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Recent Advancement and Clinical Implications of 18FDG-PET in Parkinson’s Disease, Atypical Parkinsonisms, and Other Movement Disorders

  • Neuroimaging (N. Pavese, Section Editor)
  • Published:
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Abstract

Purpose of Review

The molecular imaging field has been very instrumental in identifying the multiple network interactions that compose the human brain. The cerebral glucose metabolism is associated with neural function. 18F-fluoro-deoxyglucose-PET (FDG-PET) studies reflect brain metabolism in a pattern-specific manner. This article reviews FDG-PET studies in Parkinson’s disease (PD), atypical parkinsonism (AP), Huntington’s disease (HD), and dystonia.

Recent Findings

The metabolic pattern of PD, disease progression, non-motor symptoms such as fatigue, depression, apathy, impulse control disorders, and cognitive impairment, and the risk of progression to dementia have been identified with FDG-PET studies. In prodromal PD, the REM sleep behavior disorder-related covariance pattern has been described. In AP, FDG-PET studies have demonstrated to be superior to D2/D3 SPECT in differentiating PD from AP. The metabolic patterns of HD and dystonia have also been described.

Summary

FDG-PET studies are an excellent tool to identify patterns of brain metabolism.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. •• Strafella AP, Bohnen NI, Perlmutter JS, Eidelberg D, Pavese N, Van Eimeren T, et al. Molecular imaging to track Parkinson’s disease and atypical parkinsonisms: new imaging frontiers. Mov Disord. 2017;32(2):181–92. The authors provide an overview of molecular imaging advances in PD and AP, how these approaches help to their understanding and revise exciting new tracer developments.

    Article  Google Scholar 

  2. Mergenthaler P, Lindauer U, Dienel GA, Meisel A. Sugar for the brain: the role of glucose in physiological and pathological brain function. Trends Neurosci. 2013;36(10):587–97.

    Article  CAS  Google Scholar 

  3. Ikemoto S. Brain reward circuitry beyond the mesolimbic dopamine system: a neurobiological theory. Neurosci Biobehav Rev. 2010;35(2):129–50.

    Article  CAS  Google Scholar 

  4. Jellinger KA. Post mortem studies in Parkinson’s disease—is it possible to detect brain areas for specific symptoms? J Neural Transm Suppl. 1999;56:1–29.

    Article  CAS  Google Scholar 

  5. Eidelberg D, Moeller JR, Dhawan V, Spetsieris P, Takikawa S, Ishikawa T, et al. The metabolic topography of parkinsonism. J Cereb Blood Flow Metab. 1994;14(5):783–801.

    Article  CAS  Google Scholar 

  6. Helmich RC, Derikx LC, Bakker M, Scheeringa R, Bloem BR, Toni I. Spatial remapping of cortico-striatal connectivity in Parkinson’s disease. Cereb Cortex. 2010;20(5):1175–86.

    Article  Google Scholar 

  7. Brooks DJ, Pavese N. Imaging biomarkers in Parkinson’s disease. Prog Neurobiol. 2011;95(4):614–28.

    Article  CAS  Google Scholar 

  8. Holtbernd F, Ma Y, Peng S, Schwartz F, Timmermann L, Kracht L, et al. Dopaminergic correlates of metabolic network activity in Parkinson’s disease. Hum Brain Mapp. 2015;36:3575–85.

    Article  Google Scholar 

  9. • Granert O, Drzezga AE, Boecker H, Perneczky R, Kurz A, Götz J, et al. Metabolic topology of neurodegenerative disorders: influence of cognitive and motor deficits. J Nucl Med. 2015;56(12):1916–21. The study describes the important correlations between FDG-PET metabolic pattern and clinical measurements (cognitive AD-typical vs motor PD-typical pattern).

    Article  Google Scholar 

  10. Huang C, Tang C, Feigin A, Lesser M, Ma Y, Pourfar M, et al. Changes in network activity with the progression of Parkinson’s disease. Brain. 2007;130(Pt 7:1834–46.

    Article  Google Scholar 

  11. Ma Y, Johnston TH, Peng S, Zuo C, Koprich JB, Fox SH, et al. Reproducibility of a parkinsonism-related metabolic brain network in non-human primates: a descriptive pilot study with FDG PET. Mov Disord. 2015;30(9):1283–8.

    Article  Google Scholar 

  12. Surmeier DJ, Obeso JA, Halliday GM. Selective neuronal vulnerability in Parkinson disease. Nat Rev Neurosci. 2017;18(2):101–13.

