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NEUROIMAGING: Edited by Bogdan Draganski

Advances in MRI-based computational neuroanatomy

from morphometry to in-vivo histology

Weiskopf, Nikolausa,b; Mohammadi, Siawoosha,c; Lutti, Antoined; Callaghan, Martina F.a

Author Information
Current Opinion in Neurology: August 2015 - Volume 28 - Issue 4 - p 313-322
doi: 10.1097/WCO.0000000000000222

Abstract

Erratum

Due to an error at the Publisher's office the review article, ‘Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology’ , contained one grammatical error. The penultimate sentence in the legend for Figure 3 should read:

“Simultaneously delineating micro-anatomical and functional areas paves the way for new studies of the relation between brain structure and function.”

Furthermore some of the annotations are missing from the reference list. The annotations and their corresponding reference numbers are detailed below:

3. **Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012 Apr;15(4):528–36.

Comprehensive review discussing morphometric and diffusion-based metrics thought to reflect plasticity with particular focus on the mechanisms and potential aetiology of these plasticity-related changes in MRI metrics at the fundamental cellular and molecular level.

20. **Wharton S, Bowtell R. Fiber orientation-dependent white matter contrast in gradient echo MRI. Proc Natl Acad Sci USA. 2012 Nov 6;109(45):18559–64.

This paper presents a key generative multi-compartment model of the gradient-echo MRI signal incorporating micro-architectural effects of exchange, anisotropy and orientation. Analogies between this model and diffusion modelling offer promising perspectives for the unification of gradient-echo and diffusion data.

33. *Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage. 2005 Aug;27(1):48–58.

One of the first biophysical multi-compartment models for in vivo characterization of water diffusion in the brain white matter.

53. *Calamante F, Tournier J-D, Kurniawan ND, Yang Z, Gyengesi E, Galloway GJ, et al. Super-resolution track-density imaging studies of mouse brain: comparison to histology. Neuroimage. 2012 Jan 2;59(1):286–96.

Demonstrates the feasibility of increasing the resolution beyond that of the acquired diffusion weighted data by incorporating results from whole-brain fibre-tracking.

62. *MacKay, A., Laule, C., Vavasour, I., Bjarnason, T., Kolind, S., & Mädler, B. Insights into brain microstructure from the T2 distribution. Magnetic Resonance Imaging 2006; 24(4), 515–525. doi:10.1016/j.mri.2005.12.037.

A multi-compartment model was used to fit the T2 decay of the MR signal and estimate the myelin water fraction (MWF). The MWF showed high correlation with ex-vivo histology parameters such as the luxol fast blue histological stain for myelin.

63. *Jespersen SN, Bjarkam CR, Nyengaard JR, Chakravarty MM, Hansen B, Vosegaard T, et al. Neurite density from magnetic resonance diffusion measurements at ultrahigh field: comparison with light microscopy and electron microscopy. NeuroImage. 2010 Jan;49(1):205–16.

Comparison of parameters obtained from multi-compartment DWI models of water diffusion in the brain to light microscopy and quantitative electron microscopy.

78. **Stikov N, Perry LM, Mezer A, Rykhlevskaia E, Wandell BA, Pauly JM, et al. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage. 2011 Jan 15;54(2):1112–21.

Study unifying relaxometry and diffusion-based measures and introducing an MR-based g-ratio metric. An important step towards a unifying multivariate biophysical model of MRI contrast.

79. *Draganski B, Ashburner J, Hutton C, Kherif F, Frackowiak RSJ, Helms G, et al. Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage. 2011 Apr 15;55(4):1423–34.

This study presents a normalisation procedure for the accurate treatment of quantitative data thereby enabling group studies.

84. *Dick F, Tierney A, Lutti A, Josephs O, Sereno MI, Weiskopf N. In vivo functional and myeloarchitectonic mapping of human primary auditory areas. Journal of Neuroscience. 2012;32:16095–105.

This study combined high resolution myelin-sensitive qMRI maps with tonotopic mapping to define and characterise the primary auditory areas.

88. *Glasser MF, Van Essen DC. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci. 2011 Aug 10;31(32):11597–616.

Large population study demonstrating the correlation between non quantitative MRI markers and cortical variation in myelination.

Current Opinion in Neurology. 28(5):547, October 2015.

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