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Thalamic atrophy in patients with pure hereditary spastic paraplegia type 4

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Abstract

SPG4 is an autosomal dominant pure form of hereditary spastic paraplegia (HSP) caused by mutations in the SPAST gene. HSP is considered an upper motor neuron disorder characterized by progressive spasticity and weakness of the lower limbs caused by degeneration of the corticospinal tract. In other neurodegenerative motor disorders, the thalamus and basal ganglia are affected, with a considerable impact on disease progression. However, only a few works have studied these brain structures in HSP, mainly in complex forms of this disease. Our research aims to detect potential alterations in the volume and shape of the thalamus and various basal ganglia structures by comparing 12 patients with pure HSP and 18 healthy controls. We used two neuroimaging procedures: automated segmentation of the subcortical structures (thalamus, hippocampus, caudate nucleus, globus pallidus, and putamen) in native space and shape analysis of the structures. We found a significant reduction in thalamic volume bilaterally, as well as an inward deformation, mainly in the sensory-motor thalamic regions in patients with pure HSP and a mutation in SPG4. We also observed a significant negative correlation between the shape of the thalamus and clinical scores (the Spastic Paraplegia Rating Scale score and disease duration). Moreover, we found a ‘Group × Age’ interaction that was closely related to the severity of the disease. No differences in volume or in shape were found in the remaining subcortical structures studied. Our results suggest that changes in structure of the thalamus could be an imaging biomarker of disease progression in pHSP.

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References

  1. Harding AE (1983) Classification of the hereditary ataxias and paraplegias. Lancet 321:1151–1155. https://doi.org/10.1016/S0140-6736(83)92879-9

    Article  Google Scholar 

  2. Fink JK (2013) Hereditary spastic paraplegia: Clinico-pathologic features and emerging molecular mechanisms. Acta Neuropathol 126:307–328. https://doi.org/10.1007/s00401-013-1115-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lo Giudice T, Lombardi F, Santorelli FM et al (2014) Hereditary spastic paraplegia: Clinical-genetic characteristics and evolving molecular mechanisms. Exp Neurol 261:518–539. https://doi.org/10.1016/j.expneurol.2014.06.011

    Article  CAS  PubMed  Google Scholar 

  4. de Souza PVS, de Rezende Pinto WBV, de Rezende Batistella GN et al (2017) Hereditary spastic paraplegia: clinical and genetic hallmarks. Cerebellum 16:525–551. https://doi.org/10.1007/s12311-016-0803-z

    Article  CAS  PubMed  Google Scholar 

  5. Kadnikova VA, Ryzhkova OP, Rudenskaya GE, Polyakov AV (2019) Molecular genetic diversity and dna diagnostics of hereditary spastic paraplegia. Biol Bull Rev 9:145–156. https://doi.org/10.1134/s2079086419020063

    Article  Google Scholar 

  6. Da Graça FF, De Rezende TJR, Vasconcellos LFR et al (2019) Neuroimaging in hereditary spastic paraplegias: Current use and future perspectives. Front Neurol 10:1117. https://doi.org/10.3389/fneur.2018.01117

    Article  Google Scholar 

  7. Rezende TJR, De Albuquerque M, Lamas GM et al (2015) Multimodal MRI-based study in patients with SPG4 mutations. PLoS ONE 10:e0117666. https://doi.org/10.1371/journal.pone.0117666

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Schieving JH, de Bot ST, van de Pol LA et al (2019) De novo SPAST mutations may cause a complex SPG4 phenotype. Brain 142:e31. https://doi.org/10.1093/brain/awz140

    Article  PubMed  Google Scholar 

  9. Solowska JM, Baas PW (2015) Hereditary spastic paraplegia SPG4: What is known and not known about the disease. Brain 138:2471–2484. https://doi.org/10.1093/brain/awv178

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hazan J, Fonknechten N, Mavel D et al (1999) Spastin, a new AAA protein, is altered in the most frequent form of autosomal dominant spastic paraplegia. Nat Genet 23:296–303. https://doi.org/10.1038/15472

    Article  CAS  PubMed  Google Scholar 

  11. Liao X, Huang M, Xing W et al (2018) Resting state fMRI studies in SPG4-linked hereditary spastic paraplegia. J Neurol Sci 384:1–6. https://doi.org/10.1016/j.jns.2017.10.048

