CC BY-NC-ND 4.0 · Neuropediatrics 2023; 54(04): 244-252
DOI: 10.1055/a-2073-4178
Original Article

Clinical Significance of Diffusion Tensor Imaging in Metachromatic Leukodystrophy

Lucas Bastian Amedick
1   Department of Pediatric Neurology and Developmental Medicine, University of Tuebingen, Tuebingen, Germany
,
2   Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University Hospitals Tubingen, Tubingen, Germany
,
Judith Beschle
3   Department of Pediatric Neurology and Developmental Medicine, University Hospital Tübingen, Tuebingen, Germany
,
Manuel Strölin
1   Department of Pediatric Neurology and Developmental Medicine, University of Tuebingen, Tuebingen, Germany
,
Marko Wilke
4   Department of Pediatric Neurology, Children's Hospital, Tübingen, Germany
,
5   Department of Child Neurology, Amsterdam UMC Locatie VUmc, Amsterdam, The Netherlands
,
Petra Pouwels
6   Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, The Netherlands
,
Gisela Hagberg
7   Max-Planck-Institut für Biologische Kybernetik, Tubingen, Baden-Württemberg, Germany
,
Uwe Klose
8   Department of Diagnostic and Interventional Neuroradiology, Radiological Clinic, University of Tübingen, Tübingen, Germany
,
Thomas Naegele
9   Department für Diagnostische und Interventionelle Neuroradiologie, Universitätsklinikum Tübingen, Tubingen, Baden-Württemberg, Germany
,
Ingeborg Kraegeloh-Mann
10   Kinderklinik - University Tübingen, Tübingen, Germany
,
Samuel Groeschel
1   Department of Pediatric Neurology and Developmental Medicine, University of Tuebingen, Tuebingen, Germany
› Author Affiliations
Funding The work was supported by an institutional research grant from Takeda Pharma AG (IIR-DEU-002540) and the German Research Foundation (GR 4688/2–1).

Abstract

Background Metachromatic leukodystrophy (MLD) is a lysosomal enzyme deficiency disorder leading to progressive demyelination and, consecutively, to cognitive and motor decline. Brain magnetic resonance imaging (MRI) can detect affected white matter as T2 hyperintense areas but cannot quantify the gradual microstructural process of demyelination more accurately. Our study aimed to investigate the value of routine MR diffusion tensor imaging in assessing disease progression.

Methods MR diffusion parameters (apparent diffusion coefficient [ADC] and fractional anisotropy [FA]) were in the frontal white matter, central region (CR), and posterior limb of the internal capsule in 111 MR datasets from a natural history study of 83 patients (age: 0.5–39.9 years; 35 late-infantile, 45 juvenile, 3 adult, with clinical diffusion sequences of different scanner manufacturers) as well as 120 controls. Results were correlated with clinical parameters reflecting motor and cognitive function.

Results ADC values increase and FA values decrease depending on disease stage/severity. They show region-specific correlations with clinical parameters of motor and cognitive symptoms, respectively. Higher ADC levels in CR at diagnosis predicted a disease course with more rapid motor deterioration in juvenile MLD patients. In highly organized tissues such as the corticospinal tract, in particular, diffusion MR parameters were highly sensitive to MLD-associated changes and did not correlate with the visual quantification of T2 hyperintensities.

Conclusion Our results show that diffusion MRI can deliver valuable, robust, clinically meaningful, and easily obtainable/accessible/available parameters in the assessment of prognosis and progression of MLD. Therefore, it provides additional quantifiable information to established methods such as T2 hyperintensity.

Note

S.G., I.K.-M., and N.W. are members of the ERN-RND, project ID 739510.


MRI data used in the preparation of this manuscript were obtained from the National Institute of Mental Health (NIMH) Data Archive (NDA). NDA is a collaborative informatics system created by the NIH to provide a national resource to support and accelerate research in mental health. Dataset identifier(s): DOI 10.15154/1528588. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or of the Submitters submitting original data to NDA.


Ethics Approval

The study was approved by the local ethics committees of the University of Tuebingen, Germany, and VU University Medical Center, Amsterdam, the Netherlands (401/2005 and 2018.300). Written informed consent was given by the parents of the patients or the patients themselves as appropriate.


