Advertisement

Improved Automatic Morphology-Based Classification of Parkinson’s Disease and Progressive Supranuclear Palsy

  • Aron S. Talai
  • Zahinoor Ismail
  • Jan Sedlacik
  • Kai Boelmans
  • Nils D. Forkert
Original Article
  • 27 Downloads

Abstract

Objectives

The overlapping symptoms of Parkinson’s disease (PD) and progressive supranuclear palsy—Richardson’s syndrome (PSP-RS) often make a correct clinical diagnosis difficult. The volume of subcortical brain structures derived from high-resolution T1-weighted magnetic resonance imaging (MRI) datasets is frequently used for individual level classification of PD and PSP-RS patients. The aim of this study was to evaluate the benefit of including additional morphological features beyond the simple regional volume, as well as clinical features, and morphological features of cortical structures for an automatic classification of PD and PSP-RS patients.

Material and Methods

A total of 98 high-resolution T1-weighted MRI datasets from 76 PD patients, and 22 PSP-RS patients were available for this study. Using an atlas-based approach, the volume, surface area, and surface-area-to-volume ratio (SA:V) of 21 subcortical and 48 cortical brain regions were calculated and used as features for a support vector machine classification after application of a RELIEF feature selection method.

Results

The comparison of the classification results suggests that including all three morphological parameters (volume, surface area and SA:V) can considerably improve classification accuracy compared to using volume or surface area alone. Likewise, including clinical patient features in addition to morphological parameters also considerably increases the classification accuracy. In contrast to this, integrating morphological features of other cortical structures did not lead to improved classification accuracy. Using this optimal set-up, an accuracy of 98% was achieved with only one falsely classified PD and one falsely classified PSP-RS patient.

Conclusion

The results of this study suggest that clinical features as well as more advanced morphological features should be used for future computer-aided diagnosis systems to differentiate PD and PSP-RS patients based on morphological parameters.

Keywords

Magnetic resonance imaging T1 image sequences Computer-assisted image Analysis Parkinson’s disease Progressive supranuclear palsy 

Notes

Acknowledgements

This work was supported by Parkinson Alberta.

Conflict of interest

A.S. Talai, Z. Ismail, J. Sedlacik, K. Boelmans and N.D. Forkert declare that they have no competing interests.

