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DCP: A pipeline toolbox for diffusion connectome.
Huang, Weijie; Dong, Xinyi; Zhao, Tengda; Kucikova, Ludmila; Fu, Anguo; Shu, Ni.
Afiliación
  • Huang W; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, PR China.
  • Dong X; School of Systems Science, Beijing Normal University, Beijing, PR China.
  • Zhao T; Department of Neuroscience, Sheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK.
  • Kucikova L; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, PR China.
  • Fu A; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, PR China.
  • Shu N; Department of Neuroscience, Sheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK.
Hum Brain Mapp ; 45(3): e26626, 2024 Feb 15.
Article en En | MEDLINE | ID: mdl-38375916
ABSTRACT
The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Conectoma / Sustancia Blanca Límite: Humans Idioma: En Revista: Hum Brain Mapp Asunto de la revista: CEREBRO Año: 2024 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Conectoma / Sustancia Blanca Límite: Humans Idioma: En Revista: Hum Brain Mapp Asunto de la revista: CEREBRO Año: 2024 Tipo del documento: Article