{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:50:56Z","timestamp":1790844656217,"version":"4.1.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["NS094387"],"award-info":[{"award-number":["NS094387"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deficits in white matter (WM) integrity and motor symptoms are among the most robust and reproducible features of myotonic dystrophy type 1 (DM1). In the present study, we investigate whether WM integrity, obtained from diffusion-weighted MRI, corresponds to quantifiable motor outcomes (<jats:italic>e.g.<\/jats:italic>, fine motor skills and grip strength) and patient-reported, subjective motor deficits. Critically, we explore these relationships in the context of other potentially causative variables, including: disease duration, elapsed time since motor symptom onset; and genetic burden, the number of excessive CTG repeats causing DM1. We found that fractional anisotropy (a measure of WM integrity) throughout the cerebrum was the strongest predictor of grip strength independently of disease duration and genetic burden, while radial diffusivity predicted fine motor skill (peg board performance). Axial diffusivity did not predict motor outcomes. Our results are consistent with the notion that systemic degradation of WM in DM1 mediates the relationship between DM1 progression and genetic burden with motor outcomes of the disease. Our results suggest that tracking changes in WM integrity over time may be a valuable biomarker for tracking therapeutic interventions, such as future gene therapies, for DM1.<\/jats:p>","DOI":"10.1038\/s41598-021-84520-2","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T11:07:32Z","timestamp":1614596852000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["White matter microstructure relates to motor outcomes in myotonic dystrophy type 1 independently of disease duration and genetic burden"],"prefix":"10.1038","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2551-8127","authenticated-orcid":false,"given":"Timothy R.","family":"Koscik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7490-6636","authenticated-orcid":false,"given":"Ellen","family":"van der Plas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laurie","family":"Gutmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0201-3660","authenticated-orcid":false,"given":"Sarah A.","family":"Cumming","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darren G.","family":"Monckton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Magnotta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard K.","family":"Shields","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peggy C.","family":"Nopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,1]]},"reference":[{"key":"84520_CR1","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.bbadis.2014.05.019","volume":"1852","author":"G Meola","year":"2015","unstructured":"Meola, G. & Cardani, R. Myotonic dystrophies: An update on clinical aspects, genetic, pathology, and molecular pathomechanisms. Biochim. Biophys. Acta 1852, 594\u2013606 (2015).","journal-title":"Biochim. Biophys. Acta"},{"key":"84520_CR2","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1002\/ana.23992","volume":"74","author":"M Nakamori","year":"2013","unstructured":"Nakamori, M. et al. Splicing biomarkers of disease severity in myotonic dystrophy. Ann. Neurol. 74, 862\u2013872 (2013).","journal-title":"Ann. Neurol."},{"key":"84520_CR3","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jns.2014.04.005","volume":"341","author":"JR Wozniak","year":"2014","unstructured":"Wozniak, J. R., Mueller, B. A., Lim, K. O., Hemmy, L. S. & Day, J. W. Tractography reveals diffuse white matter abnormalities in Myotonic Dystrophy Type 1. J. Neurol. Sci. 341, 73\u201378 (2014).","journal-title":"J. Neurol. Sci."},{"key":"84520_CR4","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1212\/WNL.0000000000004300","volume":"89","author":"K Okkersen","year":"2017","unstructured":"Okkersen, K. et al. Brain imaging in myotonic dystrophy type 1: A systematic review. Neurology 89, 960\u2013969 (2017).","journal-title":"Neurology"},{"key":"84520_CR5","doi-asserted-by":"publisher","first-page":"3530","DOI":"10.1093\/brain\/awr299","volume":"134","author":"M Minnerop","year":"2011","unstructured":"Minnerop, M. et al. The brain in myotonic dystrophy 1 and 2: evidence for a predominant white matter disease. Brain 134, 3530\u20133546 (2011).","journal-title":"Brain"},{"key":"84520_CR6","doi-asserted-by":"publisher","first-page":"e104697","DOI":"10.1371\/journal.pone.0104697","volume":"9","author":"F Caso","year":"2014","unstructured":"Caso, F. et al. Cognitive impairment in myotonic dystrophy type 1 is associated with white matter damage. PLoS ONE 9, e104697 (2014).","journal-title":"PLoS ONE"},{"key":"84520_CR7","first-page":"21","volume":"30","author":"L Serra","year":"2015","unstructured":"Serra, L. et al. How genetics affects the brain to produce higher-level dysfunctions in myotonic dystrophy type 1. Funct. Neurol. 