Emma Sprooten1, Rafael O'Halloran2, Juliane Dinse3, Won Hee Lee4, Dominik Andreas Moser4, Gaelle Eve Doucet4, Morgan Goodman4, Hannah Krinsky4, Alejandro Paulino4, Alexander Rasgon4, Evan Leibu4, Priti Balchandani2, Matilde Inglese5, Sophia Frangou6. 1. Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboudumc, Nijmegen, the Netherlands. 2. Translational and Molecular Imaging Institute Translational and Molecular Imaging Institute and Brain Imaging Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 3. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. 4. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 5. Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 6. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: sophia.frangou@mssm.edu.
Abstract
BACKGROUND: Intracortical myelin is a key determinant of neuronal synchrony and plasticity that underpin optimal brain function. Magnetic resonance imaging (MRI) facilitates the examination of intracortical myelin but presents with methodological challenges. Here we describe a whole-brain approach for the in vivo investigation of intracortical myelin in the human brain using ultra-high field MRI. METHODS: Twenty-five healthy adults were imaged in a 7 Tesla MRI scanner using diffusion-weighted imaging and a T1-weighted sequence optimized for intracortical myelin contrast. Using an automated pipeline, T1 values were extracted at 20 depth-levels from each of 148 cortical regions. In each cortical region, T1 values were used to infer myelin concentration and to construct a non-linearity index as a measure the spatial distribution of myelin across the cortical ribbon. The relationship of myelin concentration and the non-linearity index with other neuroanatomical properties were investigated. Five patients with multiple sclerosis were also assessed using the same protocol as positive controls. RESULTS: Intracortical T1 values decreased between the outer brain surface and the gray-white matter boundary following a slope that showed a slight leveling between 50% and 75% of cortical depth. Higher-order regions in the prefrontal, cingulate and insular cortices, displayed higher non-linearity indices than sensorimotor regions. Across all regions, there was a positive association between T1 values and non-linearity indices (P < 10-5). Both T1 values (P < 10-5) and non-linearity indices (P < 10-15) were associated with cortical thickness. Higher myelin concentration but only in the deepest cortical levels was associated with increased subcortical fractional anisotropy (P = 0.05). CONCLUSIONS: We demonstrate the usefulness of an automatic, whole-brain method to perform depth-dependent examination of intracortical myelin organization. The extracted metrics, T1 values and the non-linearity index, have characteristic patterns across cortical regions, and are associated with thickness and underlying white matter microstructure.
BACKGROUND: Intracortical myelin is a key determinant of neuronal synchrony and plasticity that underpin optimal brain function. Magnetic resonance imaging (MRI) facilitates the examination of intracortical myelin but presents with methodological challenges. Here we describe a whole-brain approach for the in vivo investigation of intracortical myelin in the human brain using ultra-high field MRI. METHODS: Twenty-five healthy adults were imaged in a 7 Tesla MRI scanner using diffusion-weighted imaging and a T1-weighted sequence optimized for intracortical myelin contrast. Using an automated pipeline, T1 values were extracted at 20 depth-levels from each of 148 cortical regions. In each cortical region, T1 values were used to infer myelin concentration and to construct a non-linearity index as a measure the spatial distribution of myelin across the cortical ribbon. The relationship of myelin concentration and the non-linearity index with other neuroanatomical properties were investigated. Five patients with multiple sclerosis were also assessed using the same protocol as positive controls. RESULTS: Intracortical T1 values decreased between the outer brain surface and the gray-white matter boundary following a slope that showed a slight leveling between 50% and 75% of cortical depth. Higher-order regions in the prefrontal, cingulate and insular cortices, displayed higher non-linearity indices than sensorimotor regions. Across all regions, there was a positive association between T1 values and non-linearity indices (P < 10-5). Both T1 values (P < 10-5) and non-linearity indices (P < 10-15) were associated with cortical thickness. Higher myelin concentration but only in the deepest cortical levels was associated with increased subcortical fractional anisotropy (P = 0.05). CONCLUSIONS: We demonstrate the usefulness of an automatic, whole-brain method to perform depth-dependent examination of intracortical myelin organization. The extracted metrics, T1 values and the non-linearity index, have characteristic patterns across cortical regions, and are associated with thickness and underlying white matter microstructure.
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