References

  1. 1.
    Tolosa E, Wenning G, Poewe W. The diagnosis of Parkinson’s disease. Lancet Neurol. 2006;5:75–86.CrossRefPubMedCentralGoogle Scholar
  2. 2.
    Olanow CW, Hauser RA, Jankovic J, Langston W, Lang A, Poewe W, Tolosa E, Stocchi F, Melamed E, Eyal E, Rascol O. A randomized, double-blind, placebo-controlled, delayed start study to assess rasagiline as a disease modifying therapy in Parkinson’s disease (the ADAGIO study): rationale, design, and baseline characteristics. Mov Disord. 2008;23:2194–201.CrossRefPubMedCentralGoogle Scholar
  3. 3.
    Pellicano C, Assogna F, Cellupica N, Piras F, Pierantozzi M, Stefani A, Cerroni R, Mercuri B, Caltagirone C, Pontieri FE, Spalletta G. Neuropsychiatric and cognitive profile of early Richardson’s syndrome, progressive supranuclear Palsy-parkinsonism and Parkinson’s disease. Parkinsonism Relat Disord. 2017;45:50–6.CrossRefPubMedCentralGoogle Scholar
  4. 4.
    Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain. 2002;125:861–70.CrossRefPubMedCentralGoogle Scholar
  5. 5.
    Litvan I, Agid Y, Calne D, Campbell G, Dubois B, Duvoisin RC, Goetz CG, Golbe LI, Grafman J, Growdon JH, Hallett M, Jankovic J, Quinn NP, Tolosa E, Zee DS. Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP International Workshop. Neurology. 1996;47:1–9.CrossRefGoogle Scholar
  6. 6.
    Bensimon G, Ludolph A, Agid Y, Vidailhet M, Payan C, Leigh PN; NNIPPS Study Group. Riluzole treatment, survival and diagnostic criteria in Parkinson plus disorders: the NNIPPS study. Brain. 2009;132:156–71.CrossRefGoogle Scholar
  7. 7.
    Davie CA. A review of Parkinson’s disease. Br Med Bull. 2008;86:109–27.CrossRefGoogle Scholar
  8. 8.
    Marx S, Respondek G, Stamelou M, Dowiasch S, Stoll J, Bremmer F, Oertel WH, Höglinger GU, Einhäuser W. Validation of mobile eye-tracking as novel and efficient means for differentiating progressive supranuclear palsy from Parkinson’s disease. Front Behav Neurosci. 2012;6:1–11.Google Scholar
  9. 9.
    Egerton T, Williams DR, Iansek R. Comparison of gait in progressive supranuclear palsy, Parkinson’s disease and healthy older adults. BMC Neurol. 2012;12:116.CrossRefPubMedCentralGoogle Scholar
  10. 10.
    Tang CC, Poston KL, Eckert T, Feigin A, Frucht S, Gudesblatt M, Dhawan V, Lesser M, Vonsattel JP, Fahn S, Eidelberg D. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 2010;9:149–58.CrossRefPubMedCentralGoogle Scholar
  11. 11.
    Eckert T, Sailer M, Kaufmann J, Schrader C, Peschel T, Bodammer N, Heinze HJ, Schoenfeld MA. Differentiation of idiopathic Parkinson’s disease, multiple system atrophy, progressive supranuclear palsy, and healthy controls using magnetization transfer imaging. Neuroimage. 2004;21:229–35.CrossRefPubMedCentralGoogle Scholar
  12. 12.
    Forkert ND, Schmidt-Richberg A, Treszl A, Hilgetag C, Fiehler J, Münchau A, Handels H, Boelmans K. Automated volumes-of-interest identification for classical and atypical parkinsonian syndrome differentiation using T2’ MR imaging. Methods Inf Med. 2013;52:128–36.CrossRefPubMedCentralGoogle Scholar
  13. 13.
    Duchesne S, Rolland Y, Verin M. Automated computer differential classification in parkinsonian syndromes via pattern analysis on MRI. Acad Radiol. 2009;16:61–70.CrossRefPubMedCentralGoogle Scholar
  14. 14.
    Quattrone A, Nicoletti G, Messina D, Fera F, Condino F, Pugliese P, Lanza P, Barone P, Morgante L, Zappia M, Aguglia U, Gallo O. MR imaging index for differentiation of progressive supranuclear palsy from Parkinson disease and the Parkinson variant of multiple system atrophy. Radiology. 2008;246:214–21.CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Messina D, Cerasa A, Condino F, Arabia G, Novellino F, Nicoletti G, Salsone M, Morelli M, Lanza PL, Quattrone A. Patterns of brain atrophy in Parkinson’s disease, progressive supranuclear palsy and multiple system atrophy. Parkinsonism Relat Disord. 2011;17:172–6.CrossRefPubMedCentralGoogle Scholar
  16. 16.
    Focke NK, Helms G, Scheewe S, Pantel PM, Bachmann CG, Dechent P, Ebentheuer J, Mohr A, Paulus W, Trenkwalder C. Individual voxel-based subtype prediction can differentiate progressive supranuclear palsy from idiopathic Parkinson syndrome and healthy controls. Hum Brain Mapp. 2011;32:1905–15.CrossRefPubMedCentralGoogle Scholar
