Zobrazeno 1 - 6
of 6
pro vyhledávání: '"François Paugam"'
Autor:
Hao-Ting Wang, Steven L Meisler, Hanad Sharmarke, Natasha Clarke, Nicolas Gensollen, Christopher J Markiewicz, François Paugam, Bertrand Thirion, Pierre Bellec
Publikováno v:
PLoS Computational Biology, Vol 20, Iss 3, p e1011942 (2024)
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchm
Externí odkaz:
https://doaj.org/article/80ca0fd6af574cc798f616a11a8192a7
Autor:
Hao-Ting Wang, Steven L Meisler, Hanad Sharmarke, Natasha Clarke, François Paugam, Nicolas Gensollen, Christopher J Markiewicz, Bertrand Thirion, Pierre Bellec
Publikováno v:
bioRxiv
Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b986408aa97b18378c5468b6608922b7
https://doi.org/10.1101/2023.04.18.537240
https://doi.org/10.1101/2023.04.18.537240
Autor:
Aki Nikolaidis, Matteo Manchini, Tibor Auer, Katherine L. Bottenhorn, Eva Alonso-Ortiz, Gabriel Gonzalez-Escamilla, Sofie Valk, Tristan Glatard, Melvin Selim Atay, Johanna M.M. Bayer, Janine Bijsterbosch, Johannes Algermissen, Natacha Beck, Patrick Bermudez, Isil Poyraz Bilgin, Steffen Bollmann, Claire Bradley, Megan E.J. Campbell, Bryan Caron, Oren Civier, Luis Pedro Coelho, Shady El Damaty, Samir Das, Mathieu Dugré, Eric Earl, Stefanie Evas, Nastassja Lopes Fischer, De Fu Yap, Kelly G. Garner, Remi Gau, Giorgio Ganis, Dylan G. E. Gomes, Martin Grignard, Samuel Guay, Omer Faruk Gulban, Sarah Hamburg, Yaroslav O. Halchenko, Valerie Hayot-Sasson, Dawn Liu Holford, Laurentius Huber, Manuel Illanes, Tom Johnstone, Avinash Kalyani, Kinshuk Kashyap, Han Ke, Ibrahim Khormi, Gregory Kiar, Vanja Ković, Tristan Kuehn, Achintya Kumar, Xavier Lecours-Boucher, Michael Lührs, Robert Luke, Cecile Madjar, Sina Mansour L., Chris Markeweicz, Paula Andrea Martinez, Alexandra McCarroll, Léa Michel, Stefano Moia, Aswin Narayanan, Guiomar Niso, Emmet A. O’Brien, Kendra Oudyk, François Paugam, Yuri G. Pavlov, Jean-Baptiste Poline, Benedikt A. Poser, Céline Provins, Pradeep Reddy Raamana, Pierre Rioux, David Romero-Bascones, Ekansh Sareen, Antonio Schettino, Alec Shaw, Thomas Shaw, Cooper A. Smout, Anđdela Šoškié, Jessica Stone, Suzy J Styles, Ryan Sullivan, Naoyuki Sunami, Shamala Sundaray, Jasmine Wei Rou, Dao Thanh Thuy, Sebastien Tourbier, Sebastián Urch, Alejandro de la Vega, Niruhan Viswarupan, Adina Wagner, Lennart Walger, Hao-Ting Wang, Fei Ting Woon, David White, Christopher Wiggins, Will Woods, Yu-Fang Yang, Ksenia Zaytseva, Judy D. Zhu, Marcel P. Zwiers
Publikováno v:
Aperture Neuro.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9649ef1d06cdd1d9a35f2b3f9ffb7441
https://doi.org/10.52294/258801b4-a9a9-4d30-a468-c43646391211
https://doi.org/10.52294/258801b4-a9a9-4d30-a468-c43646391211
Dense functional magnetic resonance imaging datasets open new avenues to create auto-regressive models of brain activity. Individual idiosyncrasies are obscured by group models, but can be captured by purely individual models given sufficient amounts
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee077289d9b88315468cf7b70ef72756
https://doi.org/10.31234/osf.io/pvx3d
https://doi.org/10.31234/osf.io/pvx3d
Publikováno v:
2022 Conference on Cognitive Computational Neuroscience.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10771fed8cae4e0855493f17406d2fe7
https://doi.org/10.32470/ccn.2022.1228-0
https://doi.org/10.32470/ccn.2022.1228-0
Autor:
Pascal Sati, Charley Gros, Julien Cohen-Adad, Daniel S. Reich, François Paugam, Jennifer Lefeuvre, Christian S. Perone
Publikováno v:
Magn Reson Imaging
This paper presents an open-source pipeline to train neural networks to segment structures of interest from MRI data. The pipeline is tailored towards homogeneous datasets and requires relatively low amounts of manual segmentations (few dozen, or les
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fb2ecf1138c847930197eabc081f933
https://doi.org/10.1016/j.mri.2019.04.009
https://doi.org/10.1016/j.mri.2019.04.009