Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox

Siemon C. de Lange*, Koen Helwegen, Martijn P. van den Heuvel

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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 languageEnglish
Article number120108
Pages (from-to)1-12
Number of pages12
JournalNeuroImage
Volume273
Early online date12 Apr 2023
DOIs
Publication statusPublished - 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].

FundersFunder number
ZonMw Open CompetitionREMOVE 09120011910032
Horizon 2020 Framework Programme
European Research Council101055383
European Research Council
Nederlandse Organisatie voor Wetenschappelijk OnderzoekVIDI 452-16-015
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Horizon 2020101001062
Horizon 2020

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