Abstract
There is a striking relationship between cognitive performance, such as executive function, attention and motor processing, and grey matter (GM) surface- and voxel-based morphometric measures, as derived from magnetic resonance imaging (MRI). In addition, loss of GM has shown to be a reliable index of atrophy in neurological diseases, including multiple sclerosis (MS). Therefore, morphometric measures are highly valuable tools to noninvasively study brain pathology.
Among different MRI morphometric measures, cortical thinning has gained great importance for characterizing neurodegeneration in MS . While measures of dendrite density and myelin content assess further processes of GM pathology . Hence, MRI is a unique and versatile, noninvasive method for computer-aided lesion detection and brain-wide evaluation of the pathogenic neurodegenerative process in MS .
This chapter provides an overview of quantitative image analysis methods used to investigate GM pathology in MS and how to derive them, while describing the potential of inferring microstructural changes based on the microscopic and mesoscopic measurements obtained from MRI acquisitions.
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References
Arshad M, Stanley JA, Raz N (2017) Test-retest reliability and concurrent validity of in vivo myelin content indices: myelin water fraction and calibrated T1 w/T2 w image ratio. Hum Brain Mapp 38:1780–1790
Ashburner J (2009) Computational anatomy with the SPM software. Magn Reson Imaging 27:1163–1174
Ashburner J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38:95–113
Ashburner J, Friston KJ (2000) Voxel-based morphometry--the methods. NeuroImage 11:805–821
Avants BB, Tustison NJ, Song G et al (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54:2033–2044
Baecher-Allan C, Kaskow BJ, Weiner HL (2018) Multiple sclerosis: mechanisms and immunotherapy. Neuron 97:742–768
Bagnato F, Franco G, Ye F et al (2019) Selective inversion recovery quantitative magnetization transfer imaging: toward a 3 T clinical application in multiple sclerosis. Mult Scler 26(4):457–467
Barkhof F, Calabresi PA, Miller DH et al (2009) Imaging outcomes for neuroprotection and repair in multiple sclerosis trials. Nat Rev Neurol 5:256–266
Basser PJ, Mattiello J, Lebihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103:247–254
Basser PJ, Mattiello J, Lebihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66:259–267
Calabrese M, Agosta F, Rinaldi F et al (2009) Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol 66:1144–1150
Calabrese M, Magliozzi R, Ciccarelli O et al (2015) Exploring the origins of grey matter damage in multiple sclerosis. Nat Rev Neurosci 16:147–158
Calabrese M, Rinaldi F, Seppi D et al (2011) Cortical diffusion-tensor imaging abnormalities in multiple sclerosis: a 3-year longitudinal study. Radiology 261:891–898
Calabrese M, Romualdi C, Poretto V et al (2013) The changing clinical course of multiple sclerosis: a matter of gray matter. Ann Neurol 74:76–83
Cavallari M, Ceccarelli A, Wang GY et al (2014) Microstructural changes in the striatum and their impact on motor and neuropsychological performance in patients with multiple sclerosis. PLoS One 9:e101199
Cerqueira JJ, Compston DS, Geraldes R et al (2018) Time matters in multiple sclerosis: can early treatment and long-term follow-up ensure everyone benefits from the latest advances in multiple sclerosis? J Neurol Neurosurg Psychiatry 89:844–850
Chard DT, Griffin CM, Rashid W et al (2004) Progressive grey matter atrophy in clinically early relapsing-remitting multiple sclerosis. Mult Scler 10:387–391
Ciccarelli O, Werring D, Wheeler-Kingshott C et al (2001) Investigation of MS normal-appearing brain using diffusion tensor MRI with clinical correlations. Neurology 56:926–933
Collins DL, Neelin P, Peters TM et al (1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192–205
Daducci A, Canales-Rodriguez EJ, Zhang H et al (2015) Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. NeuroImage 105:32–44
Dahnke R, Yotter RA, Gaser C (2013) Cortical thickness and central surface estimation. NeuroImage 65:336–348
Dalton CM, Chard DT, Davies GR et al (2004) Early development of multiple sclerosis is associated with progressive grey matter atrophy in patients presenting with clinically isolated syndromes. Brain 127:1101–1107
De Stefano N, Giorgio A, Battaglini M et al (2010) Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology 74:1868–1876
De Stefano N, Matthews PM, Filippi M et al (2003) Evidence of early cortical atrophy in MS: relevance to white matter changes and disability. Neurology 60:1157–1162
Derakhshan M, Caramanos Z, Narayanan S et al (2014) Surface-based analysis reveals regions of reduced cortical magnetization transfer ratio in patients with multiple sclerosis: a proposed method for imaging subpial demyelination. Hum Brain Mapp 35:3402–3413
