64 Papers
200 Citations
Elvis Dohmatob is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Computer science & Smoothing. The author has an hindex of 12, co-authored 50 publications. Previous affiliations of Elvis Dohmatob include Commissariat à l'énergie atomique et aux énergies alternatives & Université Paris-Saclay.
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Papers
Which fMRI clustering gives good brain parcellations
TL;DR: It is shown that in general Ward’s clustering performs better than alternative methods with regard to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.
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Which fMRI clustering gives good brain parcellations
TL;DR: In this paper, the authors address the question of which clustering technique is appropriate and how to optimize the corresponding model, and use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples.
199
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping.
Ana Luísa Pinho,Alexis Amadon,Torsten Ruest,Murielle Fabre,Elvis Dohmatob,Isabelle Denghien,Chantal Ginisty,Séverine Becuwe-Desmidt,Séverine Roger,Laurence Laurier,Véronique Joly-Testault,Gaëlle Médiouni-Cloarec,Christine Doublé,Bernadette Martins,Philippe Pinel,Evelyn Eger,Gaël Varoquaux,Christophe Pallier,Stanislas Dehaene,Lucie Hertz-Pannier,Bertrand Thirion +20 more
TL;DR: The present article gives a detailed description of the first release of the IBC dataset, a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain.
Dark control: The default mode network as a reinforcement learning agent.
TL;DR: A process model that tries to explain how the default mode network may implement continuous evaluation and prediction of the environment to guide behavior and offers parsimonious explanations for recent experimental findings in animals and humans is proposed.
114
Extracting Brain Regions from Rest fMRI with Total-Variation Constrained Dictionary Learning
Alexandre Abraham,Elvis Dohmatob,Bertrand Thirion,Dimitris Samaras,Dimitris Samaras,Gaël Varoquaux +5 more
- 22 Sep 2013
TL;DR: In this paper, a new tool drawing from clustering and linear decomposition methods by carefully crafting a penalty is introduced to automatically extract regions from rest fMRI that better explain the data and are more stable across subjects.