Skip to main content
SpringerLink
Log in

The clinical and radiological profile of primary lateral sclerosis: a population-based study

  • Original Communication
  • Published:
Journal of Neurology Aims and scope Submit manuscript

Abstract

Background

Primary lateral sclerosis is a progressive upper-motor-neuron disorder associated with markedly longer survival than ALS. In contrast to ALS, the genetic susceptibility, histopathological profile and imaging signature of PLS are poorly characterised. Suspected PLS patients often face considerable diagnostic delay and prognostic uncertainty.

Objective

To characterise the distinguishing clinical, genetic and imaging features of PLS in contrast to ALS and healthy controls.

Methods

A prospective population-based study was conducted with 49 PLS patients, 100 ALS patients and 100 healthy controls using genetic profiling, standardised clinical assessments and neuroimaging. Whole-brain and region-of-interest analyses were undertaken to evaluate patterns of grey and white matter degeneration.

Results

In PLS, disease burden in the motor cortex is more medial than in ALS consistent with its lower limb symptom-predominance. PLS is associated with considerable cerebellar white and grey matter degeneration and the extra-motor profile of PLS includes marked insular, inferior frontal and left pars opercularis pathology. Contrary to ALS, PLS spares the postcentral gyrus. The body and splenium of the corpus callosum are preferentially affected in PLS, in contrast to the genu involvement observed in ALS. Clinical measures show anatomically meaningful correlations with imaging metrics in a somatotopic distribution. PLS patients tested negative for C9orf72 repeat expansions, known ALS and HSP-associated genes.

Conclusions

Multiparametric imaging in PLS highlights disease-specific motor and extra-motor involvement distinct from ALS. In a condition where limited post-mortem data are available, imaging offers invaluable pathological insights. Anatomical correlations with clinical metrics confirm the biomarker potential of quantitative neuroimaging in PLS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

AD:

Axial diffusivity

ALS:

Amyotrophic lateral sclerosis

C9orf72:

Chromosome 9 open reading frame 72

CST:

Corticospinal tract

DTI:

Diffusion tensor imaging

EPI:

Echo-planar imaging

FA:

Fractional anisotropy

FDR:

False discovery rate

FTD:

Frontotemporal dementia

FOV:

Field-of-view

FWE:

Familywise error

GM:

Grey matter

HARDI:

High angular resolution diffusion imaging

HC:

Healthy control

HSP:

Hereditary spastic paraplegia

LMN:

Lower motor neuron

MD:

Mean diffusivity

MND:

Motor neuron disease

MR:

Magnetic resonance

PMC:

Primary motor cortex

QBI:

q-Ball imaging

RE:

Repeat expansion

RD:

Radial diffusivity

SC:

Spinal cord

TBSS:

Tract-based spatial statistics

TE:

Echo time

TFCE:

Threshold-free cluster enhancement

TR:

Repetition time

UMN:

Upper motor neuron

VBM:

Voxel-based morphometry

WM:

White matter

References

  1. Le Forestier N, Maisonobe T, Piquard A, Rivaud S, Crevier-Buchman L, Salachas F, Pradat PF, Lacomblez L, Meininger V (2001) Does primary lateral sclerosis exist? A study of 20 patients and a review of the literature. Brain J Neurol 124(Pt 10):1989–1999

    Article  Google Scholar 

  2. Singer MA, Kojan S, Barohn RJ, Herbelin L, Nations SP, Trivedi JR, Jackson CE, Burns DK, Boyer PJ, Wolfe GI (2005) Primary lateral sclerosis: clinical and laboratory features in 25 patients. J Clin Neuromusc Dis 7(1):1–9. https://doi.org/10.1097/01.cnd.0000176974.61136.45

    Article  Google Scholar 

  3. Almeida V, de Carvalho M, Scotto M, Pinto S, Pinto A, Ohana B, Swash M (2013) Primary lateral sclerosis: predicting functional outcome. Amyotroph Lateral Scler Frontotemporal Degener 14(2):141–145

    Article  Google Scholar 

  4. Gordon PH, Cheng B, Katz IB, Pinto M, Hays AP, Mitsumoto H, Rowland LP (2006) The natural history of primary lateral sclerosis. Neurology 66(5):647–653

    Article  CAS  Google Scholar 

  5. Floeter MK, Mills R (2009) Progression in primary lateral sclerosis: a prospective analysis. Amyotroph Lateral Sclerosis 10(5–6):339–346