30, 21\u201331 (2015).","journal-title":"Funct. Neurol."},{"key":"84520_CR8","first-page":"190","volume":"12","author":"S Baldanzi","year":"2016","unstructured":"Baldanzi, S. et al. Relationship between neuropsychological impairment and grey and white matter changes in adult-onset myotonic dystrophy type 1. NeuroimageClin. 12, 190\u2013197 (2016).","journal-title":"NeuroimageClin."},{"key":"84520_CR9","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1016\/j.nicl.2016.04.012","volume":"11","author":"S Zanigni","year":"2016","unstructured":"Zanigni, S. et al. Relationship of white and gray matter abnormalities to clinical and genetic features in myotonic dystrophy type 1. NeuroImage: Clinical 11, 678\u2013685 (2016).","journal-title":"NeuroImage: Clinical"},{"key":"84520_CR10","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1093\/arclin\/acx008","volume":"32","author":"T Cabada","year":"2017","unstructured":"Cabada, T. et al. Brain Involvement in myotonic dystrophy type 1: A morphometric and diffusion tensor imaging study with neuropsychological correlation. Arch. Clin. Neuropsychol. 32, 401\u2013412 (2017).","journal-title":"Arch. Clin. Neuropsychol."},{"key":"84520_CR11","doi-asserted-by":"publisher","first-page":"807","DOI":"10.3349\/ymj.2017.58.4.807","volume":"58","author":"WK Yoo","year":"2017","unstructured":"Yoo, W. K., Park, Y. G., Choi, Y. C. & Kim, S. M. Cortical thickness and white matter integrity are associated with CTG expansion size in myotonic dystrophy type I. Yonsei Med. J. 58, 807\u2013815 (2017).","journal-title":"Yonsei Med. J."},{"key":"84520_CR12","doi-asserted-by":"publisher","first-page":"15592","DOI":"10.1038\/s41598-018-34048-9","volume":"8","author":"J-S Park","year":"2018","unstructured":"Park, J.-S. et al. Diffusion tensor imaging and voxel-based morphometry reveal corticospinal tract involvement in the motor dysfunction of adult-onset myotonic dystrophy type 1. Sci. Rep. 8, 15592 (2018).","journal-title":"Sci. Rep."},{"key":"84520_CR13","first-page":"102078","volume":"24","author":"G Labayru","year":"2019","unstructured":"Labayru, G. et al. Regional brain atrophy in gray and white matter is associated with cognitive impairment in Myotonic Dystrophy type 1. NeuroimageClin 24, 102078 (2019).","journal-title":"NeuroimageClin"},{"key":"84520_CR14","first-page":"101615","volume":"21","author":"M van Dorst","year":"2019","unstructured":"van Dorst, M. et al. Structural white matter networks in myotonic dystrophy type 1. NeuroimageClin 21, 101615 (2019).","journal-title":"NeuroimageClin"},{"key":"84520_CR15","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1002\/mus.24097","volume":"49","author":"C Heatwole","year":"2014","unstructured":"Heatwole, C. et al. Myotonic dystrophy health index: initial evaluation of a disease-specific outcome measure. Muscle Nerve 49, 906\u2013914 (2014).","journal-title":"Muscle Nerve"},{"key":"84520_CR16","doi-asserted-by":"publisher","first-page":"2245","DOI":"10.1093\/hmg\/ddz055","volume":"28","author":"G Overend","year":"2019","unstructured":"Overend, G. et al. Allele length of the DMPK CTG repeat is a predictor of progressive myotonic dystrophy type 1 phenotypes. Hum. Mol. Genet. 28, 2245\u20132254 (2019).","journal-title":"Hum. Mol. Genet."},{"key":"84520_CR17","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1037\/0022-3514.51.6.1173","volume":"51","author":"RM Baron","year":"1986","unstructured":"Baron, R. M. & Kenny, D. A. The moderator\u2013mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51, 1173 (1986).","journal-title":"J. Pers. Soc. Psychol."},{"key":"84520_CR18","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1093\/gerona\/gly046","volume":"74","author":"AR Zammit","year":"2019","unstructured":"Zammit, A. R., Robitaille, A., Piccinin, A. M., Muniz-Terrera, G. & Hofer, S. M. Associations between aging-related changes in grip strength and cognitive function in older adults: A systematic review. J. Gerontol. A Biol. Sci. Med. Sci. 74, 519\u2013527 (2019).","journal-title":"J. Gerontol. A Biol. Sci. Med. Sci."