{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T14:00:42Z","timestamp":1790776842153,"version":"4.1.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The leukodystrophy \u201cvanishing white matter\u201d (VWM) and \u201cmetachromatic leukodystrophy\u201d (MLD) affect the brain's white matter, but have very different underlying pathology. We aim to determine whether quantitative MRI reflects known neuropathological differences and correlates with clinical scores in these leukodystrophies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>VWM and MLD patients and controls were prospectively included between 2020 and 2023. Clinical scores were recorded. MRI at 3\u2009T included multi-compartment relaxometry diffusion-informed myelin water imaging (MCR-DIMWI) and multi-echo T2-relaxation imaging with compressed sensing (METRICS) to determine myelin water fractions (MWF). Multi-shell diffusion-weighted data were used for diffusion tensor imaging measures and neurite orientation dispersion and density imaging (NODDI) analysis, which estimates neurite density index, orientation dispersion index, and free water fraction. As quantitative MRI measures are age-dependent, ratios between actual and age-expected MRI measures were calculated. We performed the multilevel analysis with subsequent post-hoc and correlation tests to assess differences between groups and clinico-radiological correlations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Sixteen control (age range: 2.3\u201361.3 years, 8 male), 37 VWM (2.4\u201356.5 years, 20 male), and 14 MLD (2.2\u201341.7 years, 6 male) subjects were included. Neurite density index and MWF were lower in patients than in controls (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Free water fraction was highest in VWM (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.01), but similar to controls in MLD (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.99). Changes in diffusion tensor imaging measures relative to controls were generally more pronounced in VWM than in MLD. In both patient groups, MCR-DIMWI MWF correlated strongest with clinical measures.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Quantitative MRI correlates to clinical measures and yields differential profiles in VWM and MLD, in line with differences in neuropathology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can quantitative MRI reflect known neuropathological differences and correlate with clinical scores for these leukodystrophies<\/jats:italic>\n                      ?\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Finding<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Quantitative MRI measures, e.g., MWF, neurite density index, and free water fraction differ between leukodystrophies and controls, in correspondence to known histological differences<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>MRI techniques producing quantitative, biologically-specific, measures regarding the health of myelin and axons deliver more comprehensive information regarding pathological changes in leukodystrophies than current approaches, and are thus viable tools for monitoring patients and providing clinical trial outcome measures<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-024-11089-5","type":"journal-article","created":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T17:24:34Z","timestamp":1727285074000},"page":"1845-1857","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Quantitative MRI distinguishes different leukodystrophies and correlates with clinical measures"],"prefix":"10.1007","volume":"35","author":[{"given":"Menno D.","family":"Stellingwerff","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murtadha L.","family":"Al-Saady","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwok-Shing","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Dvorak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 P.","family":"Marques","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shannon","family":"Kolind","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daphne H.","family":"Schoenmakers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romy","family":"van Voorst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan D.","family":"Roosendaal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicole I.","family":"Wolf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Berkhof","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petra J. W.","family":"Pouwels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8912-0954","authenticated-orcid":false,"given":"Marjo S.","family":"van der Knaap","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,25]]},"reference":[{"key":"11089_CR1","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.ymgme.2015.01.006","volume":"114","author":"A Vanderver","year":"2015","unstructured":"Vanderver A, Prust M, Tonduti D et al (2015) Case definition and classification of leukodystrophies and leukoencephalopathies. Mol Genet Metab 114:494\u2013500","journal-title":"Mol Genet Metab"},{"key":"11089_CR2","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s00401-017-1739-1","volume":"134","author":"MS van der Knaap","year":"2017","unstructured":"van der Knaap MS, Bugiani M (2017) Leukodystrophies: a proposed classification system based on pathological changes and pathogenetic mechanisms. Acta Neuropathol 134:351\u2013382","journal-title":"Acta Neuropathol"},{"key":"11089_CR3","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1016\/S1474-4422(19)30143-7","volume":"18","author":"MS van der Knaap","year":"2019","unstructured":"van der Knaap MS, Schiffmann R, Mochel F, Wolf NI (2019) Diagnosis, prognosis, and treatment of