Dinkler M (1904) Zur Kasuistik der multiplen Herdsklerose des Gehirns und Ruckenmarks. Deuts Zeits f Nervenheilk 26:233–247
Dutta R, Chen J, Ohno N et al (2017) Axonal loss and Neurodegeneration in multiple sclerosis. Neurodegeneration 238–247
Duval T, Stikov N, Cohen-Adad J (2016) Modeling white matter microstructure. Funct Neurol 31:217–228
Ellwardt E, Pramanik G, Luchtman D et al (2018) Maladaptive cortical hyperactivity upon recovery from experimental autoimmune encephalomyelitis. Nat Neurosci 21:1392
Enzinger C, Barkhof F, Ciccarelli O et al (2015) Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nat Rev Neurol 11:676–686
Eshaghi A, Prados F, Brownlee WJ et al (2018) Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol 83:210–222
Filippi M, Bar-Or A, Piehl F et al (2018) Multiple sclerosis. Nat Rev Dis Primers 4:43
Fischl B (2012) FreeSurfer. NeuroImage 62:774–781
Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:11050–11055
Fleischer V, Radetz A, Ciolac D et al (2019) Graph theoretical framework of brain networks in multiple sclerosis: a review of concepts. Neuroscience 403:35–53
Focke NK, Trost S, Paulus W et al (2014) Do manual and voxel-based morphometry measure the same? A proof of concept study. Front Psych 5:39
Friese MA (2016) Widespread synaptic loss in multiple sclerosis. Brain 139:2–4
Fukutomi H, Glasser MF, Murata K et al (2019) Diffusion tensor model links to neurite orientation dispersion and density imaging at high b-value in cerebral cortical gray matter. Sci Rep 9:12246
Fukutomi H, Glasser MF, Zhang H et al (2018) Neurite imaging reveals microstructural variations in human cerebral cortical gray matter. NeuroImage 182:488–499
Glasser MF, Van Essen DC (2011) Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci 31:11597–11616
Gonzalez-Escamilla G, Ciolac D, De Santis S, et al (2020) Gray matter network reorganization in multiple sclerosis from 7-Tesla and 3-Tesla MRI data. Ann Clin Transl Neurol 7:543–553
Good CD, Johnsrude I, Ashburner J et al (2001) Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. NeuroImage 14:685–700
Grussu F, Schneider T, Tur C et al (2017) Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology? Ann Clin Transl Neurol 4:663–679
Hannoun S, Durand-Dubief F, Confavreux C et al (2012) Diffusion tensor-MRI evidence for extra-axonal neuronal degeneration in caudate and thalamic nuclei of patients with multiple sclerosis. AJNR Am J Neuroradiol 33:1363–1368
Hasan KM, Halphen C, Kamali A et al (2009) Caudate nuclei volume, diffusion tensor metrics, and T(2) relaxation in healthy adults and relapsing-remitting multiple sclerosis patients: implications for understanding gray matter degeneration. J Magn Reson Imaging 29:70–77
Heidker RM, Emerson MR, Levine SM (2017) Metabolic pathways as possible therapeutic targets for progressive multiple sclerosis. Neural Regen Res 12:1262–1267
Helms G (2015) Tissue properties from quantitative MRI. In: Toga AW (ed) Brain mapping: an encyclopedic reference, vol 1. Elsevier, San Diego, CA, pp 287–294
Henstridge CM, Tzioras M, Paolicelli RC (2019) Glial contribution to excitatory and inhibitory synapse loss in Neurodegeneration. Front Cell Neurosci 13:63
Jenkinson M, Beckmann CF, Behrens TE et al (2012) FSL. NeuroImage 62:782–790
Jiang H, Van Zijl PC, Kim J et al (2006) DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Prog Biomed 81:106–116
Jurgens T, Jafari M, Kreutzfeldt M et al (2016) Reconstruction of single cortical projection neurons reveals primary spine loss in multiple sclerosis. Brain 139:39–46
Kim JS, Singh V, Lee JK et al (2005) Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage 27:210–221
Krämer J, Brück W, Zipp F et al (2019) Imaging in mice and men: pathophysiological insights into multiple sclerosis from conventional and advanced MRI techniques. Prog Neurobiol 182:101663
Ksiazek-Winiarek DJ, Szpakowski P, Glabinski A (2015) Neural plasticity in multiple sclerosis: the functional and molecular background. Neural Plast 2015:307175
Leemans A, Jeurissen B, Sijbers J et al (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proc Intl Soc Mag Reson Med 17:3537
Lerch JP, Evans AC (2005) Cortical thickness analysis examined through power analysis and a population simulation. NeuroImage 24:163–173
Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3D surface construction algorithm. SIGGRAPH Comput Graph 21:163–169
Mckeithan LJ, Lyttle BD, Box BA et al (2019) 7T quantitative magnetization transfer (qMT) of cortical gray matter in multiple sclerosis correlates with cognitive impairment. NeuroImage 203:116190
Mollink J, Kleinnijenhuis M, Cappellen Van Walsum AV et al (2017) Evaluating fibre orientation dispersion in white matter: comparison of diffusion MRI, histology and polarized light imaging. NeuroImage 157:561–574