},{"key":"84520_CR19","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1002\/mus.26767","volume":"61","author":"RK Shields","year":"2020","unstructured":"Shields, R. K. et al. Myotonic dystrophy type 1 alters muscle twitch properties, spinal reflexes, and perturbation-induced trans-cortical reflexes. Muscle Nerve 61, 205\u2013212 (2020).","journal-title":"Muscle Nerve"},{"key":"84520_CR20","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1212\/WNL.56.3.336","volume":"56","author":"J Mathieu","year":"2001","unstructured":"Mathieu, J., Boivin, H., Meunier, D., Gaudreault, M. & B\u00e9gin, P. Assessment of a disease-specific muscular impairment rating scale in myotonic dystrophy. Neurology 56, 336\u2013340 (2001).","journal-title":"Neurology"},{"key":"84520_CR21","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3389\/fnagi.2015.00125","volume":"7","author":"AJ Mateos-Aierdi","year":"2015","unstructured":"Mateos-Aierdi, A. J. et al. Muscle wasting in myotonic dystrophies: a model of premature aging. Front. Aging Neurosci. 7, 125 (2015).","journal-title":"Front. Aging Neurosci."},{"key":"84520_CR22","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1006\/nimg.2002.1267","volume":"17","author":"S-K Song","year":"2002","unstructured":"Song, S.-K. et al. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage 17, 1429\u20131436 (2002).","journal-title":"Neuroimage"},{"key":"84520_CR23","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1002\/mrm.20774","volume":"55","author":"S-W Sun","year":"2006","unstructured":"Sun, S.-W. et al. Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magn. Reson. Med. 55, 302\u2013308 (2006).","journal-title":"Magn. Reson. Med."},{"key":"84520_CR24","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.nurt.2007.05.011","volume":"4","author":"AL Alexander","year":"2007","unstructured":"Alexander, A. L., Lee, J. E., Lazar, M. & Field, A. S. Diffusion tensor imaging of the brain. Neurotherapeutics 4, 316\u2013329 (2007).","journal-title":"Neurotherapeutics"},{"key":"84520_CR25","unstructured":"R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria, 2016)."},{"key":"84520_CR26","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116\u20131128 (2006).","journal-title":"Neuroimage"},{"key":"84520_CR27","first-page":"61","volume":"277","author":"M Gomes-Pereira","year":"2004","unstructured":"Gomes-Pereira, M., Bidichandani, S. I. & Monckton, D. G. Analysis of unstable triplet repeats using small-pool polymerase chain reaction. Methods Mol. Biol. 277, 61\u201376 (2004).","journal-title":"Methods Mol. Biol."},{"key":"84520_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/hmg\/4.1.1","volume":"4","author":"DG Monckton","year":"1995","unstructured":"Monckton, D. G., Wong, L. J., Ashizawa, T. & Caskey, C. T. Somatic mosaicism, germline expansions, germline reversions and intergenerational reductions in myotonic dystrophy males: small pool PCR analyses. Hum. Mol. Genet. 4, 1\u20138 (1995).","journal-title":"Hum. Mol. Genet."},{"key":"84520_CR29","doi-asserted-by":"publisher","first-page":"3558","DOI":"10.1093\/hmg\/dds185","volume":"21","author":"F Morales","year":"2012","unstructured":"Morales, F. et al. Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severity. Hum. Mol. Genet. 21, 3558\u20133567 (2012).","journal-title":"Hum. Mol. Genet."},{"key":"84520_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/protex.2018.089","author":"M Ciosi","year":"2018","unstructured":"Ciosi, M. et al. Library preparation and MiSeq sequencing for the genotyping-by-sequencing of the Huntington disease HTT exon one trinucleotide repeat and the quantification of somatic mosaicism. Protocol Exchange https:\/\/doi.org\/10.1038\/protex.2018.089 (2018).","journal-title":"Protocol Exchange"},{"key":"84520_CR31","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1101\/gr.4086505","volume":"15","author":"B Giardine","year":"2005","unstructured":"Giardine, B. et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 15, 1451\u20131455 (2005).","journal-title":"Genome Res."},{"key":"84520_CR32","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1002\/mus.24725","volume":"53","author":"C Heatwole","year":"2016","unstructured":"Heatwole, C. et al. Myotonic dystrophy health index: Correlations with clinical tests and patient function. Muscle Nerve 53, 183\u2013190 (2016).","journal-title":"Muscle Nerve"},{"key":"84520_CR33","doi-asserted-by":"publisher","DOI":"10.3233\/JND-190397","author":"E van der Plas","year":"2019","unstructured":"van der Plas, E. et al. Brain structural features of myotonic dystrophy type 1 and their relationship with Ctg repeats. J. Neuromuscul. Dis. https:\/\/doi.org\/10.3233\/JND-190397 (2019).","journal-title":"J. Neuromuscul. Dis."