    Article  PubMed  Google Scholar 

  12. Rucco R, Liparoti M, Jacini F et al (2019) Mutations in the SPAST gene causing hereditary spastic paraplegia are related to global topological alterations in brain functional networks. Neurol Sci 40:979–984. https://doi.org/10.1007/s10072-019-3725-y

    Article  PubMed  PubMed Central  Google Scholar 

  13. Scheuer KH, Nielsen JE, Krabbe K et al (2005) Reduced regional cerebral blood flow in SPG4-linked hereditary spastic paraplegia. J Neurol Sci 235:23–32. https://doi.org/10.1016/j.jns.2005.03.051

    Article  CAS  PubMed  Google Scholar 

  14. Duning T, Warnecke T, Schirmacher A et al (2010) Specific pattern of early white-matter changes in pure hereditary spastic paraplegia. Mov Disord 25:1986–1992. https://doi.org/10.1002/mds.23211

    Article  PubMed  Google Scholar 

  15. Lindig T, Bender B, Hauser TK et al (2015) Gray and white matter alterations in hereditary spastic paraplegia type SPG4 and clinical correlations. J Neurol 262:1961–1971. https://doi.org/10.1007/s00415-015-7791-7

    Article  PubMed  Google Scholar 

  16. Kassubek J, Juengling FD, Baumgartner A et al (2007) Different regional brain volume loss in pure and complicated hereditary spastic paraparesis: A voxel-based morphometric study. Amyotroph Lateral Scler 8:328–336. https://doi.org/10.1080/17482960701500718

    Article  PubMed  Google Scholar 

  17. Unrath A, Müller HP, Riecker A et al (2010) Whole brain-based analysis of regional white matter tract alterations in rare motor neuron diseases by diffusion tensor imaging. Hum Brain Mapp 31:1727–1740. https://doi.org/10.1002/hbm.20971

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lin JZ, Zheng HH, Ma QL et al (2020) Cortical damage associated with cognitive and motor impairment in hereditary spastic paraplegia: evidence of a novel SPAST mutation. Front Neurol 11:399. https://doi.org/10.3389/fneur.2020.00399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Agosta F, Rocca MA, Valsasina P et al (2009) A longitudinal diffusion tensor MRI study of the cervical cord and brain in amyotrophic lateral sclerosis patients. J Neurol Neurosurg Psychiatry 80:53–55. https://doi.org/10.1136/jnnp.2008.154252

    Article  CAS  PubMed  Google Scholar 

  20. Bede P, Elamin M, Byrne S et al (2013) Basal ganglia involvement in amyotrophic lateral sclerosis. Neurology 81:2107–2115. https://doi.org/10.1212/01.wnl.0000437313.80913.2c

    Article  PubMed  Google Scholar 

  21. Menke RAL, Proudfoot M, Talbot K, Turner MR (2018) The two-year progression of structural and functional cerebral MRI in amyotrophic lateral sclerosis. NeuroImage Clin 17:953–961. https://doi.org/10.1016/j.nicl.2017.12.025

    Article  CAS  PubMed  Google Scholar 

  22. Hehr U, Bauer P, Winner B et al (2007) Long-term course and mutational spectrum of spatacsin-linked spastic paraplegia. Ann Neurol 62:656–665. https://doi.org/10.1002/ana.21310

    Article  CAS  PubMed  Google Scholar 

  23. Örlén H, Melberg A, Raininko R et al (2009) SPG11 mutations cause Kjellin syndrome, a hereditary spastic paraplegia with thin corpus callosum and central retinal degeneration. Am J Med Genet Part B Neuropsychiatr Genet 150:984–992. https://doi.org/10.1002/ajmg.b.30928

    Article  CAS  Google Scholar 

  24. Samaranch L, Riverol M, Masdeu JC et al (2008) SPG11 compound mutations in spastic paraparesis with thin corpus callosum. Neurology 71:332–336. https://doi.org/10.1212/01.wnl.0000319646.23052.d1

    Article  CAS  PubMed  Google Scholar 

  25. Ueda M, Katayama Y, Kamiya T et al (1998) Hereditary spastic paraplegia with a thin corpus callosum and thalamic involvement in Japan. Neurology 51:1751–1754. https://doi.org/10.1212/WNL.51.6.1751