Nakamura K, Chen JT, Ontaneda D et al (2017) T1-/T2-weighted ratio differs in demyelinated cortex in multiple sclerosis. Ann Neurol 82:635–639
Nelson F, Datta S, Garcia N et al (2011) Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis. Mult Scler 17:1122–1129
Pierpaoli C, Basser PJ (1996) Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 36:893–906
Radetz A, Koirala N, Kraemer J et al (2020) Gray matter integrity predicts white matter network reorganization in multiple sclerosis. Hum Brain Mapp 41:917–927
Radua J, Canales-Rodriguez EJ, Pomarol-Clotet E et al (2014) Validity of modulation and optimal settings for advanced voxel-based morphometry. NeuroImage 86:81–90
Righart R, Biberacher V, Jonkman LE et al (2017) Cortical pathology in multiple sclerosis detected by the T1/T2-weighted ratio from routine magnetic resonance imaging. Ann Neurol 82:519–529
Rocca MA, Sormani MP, Rovaris M et al (2017) Long-term disability progression in primary progressive multiple sclerosis: a 15-year study. Brain 140:2814–2819
Rodriguez EG, Wegner C, Kreutzfeldt M et al (2014) Oligodendroglia in cortical multiple sclerosis lesions decrease with disease progression, but regenerate after repeated experimental demyelination. Acta Neuropathol 128:231–246
Roosendaal SD, Moraal B, Pouwels PJ et al (2009) Accumulation of cortical lesions in MS: relation with cognitive impairment. Mult Scler 15:708–714
Sailer M, Fischl B, Salat D et al (2003) Focal thinning of the cerebral cortex in multiple sclerosis. Brain 126:1734–1744
Sander M (1898) Hirnrindenbefunde bei multipler Sklerose. Eur Neurol 4:427–436
Schmierer K, Tozer DJ, Scaravilli F et al (2007) Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain. J Magn Reson Imaging 26:41–51
Schob F (1907) Ein Beitrag zur pathologischen Anatomie der multiplen Sklerose. Eur Neurol 22:62–87
Schoonheim MM, Meijer KA, Geurts JJ (2015) Network collapse and cognitive impairment in multiple sclerosis. Front Neurol 6:82
Schumacher AM, Mahler C, Kerschensteiner M (2017) Pathology and pathogenesis of progressive multiple sclerosis: concepts and controversies. Aktuel Neurol 44:476–488
Shiee N, Bazin PL, Zackowski KM et al (2012) Revisiting brain atrophy and its relationship to disability in multiple sclerosis. PLoS One 7:e37049
Spano B, Giulietti G, Pisani V et al (2018) Disruption of neurite morphology parallels MS progression. Neurol Neuroimmunol Neuroinflamm 5:e502
Steenwijk MD, Geurts JJ, Daams M et al (2016) Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. Brain 139:115–126
Tournier J-D, Smith R, Raffelt D et al (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202:116137
Trapp BD, Vignos M, Dudman J et al (2018) Cortical neuronal densities and cerebral white matter demyelination in multiple sclerosis: a retrospective study. Lancet Neurol 17:870–884
Triarhou LC (2008) The 107 cortical cytoarchitectonic areas of Constantin Von Economo and Georg N. Koskinas in the Adult human brain: excerpt from: “Atlas of Cytoarchitectonics of the Adult Human Cerebral Cortex”, Authors, Von Economo, C.(Vienna), Koskinas, GN (Athens). Karger
Tustison NJ, Avants BB (2013) Explicit B-spline regularization in diffeomorphic image registration. Front Neuroinform 7:39
Van Essen DC (2005) A population-average, landmark- and surface-based (PALS) atlas of human cerebral cortex. NeuroImage 28:635–662
Wang WY, Yu JT, Liu Y et al (2015) Voxel-based meta-analysis of grey matter changes in Alzheimer’s disease. Transl Neurodegener 4:6
Winkler AM, Greve DN, Bjuland KJ et al (2018) Joint analysis of cortical area and thickness as a replacement for the analysis of the volume of the cerebral cortex. Cereb Cortex 28:738–749
Winkler AM, Sabuncu MR, Yeo BT et al (2012) Measuring and comparing brain cortical surface area and other areal quantities. NeuroImage 61:1428–1443
Winkler AM, Webster MA, Brooks JC et al (2016) Non-parametric combination and related permutation tests for neuroimaging. Hum Brain Mapp 37:1486–1511
Yotter RA, Dahnke R, Thompson PM et al (2011) Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp 32:1109–1124
Yotter RA, Thompson PM, Gaser C (2011) Algorithms to improve the reparameterization of spherical mappings of brain surface meshes. J Neuroimaging 21:e134–e147
Zhang H, Schneider T, Wheeler-Kingshott CA et al (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61:1000–1016
Zijdenbos AP, Forghani R, Evans AC (2002) Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Trans Med Imaging 21:1280–1291
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Gonzalez-Escamilla, G., Ciolac, D. (2021). Pathophysiology of Grey Matter Affection in MS. In: Groppa, S., G. Meuth, S. (eds) Translational Methods for Multiple Sclerosis Research. Neuromethods, vol 166. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1213-2_4
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DOI: https://doi.org/10.1007/978-1-0716-1213-2_4
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