  17. 17.
    Gama RL, Távora DF, Bomfim RC, Silva CE, Bruin VM, Bruin PF. Morphometry MRI in the differential diagnosis of parkinsonian syndromes. Arq Neuropsiquiatr. 2010;68:333–8.CrossRefPubMedCentralGoogle Scholar
  18. 18.
    Price S, Paviour D, Scahill R, Stevens J, Rossor M, Lees A, Fox N. Voxel-based morphometry detects patterns of atrophy that help differentiate progressive supranuclear palsy and Parkinson’s disease. Neuroimage. 2004;23:663–9.CrossRefPubMedCentralGoogle Scholar
  19. 19.
    Scherfler C, Göbel G, Müller C, Nocker M, Wenning GK, Schocke M, Poewe W, Seppi K. Diagnostic potential of automated subcortical volume segmentation in atypical parkinsonism. Neurology. 2016;86:1242–9.CrossRefPubMedCentralGoogle Scholar
  20. 20.
    Sarica A, Critelli C, Guzzi PH, Cerasa A, Quattrone A, Cannataro M. Application of different classification techniques on brain morphological data. Comput Med Syst (CBMS), 2013 IEEE 26th Int Symp on IEEE. 2013. pp. 425–8.Google Scholar
  21. 21.
    Lee JH, Han YH, Kang BM, Mun CW, Lee SJ, Baik SK. Quantitative assessment of subcortical atrophy and iron content in progressive supranuclear palsy and parkinsonian variant of multiple system atrophy. J Neurol. 2013;260:2094–101.CrossRefPubMedCentralGoogle Scholar
  22. 22.
    Sakurai K, Tokumaru AM, Shimoji K, Murayama S, Kanemaru K, Morimoto S, Aiba I, Nakagawa M, Ozawa Y, Shimohira M, Matsukawa N, Hashizume Y, Shibamoto Y. Beyond the midbrain atrophy: wide spectrum of structural MRI finding in cases of pathologically proven progressive supranuclear palsy. Neuroradiology. 2017;59:431–43.CrossRefPubMedCentralGoogle Scholar
  23. 23.
    Dotson VM, Szymkowicz SM, Sozda CN, Kirton JW, Green ML, O’Shea A, McLaren ME, Anton SD, Manini TM, Woods AJ. Age differences in prefrontal surface area and thickness in middle aged to older adults. Front Aging Neurosci. 2016;7:1–9.CrossRefGoogle Scholar
  24. 24.
    Jubault T, Gagnon JF, Karama S, Ptito A, Lafontaine AL, Evans AC, Monchi O. Patterns of cortical thickness and surface area in early Parkinson’s disease. Neuroimage. 2011;55:462–7.CrossRefPubMedCentralGoogle Scholar
  25. 25.
    Gerrits NJ, van Loenhoud AC, van den Berg SF, Berendse HW, Foncke EM, Klein M, Stoffers D, van der Werf YD, van den Heuvel OA. Cortical thickness, surface area and subcortical volume differentially contribute to cognitive heterogeneity in Parkinson’s disease. PLoS ONE. 2016;11:1–14.CrossRefGoogle Scholar
  26. 26.
    Dickerson BC, Feczko E, Augustinack JC, Pacheco J, Morris JC, Fischl B, Buckner RL. Differential effects of aging and Alzheimer’s disease on medial temporal lobe cortical thickness and surface area. Neurobiol Aging. 2009;30:432–40.CrossRefPubMedCentralGoogle Scholar
  27. 27.
    Worker A, Blain C, Jarosz J, Chaudhuri KR, Barker GJ, Williams SC, Brown R, Leigh PN, Simmons A. Cortical thickness, surface area and volume measures in Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy. PLoS ONE. 2014;9:1–15.Google Scholar
  28. 28.
    Planetta PJ, Ofori E, Pasternak O, Burciu RG, Shukla P, DeSimone JC, Okun MS, McFarland NR, Vaillancourt DE. Free-water imaging in Parkinson’s disease and atypical parkinsonism. Brain. 2016;139:495–508.CrossRefPubMedCentralGoogle Scholar
  29. 29.
    Hirschauer TJ, Adeli H, Buford JA. Computer-aided diagnosis of Parkinson’s disease using enhanced probabilistic neural network. J Med Syst. 2015;39:179.CrossRefPubMedCentralGoogle Scholar
  30. 30.
    Long D, Wang J, Xuan M, Gu Q, Xu X, Kong D, Zhang M. Automatic classification of early Parkinson’s disease with multi-modal MR imaging. PLoS ONE. 2012;7:1–9.Google Scholar
  31. 31.
    Péran P, Cherubini A, Assogna F, Piras F, Quattrocchi C, Peppe A, Celsis P, Rascol O, Démonet JF, Stefani A, Pierantozzi M, Pontieri FE, Caltagirone C, Spalletta G, Sabatini U. Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain. 2010;133:3423–33.CrossRefGoogle Scholar
  32. 32.
    Morisi R, Cha M, Arafa M, Zagrouba E. Binary and multi-class parkinsonian disorders classification using support vector machines. In: Lecture notes in computer science. 2015. pp. 379–86.Google Scholar
