Abstract
The layer composition of the cerebral cortex represents a unique anatomical fingerprint of brain development, function, connectivity, and pathology. Historically, the cortical layers were investigated solely ex-vivo using histological means, but recent magnetic resonance imaging (MRI) studies suggest that T1 relaxation images can be utilized to separate the layers. Despite technological advancements in the field of high-resolution MRI, accurate estimation of whole-brain cortical laminar composition has remained limited due to partial volume effects, leaving some layers far beyond the image resolution. In this study, we offer a simple and accurate method for cortical laminar composition analysis, resolving partial volume effects and cortical curvature heterogeneity. We use a low-resolution 3T MRI echo planar imaging inversion recovery (EPI IR) scan protocol that provides fast acquisition (~ 12 min) and enables extraction of multiple T1 relaxation time components per voxel, which are assigned to types of brain tissue and utilized to extract the subvoxel composition of six T1 layers. While previous investigation of the layers required the estimation of cortical normals or smoothing of layer widths (similar to VBM), here we developed a sphere-based approach to explore the inner mesoscale architecture of the cortex. Our novel algorithm conducts spatial analysis using volumetric sampling of a system of virtual spheres dispersed throughout the entire cortical space. The methodology offers a robust and powerful framework for quantification and visualization of the cortical laminar structure on the cortical surface, providing a basis for quantitative investigation of its role in cognition, physiology and pathology.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Annese J, Pitiot A, Dinov ID, Toga AW (2004) A myelo-architectonic method for the structural classification of cortical areas. NeuroImage 21:15–26. https://doi.org/10.1016/j.neuroimage.2003.08.024
Ballester MAG, Zisserman AP, Brady M (2002) Estimation of the partial volume effect in MRI. Med Image Anal 6:389–405. https://doi.org/10.1016/S1361-8415(02)00061-0
Barazany D, Assaf Y (2012) Visualization of cortical lamination patterns with magnetic resonance imaging. Cereb Cortex 22:2016–2023. https://doi.org/10.1093/cercor/bhr277
Barbier EL, Marrett S, Danek A, Vortmeyer A, van Gelderen P, Duyn J, Bandettini P, Grafman J, Koretsky AP (2002) Imaging cortical anatomy by high-resolution MR at 3.0T: detection of the stripe of gennari in visual area 17. Magn Reson Med 48:735–738. https://doi.org/10.1002/mrm.10255
Beul SF, Hilgetag CC (2014) Towards a “canonical” agranular cortical microcircuit. Front Neuroanat 8:165. https://doi.org/10.3389/fnana.2014.00165
Bridge H, Clare S (2006) High-resolution MRI: in vivo histology? Philos Trans R Soc 361:137–146. https://doi.org/10.1098/rstb.2005.1777
Clare S, Bridge H (2005) Methodological issues relating to in vivo cortical myelography using MRI. Hum Brain Mapp 26:240–250. https://doi.org/10.1002/hbm.20162
Clark VP, Courchesne E, Grafe M (1992) In vivo myeloarchitectonic analysis of human striate and extrastriate cortex using magnetic resonance imaging. Cereb Cortex 2:417–424. https://doi.org/10.1093/cercor/2.5.417
Deistung A, Schafer A, Schweser F, Biedermann U, Turner R, Reichenback JR (2013) Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength. NeuroImage 65:299–314. https://doi.org/10.1016/j.neuroimage.2012.09.055
Dinse J, Hartwich N, Waehnert MD, Tardif CL, Schafer A, Geyer S, Preim B, Turner R, Bazin PL (2015) A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI. NeuroImage 114:71–87. https://doi.org/10.1016/j.neuroimage.2015.04.023
Duyn JH, van Gelderen P, Li TQ, de Zwart JA, Koretsky AP, Fukunaga M (2007) High-field MRI of brain cortical substructure based on signal phase. PNAS 104:28:11796–11801. https://doi.org/10.1073/pnas.0610821104
Fischl B (2012) FreeSurfer. NeuroImage 62:774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Fischl B, Dale AM (2010) Measuring the thickness of the human cerebral cortex from magnetic resonance images. PNAS 97:20:11050–11055. https://doi.org/10.1073/pnas.200033797
Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11–22. https://doi.org/10.1093/cercor/bhg087
