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Quantifying brain microstructure using MRI

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Mohammadi,  Siawoosh       
Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany;
Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany;
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department of Neuroradiology, University Hospital of Schleswig-Holstein, Kiel, Germany;

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Kirilina,  Evgeniya       
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Edwards,  Luke       
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Mohammadi, S., Callaghan, M. F., Kirilina, E., & Edwards, L. (2024). Quantifying brain microstructure using MRI. In Reference Module in Neuroscience and Biobehavioral Psychology. Elsevier. doi:10.1016/B978-0-12-820480-1.00189-3.


Cite as: https://hdl.handle.net/21.11116/0000-000F-B687-B
Abstract
In this article, we introduce MRI as a method that enables non-invasive mapping of microscopic tissue properties (microstructure) of the human brain. MRI can provide three dimensional volumes of the entire brain with high spatial resolution and flexible contrast between tissue types. When combined with biophysical models, these volumes can be transformed into quantitative maps of microstructural features of the tissue. The article begins by introducing basic MRI contrast mechanisms, and commonly used biophysical models that have been used in neuroscience studies to map myelin content, iron content, and cell and fiber properties. This article also highlights challenges that are commonly encountered when analyzing quantitative and biophysical parameters at the group level, as well as the importance and caveats of validating MRI-based measures against gold standard histology. We conclude the article by explaining why the interpretation of any findings must be made in the context of the underlying model or data acquisition assumptions, the consequent limitations of the data, and with knowledge of their reliability and reproducibility.