  33. 33.
    Ota M, Nakata Y, Ito K, Kamiya K, Ogawa M, Murata M, Obu S, Kunugi H, Sato N. Differential diagnosis tool for parkinsonian syndrome using multiple structural brain measures. Comput Math Methods Med. 2013;2013:571289.CrossRefPubMedCentralGoogle Scholar
  34. 34.
    Segovia F, Illán IA, Górriz JM, Ramírez J, Rominger A, Levin J. Distinguishing Parkinson’s disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks. Front Comput Neurosci. 2015;9:1–8.CrossRefGoogle Scholar
  35. 35.
    Prashanth R, Dutta Roy S, Mandal PK, Ghosh S. High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning. Int J Med Inform. 2016;90:13–21.CrossRefPubMedCentralGoogle Scholar
  36. 36.
    Boelmans K, Holst B, Hackius M, Finsterbusch J, Gerloff C, Fiehler J, Münchau A. Brain iron deposition fingerprints in Parkinson’s disease and progressive supranuclear palsy. Mov Disord. 2012;27:421–7.CrossRefPubMedCentralGoogle Scholar
  37. 37.
    Fellner F, Holl K, Held P, Fellner C, Schmitt R, Böhm-Jurkovic H. A T1-weighted rapid three-dimensional gradient-echo technique (MP-RAGE) in preoperative MRI of intracranial tumours. Neuroradiology. 1996;38:199–206.CrossRefPubMedCentralGoogle Scholar
  38. 38.
    Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Boomsma D, Cannon T, Kawashima R, Mazoyer B. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond, B, Biol Sci. 2001;356:1293–322.CrossRefPubMedCentralGoogle Scholar
  39. 39.
    Ourselin S, Roche A, Subsol G, Pennec X, Ayache N. Reconstructing a 3D structure from serial histological sections. Image Vis Comput. 2001;19:25–31.CrossRefGoogle Scholar
  40. 40.
    Modat M, Ridgway GR, Taylor ZA, Lehmann M, Barnes J, Hawkes DJ, Fox NC, Ourselin S. Fast free-form deformation using graphics processing units. Comput Methods Programs Biomed. 2010;98:278–84.CrossRefPubMedCentralGoogle Scholar
  41. 41.
    Kononenko I, Šimec E, Robnik-Šikonja M. Overcoming the myopia of inductive learning algorithms with RELIEFF. Appl Intell. 1997;7:39–55.CrossRefGoogle Scholar
  42. 42.
    Chang C, Lin C. LIBSVM: A Library for Support Vector Machines. ACM Trans Intell Syst Technol. 2011;2:27:1-27.CrossRefGoogle Scholar
  43. 43.
    Du G, Lewis MM, Kanekar S, Sterling NW, He L, Kong L, Li R, Huang X. Combined diffusion tensor imaging and apparent transverse relaxation rate differentiate Parkinson disease and atypical parkinsonism. AJNR Am J Neuroradiol. 2017;38:966–72.CrossRefPubMedCentralGoogle Scholar
  44. 44.
    Dubois B, Pillon B. Cognitive deficits in Parkinson’s disease. J Neurol. 1996;244:2–8.CrossRefGoogle Scholar
  45. 45.
    Salvatore C, Cerasa A, Castiglioni I, Gallivanone F, Augimeri A, Lopez M, Arabia G, Morelli M, Gilardi MC, Quattrone A. Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy. J Neurosci Methods. 2014;222:230–7.CrossRefPubMedCentralGoogle Scholar
  46. 46.
    Cherubini A, Morelli M, Nisticó R, Salsone M, Arabia G, Vasta R, Augimeri A, Caligiuri ME, Quattrone A. Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy. Mov Disord. 2014;29:266–9.CrossRefPubMedCentralGoogle Scholar
  47. 47.
    Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, Obeso J, Marek K, Litvan I, Lang AE, Halliday G, Goetz CG, Gasser T, Dubois B, Chan P, Bloem BR, Adler CH, Deuschl G. MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord. 2015;30:1591–601.CrossRefGoogle Scholar
  48. 48.
    Boxer AL, Yu JT, Golbe LI, Litvan I, Lang AE, Höglinger GU. Advances in progressive supranuclear palsy: new diagnostic criteria, biomarkers, and therapeutic approaches. Lancet Neurol. 2017;16:552–63.CrossRefPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Radiology and Hotchkiss Brain Institute, Faculty of MedicineUniversity of CalgaryCalgaryCanada
  2. 2.Departments of Psychiatry, Clinical Neurosciences, and Community Health Sciences, and Hotchkiss Brain InstituteUniversity of CalgaryCalgaryCanada
  3. 3.Department of Diagnostic and Interventional NeuroradiologyUniversity Medical Center Hamburg-EppendorfHamburgGermany
  4. 4.Department of NeurologyUniversity Hospital WürzburgWürzburgGermany

Personalised recommendations