    Article  CAS  Google Scholar 

  13. Su PC, Ma Y, Fukuda M, Mentis MJ, Tseng HM, Yen RF, et al. Metabolic changes following subthalamotomy for advanced Parkinson’s disease. Ann Neurol. 2001;50(4):514–20.

    Article  CAS  Google Scholar 

  14. • Cao C, Zhang H, Li D, Zhan S, Zhang J, Zhang X, et al. Modified fluorodeoxyglucose metabolism in motor circuitry by subthalamic deep brain stimulation. Stereotact Funct Neurosurg. 2017;95(2):93–101. The paper provides insight in the modulation of the motor circuitry of PD patients by STN-DBS using FDG-PET and the correlations between glucose metabolism and clinical symptoms.

    Article  Google Scholar 

  15. Asanuma K, Tang C, Ma Y, Dhawan V, Mattis P, Edwards C, et al. Network modulation in the treatment of Parkinson’s disease. Brain. 2006;129(Pt 10:2667–78.

    Article  Google Scholar 

  16. Ko JH, Feigin A, Mattis PJ, Tang CC, Ma Y, Dhawan V, et al. Network modulation following sham surgery in Parkinson’s disease. J Clin Invest. 2014;124(8):3656–66.

    Article  CAS  Google Scholar 

  17. Mure H, Hirano S, Tang CC, Isaias IU, Antonini A, Ma Y, et al. Parkinson’s disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage. 2011;54(2):1244–53.

    Article  Google Scholar 

  18. Spetsieris PG, Ko JH, Tang CC, Nazem A, Sako W, Peng S, et al. Metabolic resting-state brain networks in health and disease. Proc Natl Acad Sci U S A. 2015;112(8):2563–8.

    Article  CAS  Google Scholar 

  19. Holtbernd F, Gagnon JF, Postuma RB, Ma Y, Tang CC, Feigin A, et al. Abnormal metabolic network activity in REM sleep behavior disorder. Neurology. 2014;82:620–7.

    Article  CAS  Google Scholar 

  20. Wu P, Yu H, Peng S, Dauvilliers Y, Wang J, Ge J, et al. Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder. Brain. 2014;137:3122–8.

    Article  Google Scholar 

  21. Ztaou S, Amalric M. Contribution of cholinergic interneurons to striatal pathophysiology in Parkinson’s disease. Neurochem Int. 2019;126:1–10.

    Article  CAS  Google Scholar 

  22. Huang C, Mattis P, Tang C, Perrine K, Carbon M, Eidelberg D. Metabolic brain networks associated with cognitive function in Parkinson’s disease. Neuroimage. 2007;34(2):714–23.

    Article  Google Scholar 

  23. Williams-Gray CH, Evans JR, Goris A, Foltynie T, Ban M, Robbins TW, et al. The distinct cognitive syndromes of Parkinson’s disease: 5 year follow-up of the CamPaIGN cohort. Brain. 2009;132(Pt 11:2958–69.

    Article  Google Scholar 

  24. Litvan I, Goldman JG, Tröster AI, Schmand BA, Weintraub D, Petersen RC, et al. Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines. Mov Disord. 2012;27(3):349–56.

    Article  Google Scholar 

  25. Liepelt I, Reimold M, Maetzler W, Godau J, Reischl G, Gaenslen A, et al. Cortical hypometabolism assessed by a metabolic ratio in Parkinson’s disease primarily reflects cognitive deterioration-[18F]FDG-PET. Mov Disord. 2009;24(10):1504–11.

    Article  Google Scholar 

  26. Garcia-Garcia D, Clavero P, Gasca Salas C, Lamet I, Arbizu J, Gonzalez-Redondo R, et al. Posterior parietooccipital hypometabolism may differentiate mild cognitive impairment from dementia in Parkinson’s disease. Eur J Nucl Med Mol Imaging. 2012;39(11):1767–77.

    Article  Google Scholar 

  27. González-Redondo R, García-García D, Clavero P, Gasca-Salas C, García-Eulate R, Zubieta JL, et al. Grey matter hypometabolism and atrophy in Parkinson’s disease with cognitive impairment: a two-step process. Brain. 2014;137(Pt 8:2356–67.

    Article  Google Scholar 

  28. •• Pilotto A, Premi E, Paola Caminiti S, Presotto L, Turrone R, Alberici A, et al. Single-subject SPM FDG-PET patterns predict risk of dementia progression in Parkinson disease. Neurology. 2018;90(12):e1029–37. The study evaluates FDG-PET imaging as a possible single-subject marker of progression to dementia in PD. At 4-year follow-up, all patients showing atypical brain metabolic patterns at baseline progressed to PD dementia. The DLB- and AD-like SPM patterns were the best predictor for incident dementia with a sensitivity of 85% and a specificity of 88%.

