Author

Rachel L. C. Barrett

King's College LondonORCID 0000-0002-8235-1992Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:imaging· 2 Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1

Frequent Co-Authors

Flavio Dell’AcquaiD· 3 shared Marco CataniiD· 3 shared Francisco De Santiago Requejo· 2 shared Luís Miguel LacerdaiD· 2 shared Pedro A. Luque LagunaiD· 2 shared Richard StonesiD· 2 shared Ahmad BeyhiD· 1 shared Aldo QuattroneiD· 1 shared

Activity by Year

2017-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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3 shared papersNeuroscience

Flavio Dell’AcquaORCID

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3 papers

Recent·Most cited

MegaTrack: a framework for the anatomically accurate and time-efficient virtual dissection and analysis of large-scale tractography datasets

Flavio Dell’Acqua, Ahmad Beyh, Richard Stones, Rachel L. C. Barrett, Francisco De Santiago Requejo, Pedro A. Luque Laguna, Luís Miguel Lacerda, Catherine Davison, Anoushka Leslie, Henrietta Howells, Laura H. Goldstein, Steven Williams, Marco Catani

MegaTrack provides a framework for efficient and anatomically accurate analysis of large-scale tractography datasets, enabling rapid dissection and metric extraction with high reproducibility and reduced processing time.

bioRxiv · May 27, 2025 · PreprintNeuroscience

Tissue Optimisation Strategies for High Quality Ex Vivo Diffusion Imaging

Rachel L. C. Barrett, Diana Cash, Camilla Simmons, Eugene Kim, Tobias Wood, Richard Stones, Anthony Christopher Vernon, Marco Catani, Flavio Dell’Acqua

This paper develops an optimization strategy for ex vivo diffusion imaging in rodent brains to improve signal-to-noise ratio and reduce acquisition time.

NMR in Biomedicine · Nov 2, 2022Peer-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