Author

Michael Paquette

Université de SherbrookeORCID 0000-0002-8917-3391Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:statistical model· 3 Organism:human· 2 Approach:benchmark study· 1 Assay:imaging· 1 Assay:multiome· 1 Organism:non-human primate· 1

Frequent Co-Authors

Alessandro DaducciiD· 2 shared Alexander LeemansiD· 2 shared David RomascanoiD· 2 shared François RheaultiD· 2 shared Gabriel GirardiD· 2 shared Jasmeen Sidhu· 2 shared Jean‐Philippe ThiraniD· 2 shared Klaus Hermann Maier-HeiniD· 2 shared

Activity by Year

2017-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

Alessandro DaducciORCID

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3 papers

Recent·Most cited

Increased Sensitivity and Signal-to-Noise Ratio in Diffusion-Weighted MRI using Multi-Echo Acquisitions

Cornelius Eichner, Michael Paquette, Toralf Mildner, Torsten Schlumm, Kamilla Pléh, Liran Samuni, Catherine Crockford, Roman Martin Wittig, Carsten Jäger, Harald E. Möller, Angela Dorkas Friederici, Alfred Anwander

This paper introduces a Multi-Echo dMRI method to improve SNR and reduce noise bias in post-mortem diffusion MRI, validated on a chimpanzee brain.

NeuroImage · Jul 15, 2020Peer-reviewedNeuroscience

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

Tractography-based connectomes are dominated by false-positive connections

Klaus Hermann Maier-Hein, Peter Neher, Jean‐Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, jinyu zhong, Maxime Chamberland, Fang-Cheng F. Yeh, Ying‐Chia Lin, Qing Ji, Wilburn E. Reddick, John O. Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, , Maxime Descoteaux

The paper evaluates tractography methods by analyzing a simulated dataset, finding that most algorithms generate many false-positive connections.

Nature Communications · Nov 1, 2017Peer-reviewedNeuroscience