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Using high-resolution quantitative mapping of R1 as an index of cortical myelination
Abstract
A fundamental tenet of neuroscience is that cortical functional differentiation is related to the cross-areal differences in cyto-, receptor-, and myeloarchitectonics that are observed in ex-vivo preparations. An ongoing challenge is to create noninvasive magnetic resonance (MR) imaging techniques that offer sufficient resolution, tissue contrast, accuracy and precision to allow for characterization of cortical architecture over an entire living human brain. One exciting development is the advent of fast, high-resolution quantitative mapping of basic MR parameters that reflect cortical myeloarchitecture. Here, we outline some of the theoretical and technical advances underlying this technique, particularly in terms of measuring and correcting for transmit and receive radio frequency field inhomogeneities. We also discuss new directions in analytic techniques, including higher resolution reconstructions of the cortical surface. We then discuss two recent applications of this technique. The first compares individual and group myelin maps to functional retinotopic maps in the same individuals, demonstrating a close relationship between functionally and myeloarchitectonically defined areal boundaries (as well as revealing an interesting disparity in a highly studied visual area). The second combines tonotopic and myeloarchitectonic mapping to localize primary auditory areas in individual healthy adults, using a similar strategy as combined electrophysiological and post-mortem myeloarchitectonic studies in non-human primates.
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“many investigators consider R1 (1/T1) to be the best MR measure of myelin content (Stüber et al., 2014; Lutti et al., 2013).”
From Multi-modal characterization of rapid anterior hippocampal volume increase associated with aerobic exercise (2016) · cites this paper as Lutti et al., 2013
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Cites
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Self-citationA new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data
Eickhoff, S. B., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., & Zilles, K. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.12.034 · Cited by 4,033
Sequence-independent segmentation of magnetic resonance images
Fischl, B. R., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.016 · Cited by 2,117
MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field
Marques, J. P., Kober, T., Krüger, G., van der Zwaag, W., de Moortele, P. V., & Rolf Gruetter, A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.002 · Cited by 1,463
Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI
Glasser, M. F., & Van Essen, D. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2180-11.2011 · Cited by 1,447
Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE)
Mugler, J. P., & Brookeman, J. R. (1990) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910150117 · Cited by 1,276
Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen
Economo, C. V., & Koskinas, G. N. (1925) · Springer eBooks · Cited by 1,234
Visual field maps in human cortex.
Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.012 · Cited by 1,231 · cited 1×
“This has been an extremely productive approach, and has shown that much of the cortex is tiled with maps (Graziano and Aflalo, 2007; Schreiner and Winer, 2007; Wandell et al., 2007).”
FLASH imaging: Rapid NMR imaging using low flip-angle pulses
Haase, A., Frahm, J., Matthaei, D., Hänicke, W., & Merboldt, K. D. (2011) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2011.09.021 · Cited by 1,153
Yarnykh, V. L. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21120 · Cited by 1,028
Research works printed but not linked: 3
- Freeman, 1998, Optimisation of the ultra-fast Look–Locker echo-planar imaging T1 mapping sequence, Magn. Reson. Med., 16, 765
- Hopf, 1955, Über die Verteilung myeloarchitektonischer Merkmale in der isokortikalen Schläfenlappenrinde beim Menschen, J. Hirnforsch., 46–54
- Vogt, 1906, Der Wert der myelogenetischen Felder der Großhirnrinde, Anat. Anz., 24, 273
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Silva, R. F., Plis, S., Jing Sui (Beijing Normal University), my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pattichis, M. S., Adalı, T., & Calhoun, V. D. (2016) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2016.2594945 · Cited by 22
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Hertanu, A., Soustelle, L., Buron, J., Priellec, J. L., Cayre, M., Troter, A. L., Varma, G., Alsop, D. C., Durbec, P., Girard, O. M., et al. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29140 · Cited by 22
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Olsson, H., Andersen, M., Lätt, J., Wirestam, R., & Helms, G. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28206 · Cited by 19
Tzourio‐Mazoyer, N., Maingault, S., Panzieri, J., Pepe, A., Crivello, F., & Mazoyer, B. M. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy088 · Cited by 18
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Monsivais, H., Yeh, C., Edmondson, D. A., Harold, R., Snyder, S., Wells, E. M., Schmidt‐Wilcke, T., Foti, D., Zauber, S. E., & Dydak, U. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120523 · Cited by 18
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Ding, Z., Hu, S., Su, T., Choi, J. Y., Morris, S., Wang, X. F., Sakaie, K., Murakami, H., Huppertz, H., Blümcke, I., et al. (2024) · Epilepsia · doi:10.1111/epi.17951 · Cited by 17
Nord, C. L., Kim, S., Callesen, M. B., Kvamme, T. L., Jensen, M., Pedersen, M. U., Thomsen, K. R., & Voon, V. (2019) · Neuropsychopharmacology · doi:10.1038/s41386-019-0343-6 · Cited by 17
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Zhou, I. Y., Guo, Y., Igarashi, T., Wang, Y., Mandeville, E. T., Chan, S., Wen, L., Vangel, M. G., Lo, E. H., Ji, X., et al. (2016) · NMR in Biomedicine · doi:10.1002/nbm.3617 · Cited by 15
Welsink‐Karssies, M. M., Schrantee, A., Caan, M. W. A., Hollak, C. E. M., Janssen, M. C. H., Oussoren, E., de Vries, M. C., Roosendaal, S. D., Engelen, M., & Bosch, A. M. (2020) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2020.11.001 · Cited by 15
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Lazari, A., Salvan, P., Cottaar, M., Papp, D., van der Werf, O. J., Johnstone, A., Sanders, Z., Sampaio‐Baptista, C., Eichert, N., Miyamoto, K., et al. (2021) · Cortex · doi:10.1016/j.cortex.2021.08.017 · Cited by 15
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Fritz, F. J., Poser, B. A., & Roebroeck, A. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118285 · Cited by 15
Hannan, F., Hamilton, J., Patriquin, C. J., Pavenski, K., Jurkiewicz, M. T., Tristao, L., Owen, A. M., Kosalka, P. K., Deoni, S., Théberge, J., et al. (2023) · British Journal of Haematology · doi:10.1111/bjh.19246 · Cited by 13
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Self-citation
Metadata
- Authors
- Lutti, A., Dick, F., Sereno, M. I., & Weiskopf, N.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- University of London (GB)
- Corresponding author
- Lutti, Antoine
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 93
- Pages
- 176-188
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1991534565
- Cited by
- 346
- References
- 139
- Citations by year
- 2026: 20 · 2025: 28 · 2024: 18 · 2023: 26 · 2022: 37 · 2021: 29 · 2020: 27 · 2019: 27 · 2018: 36 · 2017: 38 · 2016: 28 · 2015: 17 · 2014: 11
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- BIROn (Birkbeck, University of London) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- IRIS (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- Wellcome Trust (091593)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- cortical myelination, myeloarchitecture, quantitative MRI, R1 mapping, cortical surface reconstruction, retinotopic map, tonotopic map, functional differentiation, non-invasive brain imaging
- MeSH terms
- Brain Mapping, Cerebral Cortex, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Myelin Sheath
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