    Article  Google Scholar 

  29. Verger A, Klesse E, Chawki MB, Witjas T, Azulay JP, Eusebio A, et al. Brain PET substrate of impulse control disorders in Parkinson’s disease: a metabolic connectivity study. Hum Brain Mapp. 2018;39(8):3178–86.

    Article  Google Scholar 

  30. Robert G, Le Jeune F, Lozachmeur C, Drapier S, Dondaine T, Péron J, et al. Apathy in patients with Parkinson disease without dementia or depression: a PET study. Neurology. 2012;79(11):1155–60.

    Article  CAS  Google Scholar 

  31. Huang C, Ravdin LD, Nirenberg MJ, Piboolnurak P, Severt L, Maniscalco JS, et al. Neuroimaging markers of motor and nonmotor features of Parkinson’s disease: an 18f fluorodeoxyglucose positron emission computed tomography study. Dement Geriatr Cogn Disord. 2013;35(3–4):183–96.

    Article  Google Scholar 

  32. Cho SS, Aminian K, Li C, Lang AE, Houle S, Strafella AP. Fatigue in Parkinson’s disease: the contribution of cerebral metabolic changes. Hum Brain Mapp. 2017;38(1):283–92.

    Article  Google Scholar 

  33. Eckert T, Tang C, Ma Y, Brown N, Lin T, Frucht S, et al. Abnormal metabolic networks in atypical parkinsonism. Mov Disord. 2008;23(5):727–33.

    Article  Google Scholar 

  34. Zalewski N, Botha H, Whitwell JL, Lowe V, Dickson DW, Josephs KA. FDG-PET in pathologically confirmed spontaneous 4R-tauopathy variants. J Neurol. 2014;261(4):710–6.

    Article  CAS  Google Scholar 

  35. Juh R, Pae CU, Kim TS, Lee CU, Choe B, Suh T. Cerebral glucose metabolism in corticobasal degeneration comparison with progressive supranuclear palsy using statistical mapping analysis. Neurosci Lett. 2005;383(1–2):22–7.

    Article  CAS  Google Scholar 

  36. Sarikaya I. PET imaging in neurology: Alzheimer’s and Parkinson’s diseases. Nucl Med Commun. 2015;36(8):775–81.

    Article  CAS  Google Scholar 

  37. Eckert T, Barnes A, Dhawan V, Frucht S, Gordon MF, Feigin AS, et al. FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage. 2005;26(3):912–21.

    Article  Google Scholar 

  38. Walker Z, Gandolfo F, Orini S, Garibotto V, Agosta F, Arbizu J, et al. Clinical utility of FDG PET in Parkinson’s disease and atypical parkinsonism associated with dementia. Eur J Nucl Med Mol Imaging. 2018;45(9):1534–45.

    Article  CAS  Google Scholar 

  39. • Hoglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, et al. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov Disord. 2017;32(6):853–64. This is a consensus-based revision of the clinical diagnostic criteria for PSP. New criteria consistent of four functional domains as clinical predictors of PSP, combinations of clinical features stratified by degrees of diagnostic certainty, clinical clues, and imaging findings as supportive features are discussed.

    Article  Google Scholar 

  40. Van Laere K, Varrone A, Brooj J. EANM procedure guidelines for brain neurotransmission SPECT/PET using dopamine D2 receptor ligands, version 2. Eur J Nucl Med Mol Imaging. 2010;37:434–42.

    Article  CAS  Google Scholar 

  41. Vlaar AM, van Kroonenburgh MJ, Kessels AG, Weber WE. Meta-analysis of the literature on diagnostic accuracy of SPECT in parkinsonian syndromes. BMC Neurol. 2007;7:27.

    Article  Google Scholar 

  42. Hellwig S, Amtage F, Kreft A, Buchert R, Winz OH, Vach W, et al. [18F]FDG-PET is superior to [123I]IBZM-SPECT for the differential diagnosis of parkinsonism. Neurology. 2012;79(13):1314–22.

    Article  CAS  Google Scholar 

  43. Young AB, Penney JB, Starosta-Rubinstein S, Markel DS, Berent S, Giordani B, et al. PET scan investigations of Huntington’s disease: cerebral metabolic correlates of neurological features and functional decline. Ann Neurol. 1986;20(3):296–303.

