Assessing quantitative MRI techniques using multimodal comparisons.
The study of brain structure and change in neuroscience is commonly conducted using macroscopic morphological measures of the brain such as regional volume or cortical thickness, providing little insight into the microstructure and physiology of the brain. In contrast, quantitative Magnetic Resonanc...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327828 |
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| author | Francis Carter Alfred Anwander Mathieu Johnson Thomás Goucha Helyne Adamson Angela D Friederici Antoine Lutti Claudine J Gauthier Nikolaus Weiskopf Pierre-Louis Bazin Christopher J Steele |
| author_facet | Francis Carter Alfred Anwander Mathieu Johnson Thomás Goucha Helyne Adamson Angela D Friederici Antoine Lutti Claudine J Gauthier Nikolaus Weiskopf Pierre-Louis Bazin Christopher J Steele |
| author_sort | Francis Carter |
| collection | DOAJ |
| description | The study of brain structure and change in neuroscience is commonly conducted using macroscopic morphological measures of the brain such as regional volume or cortical thickness, providing little insight into the microstructure and physiology of the brain. In contrast, quantitative Magnetic Resonance Imaging (MRI) allows the monitoring of microscopic brain change non-invasively in-vivo, and provides directly comparable values between tissues, regions, and individuals. To support the development and common use of qMRI for cognitive neuroscience, we analysed a set of qMRI and dMRI metrics (R1, R2*, Magnetization Transfer saturation, Proton Density saturation, Fractional Anisotropy, Mean Diffusivity) in 101 healthy young adults. Here we provide a comprehensive descriptive analysis of these metrics and their linear relationships to each other in grey and white matter to develop a more complete understanding of the relationship to tissue microstructure. Furthermore, we provide evidence that combinations of metrics may uncover informative gradients across the brain by showing that lower variance components of PCA may be used to identify cortical gradients otherwise hidden within individual metrics. We discuss these results within the context of microstructural and physiological neuroscience research. |
| format | Article |
| id | doaj-art-643e656273aa4268a8c732c9cc38e352 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-643e656273aa4268a8c732c9cc38e3522025-08-20T03:22:22ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032782810.1371/journal.pone.0327828Assessing quantitative MRI techniques using multimodal comparisons.Francis CarterAlfred AnwanderMathieu JohnsonThomás GouchaHelyne AdamsonAngela D FriedericiAntoine LuttiClaudine J GauthierNikolaus WeiskopfPierre-Louis BazinChristopher J SteeleThe study of brain structure and change in neuroscience is commonly conducted using macroscopic morphological measures of the brain such as regional volume or cortical thickness, providing little insight into the microstructure and physiology of the brain. In contrast, quantitative Magnetic Resonance Imaging (MRI) allows the monitoring of microscopic brain change non-invasively in-vivo, and provides directly comparable values between tissues, regions, and individuals. To support the development and common use of qMRI for cognitive neuroscience, we analysed a set of qMRI and dMRI metrics (R1, R2*, Magnetization Transfer saturation, Proton Density saturation, Fractional Anisotropy, Mean Diffusivity) in 101 healthy young adults. Here we provide a comprehensive descriptive analysis of these metrics and their linear relationships to each other in grey and white matter to develop a more complete understanding of the relationship to tissue microstructure. Furthermore, we provide evidence that combinations of metrics may uncover informative gradients across the brain by showing that lower variance components of PCA may be used to identify cortical gradients otherwise hidden within individual metrics. We discuss these results within the context of microstructural and physiological neuroscience research.https://doi.org/10.1371/journal.pone.0327828 |
| spellingShingle | Francis Carter Alfred Anwander Mathieu Johnson Thomás Goucha Helyne Adamson Angela D Friederici Antoine Lutti Claudine J Gauthier Nikolaus Weiskopf Pierre-Louis Bazin Christopher J Steele Assessing quantitative MRI techniques using multimodal comparisons. PLoS ONE |
| title | Assessing quantitative MRI techniques using multimodal comparisons. |
| title_full | Assessing quantitative MRI techniques using multimodal comparisons. |
| title_fullStr | Assessing quantitative MRI techniques using multimodal comparisons. |
| title_full_unstemmed | Assessing quantitative MRI techniques using multimodal comparisons. |
| title_short | Assessing quantitative MRI techniques using multimodal comparisons. |
| title_sort | assessing quantitative mri techniques using multimodal comparisons |
| url | https://doi.org/10.1371/journal.pone.0327828 |
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