Fracasso A, van Veluw SJ, Visser F, Luijten PR, Spliet W, Zwanenburg JJM, Dumoulin SO, Petridou N (2016) Lines of Baillarger in vivo and ex vivo: Myelin contrast across lamina at 7T MRI and histology. NeuroImage 133:163–175. https://doi.org/10.1016/j.neuroimage.2016.02.072
Garey LJ (2006) Brodmann’s localization in the cerebral cortex. Springer: Berlin 1–58. https://doi.org/10.1142/p151
Geyer S, Weiss M, Reimann K, Lohmann G, Turner R (2011) Microstructural parcellation of the human cerebral cortex—from brodmann’s post-mortem map to in vivo mapping with high-field magnetic resonance imaging. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2011.00019
Glasser MF, Goyal MS, Preuss TM, Raichle ME, Van Essen DC (2014) Trends and properties of human cerebral cortex: correlations with cortical myelin content. NeuroImage 93:165–175. https://doi.org/10.1016/j.neuroimage.2013.03.060
Kiernan J, Rajakumar N (2014) Barr’s the human nervous system—an anatomical viewpoint. Lippincott Williams & Wilkins, Philadelphia, USA, pp 213–246
Lifshits S, Tomer O, Shamir I, Barazany D, Tsarfaty G, Rosset S, Assaf Y (2018) Resolution considerations in imaging of the cortical layers. NeuroImage 164:112–120. https://doi.org/10.1016/j.neuroimage.2017.02.086
Lutti A, Dick F, Sereno MI, Weiskopf N (2014) Using high-resolution quantitative mapping of R1 as an index of cortical myelination. NeuroImage 93:176–188. https://doi.org/10.1016/j.neuroimage.2013.06.005
MacDonald D, Kabani N, Avis D, Evans AC (2000) Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage 12:340–356. https://doi.org/10.1006/nimg.1999.0534
Peel D, McLachlan GJ (2000) Robust mixture modelling using the t distribution. Stat Comput 10(4):339–348. https://doi.org/10.1023/A:1008981510081
Scholtens LH, de Reus MA, van den Heuvel MP (2015) Linking contemporary high resolution magnetic resonance imaging to the von economo legacy: a study on the comparison of MRI cortical thickness and histological measurements of cortical structure. Hum Brain Mapp 36:3038–3046. https://doi.org/10.1002/hbm.22826
Scholtens LH, de Reus MA, de Lange SC, Schmidt R, van den Heuvel MP (2016) An MRI von economo—koskinas atlas. NeuroImage 170:249–256. https://doi.org/10.1016/j.neuroimage.2016.12.069
Shafee R, Buckner RL, Fischl B (2015) Gray matter myelination of 1555 human brains using partial volume corrected MRI images. NeuroImage 105:473–485. https://doi.org/10.1016/j.neuroimage.2014.10.054
Tassi L, Colombo N, Garbelli R, Francione S, Lo Russo G, Mai R, Cardinale F, Cossu M, Ferrario A, Galli C, Bramerio M, Citterio A, Spreafico R (2002) Focal cortical dysplasia: neuropathological subtypes, EEG, neuroimaging and surgical outcome. Brain 125:1719–1732. https://doi.org/10.1093/brain/awf175
Tomer O, Baratz Z, Shamir I, Kaptzon D, Horowitz A, Faraggi M, Barazany D, Assaf Y (2018) A probabilistic method for modelling cortical layer composition in sub-voxel resolution. Poster session presented at the Organization for Human Brain Mapping Association, Singapore. https://ww5.aievolution.com/hbm1801/index.cfm?do=ev.viewEv&ev=1317. Accessed 20 June 2018
Turner R, Oros-Peusquens AM, Romanzetti S, Zilles K, Shah NJ (2008) Optimised in vivo visualisation of cortical structures in the human brain at 3 T using IR-TSE. Magn Reson Imaging 26:935–942. https://doi.org/10.1016/j.mri.2008.01.043
Von Bonin G (1950) Essay on the cerebral cortex. Oxford: C. C. Thomas. https://doi.org/10.1001/jama.1950.02920110084053
Waehnert MD, Dinse J, Weiss M, Streicher MN, Waehnert P, Geyer S, Turner R, Bazin PL (2014) Anatomically motivated modeling of cortical laminae. NeuroImage 93:210–220. https://doi.org/10.1016/j.neuroimage.2013.03.078
Waehnert MD, Dinse J, Schafer A, Geyer S, Bazin PL, Turner R, Tardif CL (2016) A subject-specific framework for in vivo myeloarchitectonic analysis using high resolution quantitative MRI. NeuroImage 125:94–107. https://doi.org/10.1016/j.neuroimage.2015.10.001
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shamir, I., Tomer, O., Baratz, Z. et al. A framework for cortical laminar composition analysis using low-resolution T1 MRI images. Brain Struct Funct 224, 1457–1467 (2019). https://doi.org/10.1007/s00429-019-01848-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00429-019-01848-2