    Article  CAS  PubMed  Google Scholar 

  26. Faber I, Martinez ARM, de Rezende TJR et al (2018) SPG11 mutations cause widespread white matter and basal ganglia abnormalities, but restricted cortical damage. NeuroImage Clin 19:848–857. https://doi.org/10.1016/j.nicl.2018.05.031

    Article  PubMed  PubMed Central  Google Scholar 

  27. França MC, Yasuda CL, Pereira FRS et al (2012) White and grey matter abnormalities in patients with SPG11 mutations. J Neurol Neurosurg Psychiatry 83:828–833. https://doi.org/10.1136/jnnp-2011-300129

    Article  PubMed  Google Scholar 

  28. Seidel K, De Vos R, Derksen L et al (2009) Widespread thalamic and cerebellar degeneration in a patient with a complicated hereditary spastic paraplegia (HSP). Ann Anat 191:203–211. https://doi.org/10.1016/j.aanat.2008.11.003

    Article  CAS  PubMed  Google Scholar 

  29. Falco M, Scuderi C, Musumeci S et al (2004) Two novel mutations in the spastin gene (SPG4) found by DHPLC mutation analysis. Neuromuscul Disord 14:750–753. https://doi.org/10.1016/j.nmd.2004.05.017

    Article  PubMed  Google Scholar 

  30. Crippa F, Panzeri C, Martinuzzi A et al (2006) Eight novel mutations in SPG4 in a large sample of patients with hereditary spastic paraplegia. Arch Neurol 63:750–755. https://doi.org/10.1001/archneur.63.5.750

    Article  PubMed  Google Scholar 

  31. Álvarez V, Sánchez-Ferrero E, Beetz C et al (2010) Mutational spectrum of the SPG4 (SPAST) and SPG3A (ATL1) genes in Spanish patients with hereditary spastic paraplegia. BMC Neurol 10:89. https://doi.org/10.1186/1471-2377-10-89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Schickel J, Beetz C, Frömmel C et al (2006) Unexpected pathogenic mechanism of a novel mutation in the coding sequence of SPG4 (spastin). Neurology 66:421–423. https://doi.org/10.1212/01.wnl.0000196468.01815.55

    Article  CAS  PubMed  Google Scholar 

  33. Depienne C, Fedirko E, Faucheux JM et al (2007) A de novo SPAST mutation leading to somatic mosaicism is associated with a later age at onset in HSP. Neurogenetics 8:231–233. https://doi.org/10.1007/s10048-007-0090-4

    Article  PubMed  Google Scholar 

  34. Schüle R, Holland-Letz T, Klimpe S et al (2006) The spastic paraplegia rating scale (SPRS): A reliable and valid measure of disease severity. Neurology 67:430–434. https://doi.org/10.1212/01.wnl.0000228242.53336.90

    Article  PubMed  Google Scholar 

  35. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198. https://doi.org/10.1016/0022-3956(75)90026-6

    Article  CAS  PubMed  Google Scholar 

  36. Schmitz-Hübsch T, Du Montcel ST, Baliko L et al (2006) Scale for the assessment and rating of ataxia: Development of a new clinical scale. Neurology 66:1717–1720. https://doi.org/10.1212/01.wnl.0000219042.60538.92

    Article  PubMed  Google Scholar 

  37. Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051

    Article  PubMed  Google Scholar 

  38. Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45–57. https://doi.org/10.1109/42.906424

    Article  CAS  PubMed  Google Scholar 

  39. Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56:907–922. https://doi.org/10.1016/j.neuroimage.2011.02.046

    Article  PubMed  Google Scholar 

  40. Tournier JD, Smith R, Raffelt D et al (2019) MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202:551739. https://doi.org/10.1016/j.neuroimage.2019.116137

    Article  Google Scholar 

  41. Veraart J, Fieremans E, Novikov DS (2016) Diffusion MRI noise mapping using random matrix theory. Magn Reson Med 76:1582–1593. https://doi.org/10.1002/mrm.26059

    Article  CAS  PubMed  Google Scholar 

  42. Veraart J, Novikov DS, Christiaens D et al (2016) Denoising of diffusion MRI using random matrix theory. Neuroimage 142:394–406. https://doi.org/10.1016/j.neuroimage.2016.08.016

