Julie Boyle, Karim Jerbi, and Pierre Bellec. Behavioral imitation with artificial neural networks leads to personalized models of brain dynamics during videogame play. bioRxiv, pages 2023.10.28.564546, November 2023.

[46]

Shima Rastegarnia, Marie St-Laurent, Elizabeth DuPre, Basile Pinsard, and Pierre Bellec. Brain decoding of the human connectome project tasks in a dense individual fMRI dataset. Neuroimage, 283:120395, December 2023.

[47]

François Paugam, Basile Pinsard, Guillaume Lajoie, and Pierre Bellec. A benchmark of individual auto-regressive models in a massive fMRI dataset. PsyArXiv, February 2023.

[48]

A Kemtur, F Paugam, B Pinsard, P Sainath, Y Harel, M Le clei, J A Boyle, K Jerbi, and P Bellec. AI-based modeling of brain and behavior: combining neuroimaging, imitation learning and video games. In 2022 Conference on Cognitive Computational Neuroscience, 1303. 2022.