Supplementary Material



Publication History

Received: 11 July 2022

Accepted: 22 March 2023

Accepted Manuscript online:
13 April 2023

Article published online:
10 May 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Gieselmann V, Krägeloh-Mann I. Metachromatic leukodystrophy. In: Valle D, Beudet A, Vogelstein B. et al, eds. The Online Metabolic and Molecular Bases of Inherited Disease. McGraw-Hill; 2014. :chapter 148
  • 2 Kehrer C, Elgün S, Raabe C. et al. Association of age at onset and first symptoms with disease progression in patients with metachromatic leukodystrophy. Neurology 2021; 96 (02) e255-e266
  • 3 Groeschel S, Kehrer C, Engel C. et al. Metachromatic leukodystrophy: natural course of cerebral MRI changes in relation to clinical course. J Inherit Metab Dis 2011; 34 (05) 1095-1102
  • 4 Groeschel S, í Dali C, Clas P. et al. Cerebral gray and white matter changes and clinical course in metachromatic leukodystrophy. Neurology 2012; 79 (16) 1662-1670
  • 5 Strölin M, Krägeloh-Mann I, Kehrer C, Wilke M, Groeschel S. Demyelination load as predictor for disease progression in juvenile metachromatic leukodystrophy. Ann Clin Transl Neurol 2017; 4 (06) 403-410
  • 6 Martin P, Hagberg GE, Schultz T. et al. T2-pseudonormalization and microstructural characterization in advanced stages of late-infantile metachromatic leukodystrophy. Clin Neuroradiol 2021; 31 (04) 969-980
  • 7 van Rappard DF, Klauser A, Steenweg ME. et al. Quantitative MR spectroscopic imaging in metachromatic leukodystrophy: value for prognosis and treatment. J Neurol Neurosurg Psychiatry 2018; 89 (01) 105-111
  • 8 van Rappard DF, Königs M, Steenweg ME. et al. Diffusion tensor imaging in metachromatic leukodystrophy. J Neurol 2018; 265 (03) 659-668
  • 9 Feldmann J, Martin P, Bender B. et al. MR-spectroscopy in metachromatic leukodystrophy: a model free approach and clinical correlation. Neuroimage Clin 2023; 37: 103296
  • 10 Sullivan EV, Adalsteinsson E, Pfefferbaum A. Selective age-related degradation of anterior callosal fiber bundles quantified in vivo with fiber tracking. Cereb Cortex 2006; 16 (07) 1030-1039
  • 11 Rueda-Lopes FC, Hygino da Cruz Jr LC, Doring TM, Gasparetto EL. Diffusion-weighted imaging and demyelinating diseases: new aspects of an old advanced sequence. AJR Am J Roentgenol 2014; 202 (01) W34-42
  • 12 Singh P, Kaur R. Diffusion-weighted magnetic resonance imaging findings in a case of metachromatic leukodystrophy. J Pediatr Neurosci 2016; 11 (02) 131-133
  • 13 Martin A, Sevin C, Lazarus C, Bellesme C, Aubourg P, Adamsbaum C. Toward a better understanding of brain lesions during metachromatic leukodystrophy evolution. AJNR Am J Neuroradiol 2012; 33 (09) 1731-1739
  • 14 Patay Z. Diffusion-weighted MR imaging in leukodystrophies. Eur Radiol 2005; 15 (11) 2284-2303
  • 15 Eichler F, Grodd W, Grant E. et al. Metachromatic leukodystrophy: a scoring system for brain MR imaging observations. AJNR Am J Neuroradiol 2009; 30 (10) 1893-1897
  • 16 Clas P, Groeschel S, Wilke M. A semi-automatic algorithm for determining the demyelination load in metachromatic leukodystrophy. Acad Radiol 2012; 19 (01) 26-34
  • 17 Grech-Sollars M, Hales PW, Miyazaki K. et al. Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain. NMR Biomed 2015; 28 (04) 468-485
  • 18 Fox RJ, Sakaie K, Lee JC. et al. A validation study of multicenter diffusion tensor imaging: reliability of fractional anisotropy and diffusivity values. AJNR Am J Neuroradiol 2012; 33 (04) 695-700