    Article  PubMed  Google Scholar 

  43. Kellner E, Dhital B, Kiselev VG, Reisert M (2016) Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 76:1574–1581. https://doi.org/10.1002/mrm.26054

    Article  PubMed  Google Scholar 

  44. Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: Improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320. https://doi.org/10.1109/TMI.2010.2046908

    Article  PubMed  PubMed Central  Google Scholar 

  45. 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. https://doi.org/10.1016/j.neuroimage.2010.09.025

    Article  PubMed  Google Scholar 

  46. Andersson JLR, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125:1063–1078. https://doi.org/10.1016/j.neuroimage.2015.10.019

    Article  PubMed  Google Scholar 

  47. Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35:1459–1472. https://doi.org/10.1016/j.neuroimage.2007.02.016

    Article  PubMed  Google Scholar 

  48. Tournier JD, Calamante F, Connelly A (2013) Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed 26:1775–1786. https://doi.org/10.1002/nbm.3017

    Article  PubMed  Google Scholar 

  49. Behrens TEJ, Johansen-Berg H, Woolrich MW et al (2003) Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 6:750–757. https://doi.org/10.1038/nn1075

    Article  CAS  PubMed  Google Scholar 

  50. Mayka MA, Corcos DM, Leurgans SE, Vaillancourt DE (2006) Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: A meta-analysis. Neuroimage 31:1453–1474. https://doi.org/10.1016/j.neuroimage.2006.02.004

    Article  PubMed  Google Scholar 

  51. Calamante F, Tournier JD, Heidemann RM et al (2011) Track density imaging (TDI): Validation of super resolution property. Neuroimage 56:1259–1266. https://doi.org/10.1016/j.neuroimage.2011.02.059

    Article  PubMed  Google Scholar 

  52. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  Google Scholar 

  53. Winkler AM, Ridgway GR, Webster MA et al (2014) Permutation inference for the general linear model. Neuroimage 92:381–397. https://doi.org/10.1016/j.neuroimage.2014.01.060

    Article  PubMed  Google Scholar 

  54. Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44:83–98. https://doi.org/10.1016/j.neuroimage.2008.03.061

    Article  PubMed  Google Scholar 

  55. Kamps S, van den Heuvel OA, van der Werf YD et al (2019) Smaller subcortical volume in Parkinson patients with rapid eye movement sleep behavior disorder. Brain Imaging Behav 13:1352–1360. https://doi.org/10.1007/s11682-018-9939-4

    Article  PubMed  Google Scholar 

  56. Schönecker S, Neuhofer C, Otto M et al (2018) Atrophy in the thalamus but not cerebellum is specific for C9orf72 FTD and ALS patients - An atlas-based volumetric MRI study. Front Aging Neurosci 10:45. https://doi.org/10.3389/fnagi.2018.00045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zou L, Song Y, Zhou X et al (2019) Regional morphometric abnormalities and clinical relevance in Wilson’s disease. Mov Disord 34:545–554. https://doi.org/10.1002/mds.27641

    Article  PubMed  Google Scholar 

  58. Carrera E, Bogousslavsky J (2006) The thalamus and behavior: Effects of anatomically distinct strokes. Neurology 66:1817–1823. https://doi.org/10.1212/01.wnl.0000219679.95223.4c

    Article  PubMed  Google Scholar 

  59. Tucker HR, Mahoney E, Chhetri A et al (2019) Deep brain stimulation of the ventroanterior and ventrolateral thalamus improves motor function in a rat model of Parkinson’s disease. Exp Neurol 317:155–167. https://doi.org/10.1016/j.expneurol.2019.03.008

    Article  PubMed  Google Scholar 

  60. Giraldo-Chica M, Rogers BP, Damon SM et al (2018) Prefrontal-thalamic anatomical connectivity and executive cognitive function in schizophrenia. Biol Psychiatry 83:509–517. https://doi.org/10.1016/j.biopsych.2017.09.022

    Article  PubMed  Google Scholar 

  61. Schoonheim MM, Hulst HE, Brandt RB et al (2015) Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. Neurology 84:776–783. https://doi.org/10.1212/WNL.0000000000001285