},{"key":"84520_CR34","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/S1053-8119(03)00336-7","volume":"20","author":"JLR Andersson","year":"2003","unstructured":"Andersson, J. L. R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20, 870\u2013888 (2003).","journal-title":"Neuroimage"},{"key":"84520_CR35","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.neuroimage.2015.10.019","volume":"125","author":"JLR Andersson","year":"2016","unstructured":"Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063\u20131078 (2016).","journal-title":"Neuroimage"},{"key":"84520_CR36","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","volume":"54","author":"BB Avants","year":"2011","unstructured":"Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033\u20132044 (2011).","journal-title":"Neuroimage"},{"key":"84520_CR37","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","volume":"80","author":"DC Van Essen","year":"2013","unstructured":"Van Essen, D. C. et al. The WU-Minn human connectome project: An overview. Neuroimage 80, 62\u201379 (2013).","journal-title":"Neuroimage"},{"issue":"Supplement 1","key":"84520_CR38","doi-asserted-by":"publisher","first-page":"S102","DOI":"10.1016\/S1053-8119(09)70884-5","volume":"47","author":"VS Fonov","year":"2009","unstructured":"Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R. & Collins, D. L. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47(Supplement 1), S102 (2009).","journal-title":"Neuroimage"},{"key":"84520_CR39","doi-asserted-by":"publisher","first-page":"29","DOI":"10.3389\/fninf.2013.00029","volume":"7","author":"E Young Kim","year":"2013","unstructured":"Young Kim, E. & Johnson, H. J. Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration. Front. Neuroinform. 7, 29 (2013).","journal-title":"Front. Neuroinform."},{"key":"84520_CR40","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TPAMI.2012.143","volume":"35","author":"H Wang","year":"2013","unstructured":"Wang, H. et al. Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35, 611\u2013623 (2013).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"84520_CR41","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968\u2013980 (2006).","journal-title":"Neuroimage"},{"key":"84520_CR42","volume-title":"MRI Atlas of Human White Matter","author":"S Mori","year":"2005","unstructured":"Mori, S., Wakana, S., van Zijl, P. C. M. & Nagae-Poetscher, L. M. MRI Atlas of Human White Matter (Elsevier, Amsterdam, 2005)."},{"key":"84520_CR43","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1016\/j.neuroimage.2007.02.049","volume":"36","author":"S Wakana","year":"2007","unstructured":"Wakana, S. et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36, 630\u2013644 (2007).","journal-title":"Neuroimage"},{"key":"84520_CR44","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1016\/j.neuroimage.2007.07.053","volume":"39","author":"K Hua","year":"2008","unstructured":"Hua, K. et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39, 336\u2013347 (2008).","journal-title":"Neuroimage"},{"key":"84520_CR45","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","volume":"8","author":"WE Johnson","year":"2007","unstructured":"Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118\u2013127 (2007).","journal-title":"Biostatistics"},{"key":"84520_CR46","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.neuroimage.2017.08.047","volume":"161","author":"J-P Fortin","year":"2017","unstructured":"Fortin, J.-P. et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161, 149\u2013170 (2017).","journal-title":"Neuroimage"},{"key":"84520_CR47","volume-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing (J. R. Stat, Soc, 1995)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-84520-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-84520-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-84520-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T04:34:40Z","timestamp":1670042080000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-84520-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,1]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["84520"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-84520-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,1]]},"assertion":[{"value":"30 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"DGM has been a scientific consultant and\/or received an honoraria\/stock options from AMO Pharma, Biogen Idec, Charles River, LoQus23, Small Molecule RNA, Triplet Therapeutics and Vertex Pharmaceuticals and had research contracts with AMO Pharma and Vertex Pharmaceuticals. DGM has received research grants\/contracts from the European Union, CHDI, European Huntington Disease Network, Huntington Disease Society of America, National Institute of Health, Muscular Dystrophy UK and the Myotonic Dystrophy Support Group. DGM is on the Scientific Advisory Board of Myotonic (formerly the Myotonic Dystrophy Foundation) and EuroDyMA (European <i>Dystrophia Myotonica<\/i> Association), is a scientific advisor to the Myotonic Dystrophy Support Group and is a vice president of Muscular Dystrophy UK. TRK, EvdP, LG, SAC, VM, RKS, and PCN report no competing interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4886"}}