leukodystrophies. Lancet Neurol 18:962\u2013972","journal-title":"Lancet Neurol"},{"key":"11089_CR4","doi-asserted-by":"publisher","first-page":"103427","DOI":"10.1016\/j.nicl.2023.103427","volume":"38","author":"MD Stellingwerff","year":"2023","unstructured":"Stellingwerff MD, Pouwels PJW, Roosendaal SD, Barkhof F, van der Knaap MS (2023) Quantitative MRI in leukodystrophies. Neuroimage Clin 38:103427","journal-title":"Neuroimage Clin"},{"key":"11089_CR5","doi-asserted-by":"publisher","unstructured":"Stellingwerff MD, Al-Saady ML, Chan KS et al (2023) Applicability of multiple quantitative magnetic resonance methods in genetic brain white matter disorders. J Neuroimaging. https:\/\/doi.org\/10.1111\/jon.13167","DOI":"10.1111\/jon.13167"},{"key":"11089_CR6","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1002\/ana.25287","volume":"84","author":"EMC Hamilton","year":"2018","unstructured":"Hamilton EMC, van der Lei HDW, Vermeulen G et al (2018) Natural history of vanishing white matter. Ann Neurol 84:274\u2013288","journal-title":"Ann Neurol"},{"key":"11089_CR7","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1002\/ajmg.a.61641","volume":"182","author":"JL Schmidt","year":"2020","unstructured":"Schmidt JL, Pizzino A, Nicholl J et al (2020) Estimating the relative frequency of leukodystrophies and recommendations for carrier screening in the era of next\u2010generation sequencing. Am J Med Genet A 182:1906\u20131912","journal-title":"Am J Med Genet A"},{"key":"11089_CR8","doi-asserted-by":"publisher","unstructured":"Slynko I, Nguyen S, Hamilton EMC et al (2021) Vanishing white matter: eukaryotic initiation factor 2B model and the impact of missense mutations. Mol Genet Genomic Med. https:\/\/doi.org\/10.1002\/mgg3.1593:e1593","DOI":"10.1002\/mgg3.1593:e1593"},{"key":"11089_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1055\/s-0030-1253412","volume":"41","author":"V Gieselmann","year":"2010","unstructured":"Gieselmann V, Krageloh-Mann I (2010) Metachromatic leukodystrophy-an update. Neuropediatrics 41:1\u20136","journal-title":"Neuropediatrics"},{"key":"11089_CR10","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1002\/acn3.50826","volume":"6","author":"TEM Abbink","year":"2019","unstructured":"Abbink TEM, Wisse LE, Jaku E et al (2019) Vanishing white matter: deregulated integrated stress response as therapy target. Ann Clin Transl Neurol 6:1407\u20131422","journal-title":"Ann Clin Transl Neurol"},{"key":"11089_CR11","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1111\/bpa.12606","volume":"28","author":"M Bugiani","year":"2018","unstructured":"Bugiani M, Vuong C, Breur M, van der Knaap MS (2018) Vanishing white matter: a leukodystrophy due to astrocytic dysfunction. Brain Pathol 28:408\u2013421","journal-title":"Brain Pathol"},{"key":"11089_CR12","doi-asserted-by":"publisher","unstructured":"Stellingwerff MD, Al-Saady ML, van de Brug T, Barkhof F, Pouwels PJW, van der Knaap MS (2021) MRI natural history of the leukodystrophy vanishing white matter. Radiology. https:\/\/doi.org\/10.1148\/radiol.2021210110:210110","DOI":"10.1148\/radiol.2021210110:210110"},{"key":"11089_CR13","doi-asserted-by":"publisher","first-page":"375","DOI":"10.3109\/07853890109002092","volume":"33","author":"WJ Furlong","year":"2001","unstructured":"Furlong WJ, Feeny DH, Torrance GW, Barr RD (2001) The health utilities index (HUI\u00ae) system for assessing health-related quality of life in clinical studies. Ann Med 33:375\u2013384","journal-title":"Ann Med"},{"key":"11089_CR14","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/jir.12691","volume":"64","author":"C Farmer","year":"2020","unstructured":"Farmer C, Adedipe D, Bal V, Chlebowski C, Thurm A (2020) Concordance of the Vineland adaptive behavior scales, second and third editions. J Intellect Disabil Res 64:18\u201326","journal-title":"J Intellect Disabil Res"},{"key":"11089_CR15","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1177\/08830738231188159","volume":"38","author":"F Gavazzi","year":"2023","unstructured":"Gavazzi F, Patel V, Charsar B et al (2023) Gross motor function in pediatric onset TUBB4A-related leukodystrophy: GMFM-88 performance and validation of GMFC-MLD in TUBB4A. J Child Neurol 38:498\u2013504","journal-title":"J Child Neurol"},{"key":"11089_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117159","volume":"221","author":"KS Chan","year":"2020","unstructured":"Chan KS, Marques JP (2020) Multi-compartment relaxometry and diffusion informed myelin water imaging\u2014promises and challenges of new gradient echo myelin water imaging methods. Neuroimage 221:117159","journal-title":"Neuroimage"},{"key":"11089_CR17","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1002\/mrm.28199","volume":"84","author":"AV Dvorak","year":"2020","unstructured":"Dvorak AV, Wiggermann V, Gilbert G et al (2020) Multi-spin echo T2 relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging. Magn Reson Med 84:1264\u20131279","journal-title":"Magn Reson Med"},{"key":"11089_CR18","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.neuroimage.2012.03.072","volume":"61","author":"H Zhang","year":"2012","unstructured":"Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61:1000\u20131016","journal-title":"Neuroimage"},{"key":"11089_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102789","volume":"86","author":"B Billot","year":"2023","unstructured":"Billot B, Greve DN, Puonti O et al (2023) SynthSeg: segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal 86:102789","journal-title":"Med Image Anal"},{"key":"11089_CR20","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.media.2006.06.004","volume":"10","author":"H Zhang","year":"2006","unstructured":"Zhang H, Yushkevich PA, Alexander