    Article  PubMed  Google Scholar 

  62. Haber SN, Calzavara R (2009) The cortico-basal ganglia integrative network: The role of the thalamus. Brain Res Bull 78:69–74. https://doi.org/10.1016/j.brainresbull.2008.09.013

    Article  PubMed  Google Scholar 

  63. Ross DT, Ebner FF (1990) Thalamic retrograde degeneration following cortical injury: An excitotoxic process? Neuroscience 35:525–550. https://doi.org/10.1016/0306-4522(90)90327-Z

    Article  CAS  PubMed  Google Scholar 

  64. Sørensen JC, Dalmau I, Zimmer J, Finsen B (1996) Microglial reactions to retrograde degeneration of tracer-identified thalamic neurons after frontal sensorimotor cortex lesions in adult rats. Exp Brain Res 112:203–212. https://doi.org/10.1007/bf00227639

    Article  PubMed  Google Scholar 

  65. Ferrer I, Olive M, Rivera R et al (1995) Hereditary spastic paraparesis with dementia, amyotrophy and peripheral neuropathy. A neuropathological study Neuropathol Appl Neurobiol 21:255–261. https://doi.org/10.1111/j.1365-2990.1995.tb01057.x

    Article  CAS  PubMed  Google Scholar 

  66. Dihné M, Grommes C, Lutzenburg M et al (2002) Different mechanisms of secondary neuronal damage in thalamic nuclei after focal cerebral ischemia in rats. Stroke 33:3006–3011. https://doi.org/10.1161/01.STR.0000039406.64644.CB

    Article  PubMed  Google Scholar 

  67. Haque ME, Gabr RE, Hasan KM et al (2019) Ongoing secondary degeneration of the limbic system in patients with ischemic stroke: a longitudinal MRI study. Front Neurol. https://doi.org/10.3389/fneur.2019.00154

    Article  PubMed  PubMed Central  Google Scholar 

  68. Kersbergen KJ, De Vries LS, Groenendaal F et al (2015) Corticospinal tract injury precedes thalamic volume reduction in preterm infants with cystic periventricular leukomalacia. J Pediatr 167:260-268.e3. https://doi.org/10.1016/j.jpeds.2015.05.013

    Article  PubMed  PubMed Central  Google Scholar 

  69. Ruano L, Melo C, Silva MC, Coutinho P (2014) The global epidemiology of hereditary ataxia and spastic paraplegia: A systematic review of prevalence studies. Neuroepidemiology 42:174–183

    Article  PubMed  Google Scholar 

  70. Babalola KO, Patenaude B, Aljabar P et al (2009) An evaluation of four automatic methods of segmenting the subcortical structures in the brain. Neuroimage 47:1435–1447. https://doi.org/10.1016/j.neuroimage.2009.05.029

    Article  PubMed  Google Scholar 

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Acknowledgements

All the authors wish to express our appreciation for the invaluable contribution of the late Dr. Santiago Reig to this study. We are grateful to the patients for their collaboration and to the Asociación Española de Paraparesia Espástica Familiar (AEPEF) for support with patient recruitment.

Funding

This work was supported by Ministerio de Ciencia, Innovación y Universidades and by Instituto de Salud Carlos III projects PS09/01830; PS09/01685; PS09/00839. The ASPIDE project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 801091. The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV‐2015‐0505). S. Carmona, funded by Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, co‐ funded by European Social Fund “Investing in your future” (CP16/00096).

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Author notes

  1. Francisco Grandas and Manuel Desco Shared co-last authorship.

Authors and Affiliations

  1. Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain

    Francisco J. Navas-Sánchez, Alberto Fernández-Pena, Daniel Martín de Blas, Luís Marcos-Vidal, Juan A. Guzmán-de-Villoria, Julia Romero, Susanna Carmona & Manuel Desco

  2. Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain

    Francisco J. Navas-Sánchez, Luís Marcos-Vidal, Juan A. Guzmán-de-Villoria, Susanna Carmona & Manuel Desco