  • 19 Groeschel S, Hagberg GE, Schultz T. et al. Assessing white matter microstructure in brain regions with different myelin architecture using MRI. PLoS One 2016; 11 (11) e0167274
  • 20 Kehrer C, Blumenstock G, Raabe C, Krägeloh-Mann I. Development and reliability of a classification system for gross motor function in children with metachromatic leucodystrophy. Dev Med Child Neurol 2011; 53 (02) 156-160
  • 21 DeLano MC, Cooper TG, Siebert JE, Potchen MJ, Kuppusamy K. High-b-value diffusion-weighted MR imaging of adult brain: image contrast and apparent diffusion coefficient map features. AJNR Am J Neuroradiol 2000; 21 (10) 1830-1836
  • 22 García Santos JM, Ordóñez C, Torres del Río S. ADC measurements at low and high b values: insight into normal brain structure with clinical DWI. Magn Reson Imaging 2008; 26 (01) 35-44
  • 23 Hui ES, Cheung MM, Chan KC, Wu EX. B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes. Neuroimage 2010; 49 (03) 2366-2374
  • 24 Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016; 125: 1063-1078
  • 25 Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994; 103 (03) 247-254
  • 26 Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002; 17 (02) 825-841
  • 27 Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 2009; 48 (01) 63-72
  • 28 Tournier JD, Calamante F, Connelly A. MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 2012; 22 (01) 53-66
  • 29 Tillema JM, Derks MG, Pouwels PJ. et al. Volumetric MRI data correlate to disease severity in metachromatic leukodystrophy. Ann Clin Transl Neurol 2015; 2 (09) 932-940
  • 30 Schoenmakers DH, Beerepoot S, Krägeloh-Mann I. et al. Recognizing early MRI signs (or their absence) is crucial in diagnosing metachromatic leukodystrophy. Ann Clin Transl Neurol 2022; 9 (12) 1999-2009
  • 31 Kehrer C, Blumenstock G, Gieselmann V, Krägeloh-Mann I. GERMAN LEUKONET. The natural course of gross motor deterioration in metachromatic leukodystrophy. Dev Med Child Neurol 2011; 53 (09) 850-855
  • 32 Reynolds JE, Grohs MN, Dewey D, Lebel C. Global and regional white matter development in early childhood. Neuroimage 2019; 196: 49-58
  • 33 Clayden JD, Jentschke S, Muñoz M. et al. Normative development of white matter tracts: similarities and differences in relation to age, gender, and intelligence. Cereb Cortex 2012; 22 (08) 1738-1747
  • 34 Aung WY, Mar S, Benzinger TL. Diffusion tensor MRI as a biomarker in axonal and myelin damage. Imaging Med 2013; 5 (05) 427-440
  • 35 Suzuki K. Lysosomal diseases. In: Lewis DN, Love S, Ellison DW. eds. Greenfields Neuropathology 8th ed. London, UK: Hodder Arnold Publishers; 2007: 515-599
  • 36 De Santis S, Drakesmith M, Bells S, Assaf Y, Jones DK. Why diffusion tensor MRI does well only some of the time: variance and covariance of white matter tissue microstructure attributes in the living human brain. Neuroimage 2014; 89 (100) 35-44
  • 37 Winklewski PJ, Sabisz A, Naumczyk P, Jodzio K, Szurowska E, Szarmach A. Understanding the physiopathology behind axial and radial diffusivity changes-what do we know?. Front Neurol 2018; 9: 92
  • 38 Groeschel S, Tournier JD, Northam GB. et al. Identification andbib interpretation of microstructural abnormalities in motor pathways in adolescents born preterm. Neuroimage 2014; 87: 209-219
  • 39 Wheeler-Kingshott CA, Cercignani M. About “axial” and “radial” diffusivities. Magn Reson Med 2009; 61 (05) 1255-1260
  • 40 Mirzaalian H, de Pierrefeu A, Savadjiev P. et al. Harmonizing diffusion MRI data across multiple sites and scanners. Med Image Comput Comput Assist Interv 2015; 9349: 12-19