    Article  Google Scholar 

  6. Finegan E, Chipika RH, Shing SLH, Hardiman O, Bede P (2019) Primary lateral sclerosis: a distinct entity or part of the ALS spectrum? Amyotroph Lateral Sclerosis Frontotemporal Degener. https://doi.org/10.1080/21678421.2018.1550518

    Article  Google Scholar 

  7. Iwata NK, Kwan JY, Danielian LE, Butman JA, Tovar-Moll F, Bayat E, Floeter MK (2011) White matter alterations differ in primary lateral sclerosis and amyotrophic lateral sclerosis. Brain J Neurol 134(9):2642–2655. https://doi.org/10.1093/brain/awr178

    Article  Google Scholar 

  8. Müller HP, Gorges M, Kassubek R, Dorst J, Ludolph AC, Kassubek J (2018) Identical patterns of cortico-efferent tract involvement in primary lateral sclerosis and amyotrophic lateral sclerosis: a tract of interest-based MRI study. NeuroImage Clin 18:762–769. https://doi.org/10.1016/j.nicl.2018.03.018

    Article  PubMed  PubMed Central  Google Scholar 

  9. Unrath A, Muller H-P, Riecker A, Ludolph AC, Sperfeld A-D, Kassubek J (2010) Whole brain-based analysis of regional white matter tract alterations in rare motor neuron diseases by diffusion tensor imaging. Hum Brain Mapp 31(11):1727–1740

    PubMed  Google Scholar 

  10. Agosta F, Galantucci S, Riva N, Chio A, Messina S, Iannaccone S, Calvo A, Silani V, Copetti M, Falini A, Comi G, Filippi M (2014) Intrahemispheric and interhemispheric structural network abnormalities in PLS and ALS. Hum Brain Mapp 35(4):1710–1722. https://doi.org/10.1002/hbm.22286

    Article  PubMed  Google Scholar 

  11. Butman JA, Floeter MK (2007) Decreased thickness of primary motor cortex in primary lateral sclerosis. Am J Neuroradiol 28(1):87–91

    CAS  PubMed  Google Scholar 

  12. Schuster C, Kasper E, Machts J, Bittner D, Kaufmann J, Benecke R, Teipel S, Vielhaber S, Prudlo J (2013) Focal thinning of the motor cortex mirrors clinical features of amyotrophic lateral sclerosis and their phenotypes: a neuroimaging study. J Neurol 260(11):2856–2864

    Article  Google Scholar 

  13. Paganoni S, Alshikho MJ, Zürcher NR, Cernasov P, Babu S, Loggia ML, Chan J, Chonde DB, Garcia DI, Catana C, Mainero C, Rosen BR, Cudkowicz ME, Hooker JM, Atassi N (2018) Imaging of glia activation in people with primary lateral sclerosis. NeuroImage Clin 17:347–353. https://doi.org/10.1016/j.nicl.2017.10.024

    Article  PubMed  Google Scholar 

  14. Bede P, Hardiman O (2014) Lessons of ALS imaging: pitfalls and future directions—a critical review. NeuroImage Clin 4:436–443. https://doi.org/10.1016/j.nicl.2014.02.011

    Article  PubMed  PubMed Central  Google Scholar 

  15. Clark MG, Smallwood Shoukry R, Huang CJ, Danielian LE, Bageac D, Floeter MK (2018) Loss of functional connectivity is an early imaging marker in primary lateral sclerosis. Amyotroph Lateral Sclerosis Frontotemporal Degener 19(7–8):562–569. https://doi.org/10.1080/21678421.2018.1517180

    Article  Google Scholar 

  16. Van Weehaeghe D, Ceccarini J, Delva A, Robberecht W, Van Damme P, Van Laere K (2016) Prospective validation of 18F-FDG brain PET discriminant analysis methods in the diagnosis of amyotrophic lateral sclerosis. J Nucl Med 57(8):1238–1243. https://doi.org/10.2967/jnumed.115.166272

    Article  CAS  PubMed  Google Scholar 

  17. Bede P, Bokde A, Elamin M, Byrne S, McLaughlin RL, Jordan N, Hampel H, Gallagher L, Lynch C, Fagan AJ, Pender N, Hardiman O (2013) Grey matter correlates of clinical variables in amyotrophic lateral sclerosis (ALS): a neuroimaging study of ALS motor phenotype heterogeneity and cortical focality. J Neurol Neurosurg Psychiatry 84(7):766–773. https://doi.org/10.1136/jnnp-2012-302674

