Abstract
We describe a Connectivity Analysis TOolbox (CATO) for the reconstruction of structural and functional brain connectivity based on diffusion weighted imaging and resting-state functional MRI data. CATO is a multimodal software package that enables researchers to run end-to-end reconstructions from MRI data to structural and functional connectome maps, customize their analyses and utilize various software packages to preprocess data. Structural and functional connectome maps can be reconstructed with respect to user-defined (sub)cortical atlases providing aligned connectivity matrices for integrative multimodal analyses. We outline the implementation and usage of the structural and functional processing pipelines in CATO. Performance was calibrated with respect to simulated diffusion weighted imaging data from the ITC2015 challenge and test-retest diffusion weighted imaging data and resting-state functional MRI data from the Human Connectome Project. CATO is open-source software distributed under the MIT License and available as a MATLAB toolbox and as a stand-alone application at www.dutchconnectomelab.nl/CATO.
Original language | English |
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Article number | 120108 |
Pages (from-to) | 1-12 |
Number of pages | 12 |
Journal | NeuroImage |
Volume | 273 |
Early online date | 12 Apr 2023 |
DOIs | |
Publication status | Published - Jun 2023 |
Bibliographical note
Funding Information:This project has received funding from the Amsterdam Neuroscience alliance grant [to SCdL], from the ZonMw Open Competition, project REMOVE 09120011910032, from the Netherlands Organization for Scientific Research (NWO), VIDI 452-16-015 [to MPvdH], from the European Research Council (ERC), Advanced Grant 101055383 OVERNIGHT, and from the ERC under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. ERC CONNECT 101001062) [to MPvdH].
Funding Information:
This project has received funding from the Amsterdam Neuroscience alliance grant [to SCdL], from the ZonMw Open Competition, project REMOVE 09120011910032, from the Netherlands Organization for Scientific Research (NWO), VIDI 452-16-015 [to MPvdH], from the European Research Council (ERC), Advanced Grant 101055383 OVERNIGHT, and from the ERC under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. ERC CONNECT 101001062) [to MPvdH].
Publisher Copyright:
© 2023
Funding
This project has received funding from the Amsterdam Neuroscience alliance grant [to SCdL], from the ZonMw Open Competition, project REMOVE 09120011910032, from the Netherlands Organization for Scientific Research (NWO), VIDI 452-16-015 [to MPvdH], from the European Research Council (ERC), Advanced Grant 101055383 OVERNIGHT, and from the ERC under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. ERC CONNECT 101001062) [to MPvdH]. This project has received funding from the Amsterdam Neuroscience alliance grant [to SCdL], from the ZonMw Open Competition, project REMOVE 09120011910032, from the Netherlands Organization for Scientific Research (NWO), VIDI 452-16-015 [to MPvdH], from the European Research Council (ERC), Advanced Grant 101055383 OVERNIGHT, and from the ERC under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. ERC CONNECT 101001062) [to MPvdH].
Funders | Funder number |
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ZonMw Open Competition | REMOVE 09120011910032 |
Horizon 2020 Framework Programme | |
European Research Council | 101055383 |
European Research Council | |
Nederlandse Organisatie voor Wetenschappelijk Onderzoek | VIDI 452-16-015 |
Nederlandse Organisatie voor Wetenschappelijk Onderzoek | |
Horizon 2020 | 101001062 |
Horizon 2020 |