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• W3201699274 startingPage "118559" @default.
• W3201699274 abstract "The human lateral geniculate nucleus (LGN) of the visual thalamus is a key subcortical processing site for visual information analysis. Due to its small size and deep location within the brain, a non-invasive characterization of the LGN and its microstructurally distinct magnocellular (M) and parvocellular (P) subdivisions in humans is challenging. Here, we investigated whether structural quantitative MRI (qMRI) methods that are sensitive to underlying microstructural tissue features enable MR-based mapping of human LGN M and P subdivisions. We employed high-resolution 7 Tesla in-vivo qMRI in N = 27 participants and ultra-high resolution 7 Tesla qMRI of a post-mortem human LGN specimen. We found that a quantitative assessment of the LGN and its subdivisions is possible based on microstructure-informed qMRI contrast alone. In both the in-vivo and post-mortem qMRI data, we identified two components of shorter and longer longitudinal relaxation time (T1) within the LGN that coincided with the known anatomical locations of a dorsal P and a ventral M subdivision, respectively. Through ground-truth histological validation, we further showed that the microstructural MRI contrast within the LGN pertains to cyto- and myeloarchitectonic tissue differences between its subdivisions. These differences were based on cell and myelin density, but not on iron content. Our qMRI-based mapping strategy paves the way for an in-depth understanding of LGN function and microstructure in humans. It further enables investigations into the selective contributions of LGN subdivisions to human behavior in health and disease." @default.
• W3201699274 created "2021-10-11" @default.
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• W3201699274 date "2021-12-01" @default.
• W3201699274 modified "2023-10-16" @default.
• W3201699274 title "Mapping the human lateral geniculate nucleus and its cytoarchitectonic subdivisions using quantitative MRI" @default.
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• W3201699274 doi "https://doi.org/10.1016/j.neuroimage.2021.118559" @default.
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