    Article  PubMed  Google Scholar 

  18. Bede P, Elamin M, Byrne S, Hardiman O (2013) Sexual dimorphism in ALS: exploring gender-specific neuroimaging signatures. Amyotroph Lateral Sclerosis Frontotemporal Degener. https://doi.org/10.3109/21678421.2013.865749

    Article  Google Scholar 

  19. Floeter MK, Katipally R, Kim MP, Schanz O, Stephen M, Danielian L, Wu T, Huey ED, Meoded A (2014) Impaired corticopontocerebellar tracts underlie pseudobulbar affect in motor neuron disorders. Neurology 83(7):620–627. https://doi.org/10.1212/wnl.0000000000000693

    Article  PubMed  PubMed Central  Google Scholar 

  20. Meoded A, Morrissette AE, Katipally R, Schanz O, Gotts SJ, Floeter MK (2015) Cerebro-cerebellar connectivity is increased in primary lateral sclerosis. NeuroImage Clin 7:288–296

    Article  Google Scholar 

  21. Meoded A, Kwan JY, Peters TL, Huey ED, Danielian LE, Wiggs E, Morrissette A, Wu T, Russell JW, Bayat E, Grafman J, Floeter MK (2013) Imaging findings associated with cognitive performance in primary lateral sclerosis and amyotrophic lateral sclerosis. Dementia Geriatr Cognit Disord Extra 3(1):233–250

    Article  Google Scholar 

  22. Canu E, Agosta F, Galantucci S, Chio A, Riva N, Silani V, Falini A, Comi G, Filippi M (2013) Extramotor damage is associated with cognition in primary lateral sclerosis patients. PLoS One [Electronic Resource] 8(12):e82017

    Article  Google Scholar 

  23. Tu S, Menke RAL, Talbot K, Kiernan MC, Turner MR (2019) Cerebellar tract alterations in PLS and ALS. Amyotroph Lateral Sclerosis Frontotemporal Degener 20(3–4):281–284. https://doi.org/10.1080/21678421.2018.1562554

    Article  Google Scholar 

  24. Bede P, Querin G, Pradat PF (2018) The changing landscape of motor neuron disease imaging: the transition from descriptive studies to precision clinical tools. Curr Opin Neurol 31(4):431–438. https://doi.org/10.1097/wco.0000000000000569

    Article  PubMed  Google Scholar 

  25. Dickson DW, Josephs KA, Amador-Ortiz C (2007) TDP-43 in differential diagnosis of motor neuron disorders. Acta Neuropathol 114(1):71–79

    Article  CAS  Google Scholar 

  26. Kosaka T, Fu YJ, Shiga A, Ishidaira H, Tan CF, Tani T, Koike R, Onodera O, Nishizawa M, Kakita A, Takahashi H (2012) Primary lateral sclerosis: upper-motor-predominant amyotrophic lateral sclerosis with frontotemporal lobar degeneration–immunohistochemical and biochemical analyses of TDP-43. Neuropathol Off J Japan Society Neuropathol 32(4):373–384

    Article  Google Scholar 

  27. Brooks BR, Miller RG, Swash M, Munsat TL, World Federation of Neurology Research Group on Motor Neuron D (2000) El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Sclerosis Other Motor Neuron Disord Off Publ World Feder Neurol Res Group Motor Neuron Dis 1(5):293–299

    CAS  Google Scholar 

  28. Woo JH, Wang S, Melhem ER, Gee JC, Cucchiara A, McCluskey L, Elman L (2014) Linear associations between clinically assessed upper motor neuron disease and diffusion tensor imaging metrics in amyotrophic lateral sclerosis. PLoS One [Electronic Resource] 9(8):e105753

    Article  Google Scholar 

  29. Bohannon RW, Smith MB (1987) Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther 67(2):206–207

    Article  CAS  Google Scholar 

  30. Pinto-Grau M, Burke T, Lonergan K, McHugh C, Mays I, Madden C, Vajda A, Heverin M, Elamin M, Hardiman O, Pender N (2017) Screening for cognitive dysfunction in ALS: validation of the Edinburgh Cognitive and Behavioural ALS Screen (ECAS) using age and education adjusted normative data. Amyotroph Lateral Sclerosis Frontotemporal Degener 18(1–2):99–106. https://doi.org/10.1080/21678421.2016.1249887

