Author

Matteo Frigo

Université Côte d'AzurORCID 0000-0002-9365-8017Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 4

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:statistical model· 4 Research Focus:benchmarking· 3 Assay:multiome· 2 Approach:benchmark study· 1 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Approach:graph neural network· 1

Frequent Co-Authors

Rachid Deriche BiD· 3 shared Samuel Deslauriers‐GauthieriD· 3 shared Alexander LeemansiD· 2 shared Bennett A. LandmaniD· 2 shared Kurt G. SchillingiD· 2 shared Mauro ZucchelliiD· 2 shared Paulo RodriguesiD· 2 shared Prasanna ParvathaneniiD· 2 shared

Activity by Year

2019-2021 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersNeuroscience

Rachid Deriche BORCID

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4 papers

Recent·Most cited

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Alberto De Luca, Andrada Ianuș, Alexander Leemans, Marco Palombo, Noam Shemesh, Hui Gary Zhang, Daniel C. Alexander, Markus Nilsson, Martijn Froeling, Geert Jan Biessels, Mauro Zucchelli, Matteo Frigo, Enes Albay, Sara Sedlar, Abib Alimi, , Kurt G. Schilling

NeuroImage · Jul 6, 2021Peer-reviewedNeuroscience

Network alignment and similarity reveal atlas-based topological differences in structural connectomes

Matteo Frigo, Emilio Cruciani, David Coudert A, Rachid Deriche B, Emanuele Natale, Samuel Deslauriers‐Gauthier

The paper introduces graph similarity measures and alignment techniques to evaluate atlas-based topological robustness in structural connectomes across subjects.

Network Neuroscience · May 21, 2021Peer-reviewedNeuroscience

Multi-Tissue Multi-Compartment models of diffusion MRI

Matteo Frigo, Rutger Fick, Mauro Zucchelli, Samuel Deslauriers‐Gauthier, Rachid Deriche B

This paper introduces a multi-tissue multi-compartment model for diffusion MRI using single-TE acquisitions, enabling analysis of previously acquired datasets.

bioRxiv · Jan 29, 2021 · PreprintNeuroscience

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

Vishwesh Nath, Kurt G. Schilling, Prasanna Parvathaneni, Yuankai Huo, Justin A. Blaber, Allison E. Hainline, Muhamed Baraković, David Romascano, Jonathan Rafael‐Patiño, Matteo Frigo, Gabriel Girard, Jean‐Philippe Thiran, Alessandro Daducci, Matt Rowe, Paulo Rodrigues, , Bennett A. Landman

The paper evaluates tractography reproducibility using empirical data from a single healthy volunteer scanned on two scanners, finding high consistency among top algorithms but variability due to tract volume containment.

Journal of Magnetic Resonance Imaging · Jun 9, 2019Peer-reviewedNeuroscience