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BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis

Multimodal neuroimaging studies have recently become a trend in the neuroimaging field and are certainly a standard for the future. Brain connectivity studies combining functional activation patterns using resting-state or task-related functional magnetic resonance imaging (fMRI) and diffusion tenso...

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Autores principales: Marques, Paulo, Soares, José M., Alves, Victor, Sousa, Nuno
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836207/
https://www.ncbi.nlm.nih.gov/pubmed/24319419
http://dx.doi.org/10.3389/fnhum.2013.00794
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author Marques, Paulo
Soares, José M.
Alves, Victor
Sousa, Nuno
author_facet Marques, Paulo
Soares, José M.
Alves, Victor
Sousa, Nuno
author_sort Marques, Paulo
collection PubMed
description Multimodal neuroimaging studies have recently become a trend in the neuroimaging field and are certainly a standard for the future. Brain connectivity studies combining functional activation patterns using resting-state or task-related functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) tractography have growing popularity. However, there is a scarcity of solutions to perform optimized, intuitive, and consistent multimodal fMRI/DTI studies. Here we propose a new tool, brain connectivity analysis tool (BrainCAT), for an automated and standard multimodal analysis of combined fMRI/DTI data, using freely available tools. With a friendly graphical user interface, BrainCAT aims to make data processing easier and faster, implementing a fully automated data processing pipeline and minimizing the need for user intervention, which hopefully will expand the use of combined fMRI/DTI studies. Its validity was tested in an aging study of the default mode network (DMN) white matter connectivity. The results evidenced the cingulum bundle as the structural connector of the precuneus/posterior cingulate cortex and the medial frontal cortex, regions of the DMN. Moreover, mean fractional anisotropy (FA) values along the cingulum extracted with BrainCAT showed a strong correlation with FA values from the manual selection of the same bundle. Taken together, these results provide evidence that BrainCAT is suitable for these analyses.
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spelling pubmed-38362072013-12-06 BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis Marques, Paulo Soares, José M. Alves, Victor Sousa, Nuno Front Hum Neurosci Neuroscience Multimodal neuroimaging studies have recently become a trend in the neuroimaging field and are certainly a standard for the future. Brain connectivity studies combining functional activation patterns using resting-state or task-related functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) tractography have growing popularity. However, there is a scarcity of solutions to perform optimized, intuitive, and consistent multimodal fMRI/DTI studies. Here we propose a new tool, brain connectivity analysis tool (BrainCAT), for an automated and standard multimodal analysis of combined fMRI/DTI data, using freely available tools. With a friendly graphical user interface, BrainCAT aims to make data processing easier and faster, implementing a fully automated data processing pipeline and minimizing the need for user intervention, which hopefully will expand the use of combined fMRI/DTI studies. Its validity was tested in an aging study of the default mode network (DMN) white matter connectivity. The results evidenced the cingulum bundle as the structural connector of the precuneus/posterior cingulate cortex and the medial frontal cortex, regions of the DMN. Moreover, mean fractional anisotropy (FA) values along the cingulum extracted with BrainCAT showed a strong correlation with FA values from the manual selection of the same bundle. Taken together, these results provide evidence that BrainCAT is suitable for these analyses. Frontiers Media S.A. 2013-11-21 /pmc/articles/PMC3836207/ /pubmed/24319419 http://dx.doi.org/10.3389/fnhum.2013.00794 Text en Copyright © 2013 Marques, Soares, Alves and Sousa. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Marques, Paulo
Soares, José M.
Alves, Victor
Sousa, Nuno
BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis
title BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis
title_full BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis
title_fullStr BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis
title_full_unstemmed BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis
title_short BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis
title_sort braincat - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836207/
https://www.ncbi.nlm.nih.gov/pubmed/24319419
http://dx.doi.org/10.3389/fnhum.2013.00794
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