    Article  Google Scholar 

  31. Cedarbaum JM, Stambler N, Malta E, Fuller C, Hilt D, Thurmond B, Nakanishi A (1999) The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J Neurol Sci 169(1–2):13–21

    Article  CAS  Google Scholar 

  32. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal 17:10–12

    Google Scholar 

  33. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data Processing S (2009) The sequence alignment/map format and SAMtools. Bioinformatics (Oxford, England) 25(16):2078–2079. https://doi.org/10.1093/bioinformatics/btp352

    Article  CAS  Google Scholar 

  34. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575. https://doi.org/10.1086/519795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6(2):80–92. https://doi.org/10.4161/fly.19695

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Paila U, Chapman BA, Kirchner R, Quinlan AR (2013) GEMINI: integrative exploration of genetic variation and genome annotations. PLoS Comput Biol 9(7):e1003153. https://doi.org/10.1371/journal.pcbi.1003153

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Project Min EALSSC (2018) Project MinE: study design and pilot analyses of a large-scale whole-genome sequencing study in amyotrophic lateral sclerosis. Eur J Hum Genet EJHG 26(10):1537–1546. https://doi.org/10.1038/s41431-018-0177-4

    Article  CAS  Google Scholar 

  38. Abel O, Shatunov A, Jones AR, Andersen PM, Powell JF, Al-Chalabi A (2013) Development of a smartphone app for a genetics website: the amyotrophic lateral sclerosis online genetics database (ALSoD). JMIR mHealth uHealth 1(2):e18–e18. https://doi.org/10.2196/mhealth.2706

    Article  PubMed  PubMed Central  Google Scholar 

  39. Klebe S, Stevanin G, Depienne C (2015) Clinical and genetic heterogeneity in hereditary spastic paraplegias: from SPG1 to SPG72 and still counting. Rev Neurol (Paris) 171(6–7):505–530. https://doi.org/10.1016/j.neurol.2015.02.017

    Article  CAS  Google Scholar 

  40. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB, Tukiainen T, Birnbaum DP, Kosmicki JA, Duncan LE, Estrada K, Zhao F, Zou J, Pierce-Hoffman E, Berghout J, Cooper DN, Deflaux N, DePristo M, Do R, Flannick J, Fromer M, Gauthier L, Goldstein J, Gupta N, Howrigan D, Kiezun A, Kurki MI, Moonshine AL, Natarajan P, Orozco L, Peloso GM, Poplin R, Rivas MA, Ruano-Rubio V, Rose SA, Ruderfer DM, Shakir K, Stenson PD, Stevens C, Thomas BP, Tiao G, Tusie-Luna MT, Weisburd B, Won HH, Yu D, Altshuler DM, Ardissino D, Boehnke M, Danesh J, Donnelly S, Elosua R, Florez JC, Gabriel SB, Getz G, Glatt SJ, Hultman CM, Kathiresan S, Laakso M, McCarroll S, McCarthy MI, McGovern D, McPherson R, Neale BM, Palotie A, Purcell SM, Saleheen D, Scharf JM, Sklar P, Sullivan PF, Tuomilehto J, Tsuang MT, Watkins HC, Wilson JG, Daly MJ, MacArthur DG (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536(7616):285–291. https://doi.org/10.1038/nature19057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR (2015) A global reference for human genetic variation. Nature 526(7571):68–74. https://doi.org/10.1038/nature15393