DC, Gee JC (2006) Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med Image Anal 10:764\u2013785","journal-title":"Med Image Anal"},{"key":"11089_CR21","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.neuroimage.2017.07.015","volume":"170","author":"E Garyfallidis","year":"2018","unstructured":"Garyfallidis E, Cote MA, Rheault F et al (2018) Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage 170:283\u2013295","journal-title":"Neuroimage"},{"key":"11089_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3778","volume":"32","author":"C Lebel","year":"2019","unstructured":"Lebel C, Treit S, Beaulieu C (2019) A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR Biomed 32:e3778","journal-title":"NMR Biomed"},{"key":"11089_CR23","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.neuroimage.2013.09.058","volume":"84","author":"DC Dean 3rd","year":"2014","unstructured":"Dean DC 3rd, O\u2019Muircheartaigh J, Dirks H et al (2014) Modeling healthy male white matter and myelin development: 3 through 60months of age. Neuroimage 84:742\u2013752","journal-title":"Neuroimage"},{"key":"11089_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118749","volume":"245","author":"CS Parker","year":"2021","unstructured":"Parker CS, Veale T, Bocchetta M et al (2021) Not all voxels are created equal: reducing estimation bias in regional NODDI metrics using tissue-weighted means. Neuroimage 245:118749","journal-title":"Neuroimage"},{"key":"11089_CR25","doi-asserted-by":"publisher","unstructured":"Stellingwerff MD, van de Wiel MA, van der Knaap MS (2022) Radiological correlates of episodes of acute decline in the leukodystrophy vanishing white matter. Neuroradiology. https:\/\/doi.org\/10.1007\/s00234-022-03097-3","DOI":"10.1007\/s00234-022-03097-3"},{"key":"11089_CR26","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1055\/s-0032-1307456","volume":"43","author":"HD van der Lei","year":"2012","unstructured":"van der Lei HD, Steenweg ME, Barkhof F et al (2012) Characteristics of early MRI in children and adolescents with vanishing white matter. Neuropediatrics 43:22\u201326","journal-title":"Neuropediatrics"},{"key":"11089_CR27","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1007\/s00062-020-00975-2","volume":"31","author":"P Martin","year":"2020","unstructured":"Martin P, Hagberg GE, Schultz T et al (2020) T2-pseudonormalization and microstructural characterization in advanced stages of late-infantile metachromatic leukodystrophy. Clin Neuroradiol 31:969\u2013980","journal-title":"Clin Neuroradiol"},{"key":"11089_CR28","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s00415-018-8765-3","volume":"265","author":"DF van Rappard","year":"2018","unstructured":"van Rappard DF, Konigs M, Steenweg ME et al (2018) Diffusion tensor imaging in metachromatic leukodystrophy. J Neurol 265:659\u2013668","journal-title":"J Neurol"},{"key":"11089_CR29","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1002\/nbm.951","volume":"18","author":"EE Odrobina","year":"2005","unstructured":"Odrobina EE, Lam TY, Pun T, Midha R, Stanisz GJ (2005) MR properties of excised neural tissue following experimentally induced demyelination. NMR Biomed 18:277\u2013284","journal-title":"NMR Biomed"},{"key":"11089_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-79540-3","volume":"11","author":"AV Dvorak","year":"2021","unstructured":"Dvorak AV, Swift-LaPointe T, Vavasour IM et al (2021) An atlas for human brain myelin content throughout the adult life span. Sci Rep 11:269","journal-title":"Sci Rep"},{"key":"11089_CR31","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1002\/acn3.540","volume":"5","author":"MD Klok","year":"2018","unstructured":"Klok MD, Bugiani M, de Vries SI et al (2018) Axonal abnormalities in vanishing white matter. Ann Clin Transl Neurol 5:429\u2013444","journal-title":"Ann Clin Transl Neurol"},{"key":"11089_CR32","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1002\/mrm.10411","volume":"49","author":"S Webb","year":"2003","unstructured":"Webb S, Munro CA, Midha R, Stanisz GJ (2003) Is multicomponent T2 a good measure of myelin content in peripheral nerve? Magn Reson Med 49:638\u2013645","journal-title":"Magn Reson Med"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-024-11089-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-024-11089-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-024-11089-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T05:28:00Z","timestamp":1742189280000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-024-11089-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["11089"],"URL":"https:\/\/doi.org\/10.1007\/s00330-024-11089-5","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,25]]},"assertion":[{"value":"21 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is M.S. van der Knaap.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"J. Berkhof has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"This study was approved by the local ethics committee and signed informed consent was obtained from subjects and\/or caregivers.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Most control subjects (15 out of 16) have been previously reported (Stellingwerff et al 2023, Neuroimage). This prior article dealt with the development of the MRI acquisition protocol used in the current manuscript. The current manuscript aims to determine whether quantitative MRI measures reflect differences in neuropathology between two different leukodystrophies, correlate with clinical scores, and provide biomarkers for monitoring. These questions were not addressed in the previous article. None of the patients have been previously reported.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}