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Samuel Deslauriers-Gauthier

Researcher at French Institute for Research in Computer Science and Automation

Publications -  60
Citations -  1509

Samuel Deslauriers-Gauthier is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Diffusion MRI & Tractography. The author has an hindex of 10, co-authored 48 publications receiving 1065 citations. Previous affiliations of Samuel Deslauriers-Gauthier include Université de Sherbrooke & Nanyang Technological University.

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The challenge of mapping the human connectome based on diffusion tractography

Klaus H. Maier-Hein, +76 more
TL;DR: The encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent) is reported, however, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups.
Posted ContentDOI

Tractography-based connectomes are dominated by false-positive connections

Klaus H. Maier-Hein, +76 more
- 07 Nov 2016 - 
TL;DR: The results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.
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A unified framework for multimodal structure-function mapping based on eigenmodes

TL;DR: A unified computational framework is proposed that generalizes recently proposed structure-function mappings based on eigenmodes and shows how they can be obtained by specific choices of the parameters of this framework by applying to 50 subjects of the Human Connectome Project.
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Sampling Signals With a Finite Rate of Innovation on the Sphere

TL;DR: This work shows that a particular class of non-bandlimited signals, which have a finite rate of innovation, can be exactly recovered using a finite number of samples, and designs an optimal sampling kernel that achieves accurate reconstruction of the signal using only 3K samples.