    Article  CAS  PubMed  Google Scholar 

  42. Stenson PD, Mort M, Ball EV, Evans K, Hayden M, Heywood S, Hussain M, Phillips AD, Cooper DN (2017) The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Hum Genet 136(6):665–677. https://doi.org/10.1007/s00439-017-1779-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Byrne S, Elamin M, Bede P, Shatunov A, Walsh C, Corr B, Heverin M, Jordan N, Kenna K, Lynch C, McLaughlin RL, Iyer PM, O'Brien C, Phukan J, Wynne B, Bokde AL, Bradley DG, Pender N, Al-Chalabi A, Hardiman O (2012) Cognitive and clinical characteristics of patients with amyotrophic lateral sclerosis carrying a C9orf72 repeat expansion: a population-based cohort study. Lancet Neurol 11(3):232–240. https://doi.org/10.1016/S1474-4422(12)70014-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kenna KP, McLaughlin RL, Byrne S, Elamin M, Heverin M, Kenny EM, Cormican P, Morris DW, Donaghy CG, Bradley DG, Hardiman O (2013) Delineating the genetic heterogeneity of ALS using targeted high-throughput sequencing. J Med Genet 50(11):776–783. https://doi.org/10.1136/jmedgenet-2013-101795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Douaud G, Smith S, Jenkinson M, Behrens T, Johansen-Berg H, Vickers J, James S, Voets N, Watkins K, Matthews PM, James A (2007) Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain J Neurol 130(Pt 9):2375–2386

    Article  Google Scholar 

  46. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS (2001) A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14(1 Pt 1):21–36

    Article  CAS  Google Scholar 

  47. Fischl B (2012) FreeSurfer. NeuroImage 62(2):774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021

    Article  PubMed  PubMed Central  Google Scholar 

  48. Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci 97(20):11050–11055. https://doi.org/10.1073/pnas.200033797

    Article  CAS  PubMed  Google Scholar 

  49. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021

    Article  Google Scholar 

  50. Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S (2007) Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage 36(3):630–644. https://doi.org/10.1016/j.neuroimage.2007.02.049

    Article  PubMed  PubMed Central  Google Scholar 

  51. Bede P, Elamin M, Byrne S, McLaughlin RL, Kenna K, Vajda A, Fagan A, Bradley DG, Hardiman O (2015) Patterns of cerebral and cerebellar white matter degeneration in ALS. J Neurol Neurosurg Psychiatry 86(4):468–470. https://doi.org/10.1136/jnnp-2014-308172

    Article  CAS  PubMed  Google Scholar 

  52. Schuster C, Elamin M, Hardiman O, Bede P (2016) The segmental diffusivity profile of amyotrophic lateral sclerosis associated white matter degeneration. Eur J Neurol 23(8):1361–1371. https://doi.org/10.1111/ene.13038

    Article  CAS  PubMed  Google Scholar 

  53. Bersano E, Sarnelli MF, Solara V, De Marchi F, Sacchetti GM, Stecco A, Corrado L, D'Alfonso S, Cantello R, Mazzini L (2018) A case of late-onset OCD developing PLS and FTD. Amyotroph Lateral Sclerosis Frontotemporal Degener 19(5–6):463–465. https://doi.org/10.1080/21678421.2018.1440405

    Article  Google Scholar 

  54. de Vries BS, Rustemeijer LMM, van der Kooi AJ, Raaphorst J, Schröder CD, Nijboer TCW, Hendrikse J, Veldink JH, van den Berg LH, van Es MA (2017) A case series of PLS patients with frontotemporal dementia and overview of the literature. Amyotroph Lateral Sclerosis Frontotemporal Degener 18(7–8):534–548. https://doi.org/10.1080/21678421.2017.1354996

    Article  Google Scholar 

  55. Budde MD, Xie M, Cross AH, Song SK (2009) Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci Off J Soc Neurosci 29(9):2805–2813. https://doi.org/10.1523/JNEUROSCI.4605-08.2009

    Article  CAS  Google Scholar 

  56. Sun SW, Liang HF, Trinkaus K, Cross AH, Armstrong RC, Song SK (2006) Noninvasive detection of cuprizone induced axonal damage and demyelination in the mouse corpus callosum. Magn Reson Med Off J Soc Magn Reson Med Soc Magn Reson Med 55(2):302–308. https://doi.org/10.1002/mrm.20774

    Article  Google Scholar 

  57. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH (2002) Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage 17(3):1429–1436

    Article  Google Scholar 

  58. Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, Cross AH, Armstrong RC (2005) Demyelination increases radial diffusivity in corpus callosum of mouse brain. NeuroImage 26(1):132–140. https://doi.org/10.1016/j.neuroimage.2005.01.028

    Article  PubMed  Google Scholar 

  59. Ulug AM, Grunewald T, Lin MT, Kamal AK, Filippi CG, Zimmerman RD, Beal MF (2004) Diffusion tensor imaging in the diagnosis of primary lateral sclerosis. J Magn Reson Imaging 19(1):34–39