  3. Department of Psychiatry, Centre Hospitalier Universitaire Vaudois, Prilly, Switzerland

    Yasser Alemán-Gómez

  4. Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

    Yasser Alemán-Gómez

  5. Medical Image Analysis Laboratory (MIAL), Centre D’Imagerie BioMédicale (CIBM), Lausanne, Switzerland

    Yasser Alemán-Gómez & Luís Marcos-Vidal

  6. Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain

    Manuel Desco

  7. Servicio de Radiodiagnóstico, Hospital General Universitario Gregorio Marañón, Madrid, Spain

    Juan A. Guzmán-de-Villoria & Julia Romero

  8. Hospital Madrid Montepríncipe, Madrid, Spain

    Pilar Fernández-García

  9. Servicio de Neurología, Hospital General Universitario Gregorio Marañón, Madrid, Spain

    Irene Catalina, José L. Muñoz-Blanco & Francisco Grandas

  10. Hospital Ruber Internacional, Servicio de Neurología, Madrid, Spain

    Laura Lillo

  11. Hospital Universitario Fundación Alcorcón, Servicio de Neurología Alcorcón, Madrid, Spain

    Laura Lillo

  12. Instituto de Investigación Sanitaria, Santiago de Compostela, Spain

    Beatriz Quintáns & María-Jesús Sobrido

  13. Laboratorio Biomolecular, Cuenca, Ecuador

    Andrés Ordoñez-Ugalde

  14. Unidad de Genética y Molecular, Hospital de Especialidades José Carrasco Arteaga, Cuenca, Ecuador

    Andrés Ordoñez-Ugalde

  15. Neurogenetics Group, FPGMX-IDIS, Santiago de Compostela, Spain

    Andrés Ordoñez-Ugalde

  16. Hospital Clínico Universitario de A Coruña, SERGAS, Santiago de Compostela, Spain

    María-Jesús Sobrido

  17. Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-U711), Madrid, Spain

    Beatriz Quintáns

  18. Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela, Spain

    Beatriz Quintáns

  19. Departamento de Neurología, Hospital Clínico Universitario de Santiago de Compostela, A Coruña, Santiago de Compostela, Spain

    Julio Pardo

  20. Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

    Manuel Desco

Authors
  1. Francisco J. Navas-Sánchez
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  2. Alberto Fernández-Pena
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  3. Daniel Martín de Blas
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  9. Irene Catalina
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  10. Laura Lillo
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  11. José L. Muñoz-Blanco
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  13. Beatriz Quintáns
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  14. Julio Pardo
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  15. María-Jesús Sobrido
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  17. Francisco Grandas
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  18. Manuel Desco
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Corresponding author

Correspondence to Francisco J. Navas-Sánchez.

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Conflicts of interest

None of the authors have any conflict of interest to disclose.

Research involving human participants

The study was approved by the Ethics and Clinical Research Board of Hospital Gregorio Marañón. Written informed consent was obtained from all patients before their participation in the study.

Author roles in the manuscript

(1. Research project: A. Conception, B. Organization, C. Execution; 2. Statistical Analysis: A. Design, B. Execution, C. Review and Critique; 3. Manuscript Preparation: A. Writing of the first draft, B. Review and Critique.): Francisco J. Navas-Sánchez: 1B, 1C, 2A, 2B, 3A, 3B; Alberto Fernández-Pena: 1C, 2C, 3B; Daniel Martín de Blas: 2C, 3B; Yasser Alemán-Gómez: 1B, 2C, 3B; Luís Marcos-Vidal: 2C, 3B; Juan A. Guzmán-de-Villoria: 1C, 3B; Pilar Fernández-García: 1C, 3B; Julia Romero: 1C, 3B; Irene Catalina: 1C, 3B; Laura Lillo: 1C, 3B; José L. Muñoz-Blanco: 1C, 3B; Andrés Ordoñez-Ugalde: 1C, 3B; Beatriz Quintáns: 1C, 3B; Julio Pardo: 1C, 3B; María-Jesús Sobrido: 1C, 3B; Susanna Carmona: 1C, 3B; Francisco Grandas: 1A, 1B, 1C, 2C, 3B; Manuel Desco: 1A, 1B, 2C, 3B;

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Navas-Sánchez, F.J., Fernández-Pena, A., Martín de Blas, D. et al. Thalamic atrophy in patients with pure hereditary spastic paraplegia type 4. J Neurol 268, 2429–2440 (2021). https://doi.org/10.1007/s00415-020-10387-4

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  • DOI: https://doi.org/10.1007/s00415-020-10387-4

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