The diffusion-simulated connectivity (DiSCo) dataset
The methodological development in the mapping of the brain structural connectome from diffusion-weighted magnetic resonance imaging (DW-MRI) has raised many hopes in the neuroscientific community. Indeed, the knowledge of the connections between different brain regions is fundamental to study brain...
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Elsevier
2021-10-01
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340921007113 |
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author | Jonathan Rafael-Patino Gabriel Girard Raphaël Truffet Marco Pizzolato Emmanuel Caruyer Jean-Philippe Thiran |
author_facet | Jonathan Rafael-Patino Gabriel Girard Raphaël Truffet Marco Pizzolato Emmanuel Caruyer Jean-Philippe Thiran |
author_sort | Jonathan Rafael-Patino |
collection | DOAJ |
description | The methodological development in the mapping of the brain structural connectome from diffusion-weighted magnetic resonance imaging (DW-MRI) has raised many hopes in the neuroscientific community. Indeed, the knowledge of the connections between different brain regions is fundamental to study brain anatomy and function. The reliability of the structural connectome is therefore of paramount importance. In the search for accuracy, researchers have given particular attention to linking white matter tractography methods – used for estimating the connectome – with information about the microstructure of the nervous tissue. The creation and validation of methods in this context were hampered by a lack of practical numerical phantoms. To achieve this, we created a numerical phantom that mimics complex anatomical fibre pathway trajectories while also accounting for microstructural features such as axonal diameter distribution, myelin presence, and variable packing densities. The substrate has a micrometric resolution and an unprecedented size of 1 cubic millimetre to mimic an image acquisition matrix of 40×40×40 voxels. DW-MRI images were obtained from Monte Carlo simulations of spin dynamics to enable the validation of quantitative tractography. The phantom is composed of 12,196 synthetic tubular fibres with diameters ranging from 1.4 µm to 4.2 µm, interconnecting sixteen regions of interest. The simulated images capture the microscopic properties of the tissue (e.g. fibre diameter, water diffusing within and around fibres, free water compartment), while also having desirable macroscopic properties resembling the anatomy, such as the smoothness of the fibre trajectories. While previous phantoms were used to validate either tractography or microstructure, this phantom can enable a better assessment of the connectome estimation’s reliability on the one side, and its adherence to the actual microstructure of the nervous tissue on the other. |
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spelling | doaj.art-ab2a1e1bcce74867a1fbc65409a1e94f2022-12-21T19:14:20ZengElsevierData in Brief2352-34092021-10-0138107429The diffusion-simulated connectivity (DiSCo) datasetJonathan Rafael-Patino0Gabriel Girard1Raphaël Truffet2Marco Pizzolato3Emmanuel Caruyer4Jean-Philippe Thiran5Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, SwitzerlandCorresponding author at: EPFL STI IEL LTS5, ELD 232 (Bâtiment ELD), Station 11, CH-1015 Lausanne.; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, SwitzerlandUniv Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL Rennes, U-1228, FranceSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, DenmarkUniv Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL Rennes, U-1228, FranceSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, SwitzerlandThe methodological development in the mapping of the brain structural connectome from diffusion-weighted magnetic resonance imaging (DW-MRI) has raised many hopes in the neuroscientific community. Indeed, the knowledge of the connections between different brain regions is fundamental to study brain anatomy and function. The reliability of the structural connectome is therefore of paramount importance. In the search for accuracy, researchers have given particular attention to linking white matter tractography methods – used for estimating the connectome – with information about the microstructure of the nervous tissue. The creation and validation of methods in this context were hampered by a lack of practical numerical phantoms. To achieve this, we created a numerical phantom that mimics complex anatomical fibre pathway trajectories while also accounting for microstructural features such as axonal diameter distribution, myelin presence, and variable packing densities. The substrate has a micrometric resolution and an unprecedented size of 1 cubic millimetre to mimic an image acquisition matrix of 40×40×40 voxels. DW-MRI images were obtained from Monte Carlo simulations of spin dynamics to enable the validation of quantitative tractography. The phantom is composed of 12,196 synthetic tubular fibres with diameters ranging from 1.4 µm to 4.2 µm, interconnecting sixteen regions of interest. The simulated images capture the microscopic properties of the tissue (e.g. fibre diameter, water diffusing within and around fibres, free water compartment), while also having desirable macroscopic properties resembling the anatomy, such as the smoothness of the fibre trajectories. While previous phantoms were used to validate either tractography or microstructure, this phantom can enable a better assessment of the connectome estimation’s reliability on the one side, and its adherence to the actual microstructure of the nervous tissue on the other.http://www.sciencedirect.com/science/article/pii/S2352340921007113Diffusion MRIMonte CarloSimulationNumerical phantomStructural connectivityTractography |
spellingShingle | Jonathan Rafael-Patino Gabriel Girard Raphaël Truffet Marco Pizzolato Emmanuel Caruyer Jean-Philippe Thiran The diffusion-simulated connectivity (DiSCo) dataset Data in Brief Diffusion MRI Monte Carlo Simulation Numerical phantom Structural connectivity Tractography |
title | The diffusion-simulated connectivity (DiSCo) dataset |
title_full | The diffusion-simulated connectivity (DiSCo) dataset |
title_fullStr | The diffusion-simulated connectivity (DiSCo) dataset |
title_full_unstemmed | The diffusion-simulated connectivity (DiSCo) dataset |
title_short | The diffusion-simulated connectivity (DiSCo) dataset |
title_sort | diffusion simulated connectivity disco dataset |
topic | Diffusion MRI Monte Carlo Simulation Numerical phantom Structural connectivity Tractography |
url | http://www.sciencedirect.com/science/article/pii/S2352340921007113 |
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