    Article  Google Scholar 

  60. Muller HP, Agosta F, Riva N, Spinelli EG, Comi G, Ludolph AC, Filippi M, Kassubek J (2018) Fast progressive lower motor neuron disease is an ALS variant: a two-centre tract of interest-based MRI data analysis. NeuroImage Clin 17:145–152. https://doi.org/10.1016/j.nicl.2017.10.008

    Article  PubMed  Google Scholar 

  61. Schuster C, Hardiman O, Bede P (2017) Survival prediction in amyotrophic lateral sclerosis based on MRI measures and clinical characteristics. BMC Neurol 17(1):73. https://doi.org/10.1186/s12883-017-0854-x

    Article  PubMed  PubMed Central  Google Scholar 

  62. Schuster C, Hardiman O, Bede P (2016) Development of an automated MRI-based diagnostic protocol for amyotrophic lateral sclerosis using disease-specific pathognomonic features: a quantitative disease-state classification study. PLoS ONE 11(12):e0167331. https://doi.org/10.1371/journal.pone.0167331

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Grollemund V, Pradat PF, Querin G, Delbot F, Le Chat G, Pradat-Peyre JF, Bede P (2019) Machine learning in amyotrophic lateral sclerosis: achievements, pitfalls, and future directions. Front Neurosci 13:135. https://doi.org/10.3389/fnins.2019.00135

    Article  PubMed  PubMed Central  Google Scholar 

  64. Dickson DW (2008) TDP-43 immunoreactivity in neurodegenerative disorders: disease versus mechanism specificity. Acta Neuropathol 115(1):147–149. https://doi.org/10.1007/s00401-007-0323-5

    Article  PubMed  Google Scholar 

  65. Brettschneider J, Del Tredici K, Toledo JB, Robinson JL, Irwin DJ, Grossman M, Suh E, Van Deerlin VM, Wood EM, Baek Y, Kwong L, Lee EB, Elman L, McCluskey L, Fang L, Feldengut S, Ludolph AC, Lee VM, Braak H, Trojanowski JQ (2013) Stages of pTDP-43 pathology in amyotrophic lateral sclerosis. Ann Neurol 74(1):20–38. https://doi.org/10.1002/ana.23937

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bede P, Hardiman O (2014) Lessons of ALS imaging: pitfalls and future directions—a critical review. NeuroImage Clin 4:436–443. https://doi.org/10.1016/j.nicl.2014.02.011

    Article  PubMed  PubMed Central  Google Scholar 

  67. Turner MR, Agosta F, Bede P, Govind V, Lule D, Verstraete E (2012) Neuroimaging in amyotrophic lateral sclerosis. Biomark Med 6(3):319–337. https://doi.org/10.2217/bmm.12.26

    Article  CAS  PubMed  Google Scholar 

  68. Christidi F, Karavasilis E, Ferentinos P, Xirou S, Velonakis G, Rentzos M, Zouvelou V, Zalonis I, Efstathopoulos E, Kelekis N, Evdokimidis I (2018) Investigating the neuroanatomical substrate of pathological laughing and crying in amyotrophic lateral sclerosis with multimodal neuroimaging techniques. Amyotroph Lateral Sclerosis Frontotemporal Degener 19(1–2):12–20. https://doi.org/10.1080/21678421.2017.1386689

    Article  Google Scholar 

  69. Stoodley CJ, Schmahmann JD (2010) Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 46(7):831–844. https://doi.org/10.1016/j.cortex.2009.11.008

    Article  PubMed  PubMed Central  Google Scholar 

  70. Bede P, Finegan E (2018) Revisiting the pathoanatomy of pseudobulbar affect: mechanisms beyond corticobulbar dysfunction. Amyotroph Lateral Sclerosis Frontotemporal Degener 19(1–2):4–6. https://doi.org/10.1080/21678421.2017.1392578

    Article  Google Scholar 

  71. Bede P, Bokde ALW, Byrne S, Elamin M, McLaughlin RL, Kenna K, Fagan AJ, Pender N, Bradley DG, Hardiman O (2013) Multiparametric MRI study of ALS stratified for the C9orf72 genotype. Neurology 81(4):361–369. https://doi.org/10.1212/WNL.0b013e31829c5eee

    Article  PubMed  PubMed Central  Google Scholar 

  72. Mitsumoto H, Nagy PL, Gennings C, Murphy J, Andrews H, Goetz R, Floeter MK, Hupf J, Singleton J, Barohn RJ, Nations S, Shoesmith C, Kasarskis E, Factor-Litvak P (2015) Phenotypic and molecular analyses of primary lateral sclerosis. Neurol Genet. https://doi.org/10.1212/01.NXG.0000464294.88607.dd

    Article  PubMed  PubMed Central  Google Scholar 

  73. Chipika RH, Finegan E, Li Hi Shing S, Hardiman O, Bede P (2019) tracking a fast-moving disease: longitudinal markers, monitoring, and clinical trial endpoints in ALS. Front Neurol 10:229. https://doi.org/10.3389/fneur.2019.00229

    Article  PubMed  PubMed Central  Google Scholar 

  74. Schuster C, Elamin M, Hardiman O, Bede P (2015) Presymptomatic and longitudinal neuroimaging in neurodegeneration–from snapshots to motion picture: a systematic review. J Neurol Neurosurg Psychiatry 86(10):1089–1096. https://doi.org/10.1136/jnnp-2014-309888

    Article  PubMed  Google Scholar 

  75. Steinacker P, Feneberg E, Weishaupt J, Brettschneider J, Tumani H, Andersen PM, von Arnim CA, Bohm S, Kassubek J, Kubisch C, Lule D, Muller HP, Muche R, Pinkhardt E, Oeckl P, Rosenbohm A, Anderl-Straub S, Volk AE, Weydt P, Ludolph AC, Otto M (2016) Neurofilaments in the diagnosis of motoneuron diseases: a prospective study on 455 patients. J Neurol Neurosurg Psychiatry 87(1):12–20. https://doi.org/10.1136/jnnp-2015-311387

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors are thankful for the kindness and generosity of all patients, their families and the healthy controls for participating in this research project. Without their support, this project would not have been possible. Peter Bede is supported by the Health Research Board (HRB—Ireland; HRB EIA-2017-019), the Andrew Lydon scholarship, the Irish Institute of Clinical Neuroscience IICN—Novartis Ireland Research Grant, the Iris O'Brien Foundation, and the Research Motor Neuron (RMN-Ireland) Foundation. Russell L McLaughlin is supported by the Motor Neurone Disease Association (957-799) and Science Foundation Ireland (17/CDA/4737). Mark A Doherty is supported by Science Foundation Ireland (15/SPP/3244). The sponsors of this study had no role in the design, analyses, presentation of this work or the decision to submit these findings for publication.

Author information

Authors and Affiliations

  1. Computational Neuroimaging Group, TBSI, Trinity College Dublin, Dublin, Ireland

    Eoin Finegan, Rangariroyashe H. Chipika, Stacey Li Hi Shing, Niall Pender, Orla Hardiman & Peter Bede

  2. Complex Trait Genomics Laboratory, Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland

    Mark A. Doherty, Jennifer C. Hengeveld, Alice Vajda & Russell L. McLaughlin

  3. Western Health and Social Care Trust (WHSCT), Northern Ireland, UK

    Colette Donaghy

  4. Department of Psychology, Beaumont Hospital, Dublin, Ireland

    Niall Pender

Authors
  1. Eoin Finegan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rangariroyashe H. Chipika
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stacey Li Hi Shing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark A. Doherty
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jennifer C. Hengeveld
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alice Vajda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Colette Donaghy
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Russell L. McLaughlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Niall Pender
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Orla Hardiman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Peter Bede
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Drafting the manuscript: EF, PB. Clinical assessments: EF, OH, RHC, CD, NP, PB. Neuroimaging analyses: EF, PB. Genetic analyses: MAD, JCH, AV, RLM. Conceptualisation of the study: EF, OH, PB. Revision of the manuscript for intellectual content: EF, RHC, SLHS, MAD, JCH, AV, CD, RLM, NP, OH, and PB.

Corresponding author

Correspondence to Peter Bede.

Ethics declarations

Conflicts of interest

The authors of this manuscript have no conflicts of interest to disclose.

Ethical Standards

This study was approved by the Institutional Ethics (Medical Research) Committee, and all participants provided informed consent prior to inclusion.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Finegan, E., Chipika, R.H., Li Hi Shing, S. et al. The clinical and radiological profile of primary lateral sclerosis: a population-based study. J Neurol 266, 2718–2733 (2019). https://doi.org/10.1007/s00415-019-09473-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00415-019-09473-z

Keywords

Search

Navigation