    Article  CAS  Google Scholar 

  44. Kuwert T, Lange HW, Langen KJ, Herzog H, Aulich A, Feinendegen LE. Cortical and subcortical glucose consumption measured by PET in patients with Huntington’s disease. Brain. 1990;113(Pt 5:1405–23.

    Article  Google Scholar 

  45. Antonini A, Leenders KL, Spiegel R, Meier D, Vontobel P, Weigell-Weber M, et al. Striatal glucose metabolism and dopamine D2 receptor binding in asymptomatic gene carriers and patients with Huntington’s disease. Brain. 1996;119(Pt 6):2085–95.

    Article  Google Scholar 

  46. Ciarmiello A, Cannella M, Lastoria S, Simonelli M, Frati L, Rubinsztein DC, et al. Brain white-matter volume loss and glucose hypometabolism precede the clinical symptoms of Huntington’s disease. J Nucl Med. 2006;47:215–22.

    PubMed  CAS  Google Scholar 

  47. Feigin A, Tang C, Ma Y, Mattis P, Zgaljardic D, Guttman M, et al. Thalamic metabolism and symptom onset in preclinical Huntington’s disease. Brain. 2007;130:2858–67.

    Article  CAS  Google Scholar 

  48. Tang CC, Feigin A, Ma Y, Habeck C, Paulsen JS, Leenders KL, et al. Metabolic network as a progression biomarker of premanifest Huntington’s disease. J Clin Invest. 2013;123(9):4076–88.

    Article  CAS  Google Scholar 

  49. Neychev VK, Gross R, Lehéricy S, Hess EJ, Jinnah HA. The functional neuroanatomy of dystonia. Neurobiol Dis. 2011;42(2):185–201.

    Article  Google Scholar 

  50. Lehéricy S, Tijssen MAJ, Vidailhet M, Kaji R, Meunier S. The anatomical basis of dystonia: current view using neuroimaging. Mov Disord. 2013;28(7):944–57.

    Article  Google Scholar 

  51. • Kaji R, Bhatia K, Graybiel AM. Pathogenesis of dystonia: is it of cerebellar or basal ganglia origin? J Neurol Neurosurg Psychiatry. 2018;89(5):488–92. The paper reviews current evidence on a new functional interaction between the cerebellum and basal ganglia in the pathogenesis of dystonia; highlighting it is now regarded as a ‘network’ disorder including the cerebellum.

    Article  Google Scholar 

  52. Trost M, Carbon M, Edwards C, Ma Y, Raymond D, Mentis MJ, et al. Primary dystonia: is abnormal functional brain architecture linked to genotype? Ann Neurol. 2002;52(6):853–6.

    Article  Google Scholar 

  53. Carbon M, Eidelberg D. Abnormal structure-function relationships in hereditary dystonia. Neuroscience. 2009;164(1):220–9.

    Article  CAS  Google Scholar 

  54. Carbon M, Raymond D, Ozelius L, Saunders-Pullman R, Frucht S, Dhawan V, et al. Metabolic changes in DYT11 myoclonus-dystonia. Neurology. 2013;80(4):385–91.

    Article  CAS  Google Scholar 

  55. • Matthews DC, Lerman H, Lukic A, Andrews RD, Mirelman A, Wernick MN, et al. FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease. Neuroimage Clin. 2018;20:572–9. The authors present evidence on how FDG-PET using two classifiers can discriminate HC from PD observing very similar metabolic patters, consistent with the PDRP. They propose that FDG-PET and multivariate classification can provide an objective biomarker of disease stage with the potential to detect treatment effects on PD progression.

    Article  Google Scholar 

  56. Tondo G, Esposito M, Dervenoulas G, Wilson H, Politis M, Pagano G. Hybrid PET-MRI applications in movement disorders. Int Rev Neurobiol. 2019;144:211–57.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Neurology, CEMIC University Hospital, Elias Galván 4102, C1431FWO, Buenos Aires, Argentina

    Cecilia Peralta & Tamara Soto Depetris

  2. Department of Biostatistics, School of Science and Technology, National University of San Martín, Campus Miguelete, 25 de Mayo y Francia, Buenos Aires, Argentina

    Federico Biafore

  3. Department of Molecular and Metabolic Imaging, CEMIC University Hospital, Elias Galván, 4102, Buenos Aires, Argentina

    Maria Bastianello

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  1. Cecilia Peralta
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  2. Federico Biafore
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  4. Maria Bastianello
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Correspondence to Cecilia Peralta.

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Cecilia Peralta, Federico Biafore, Tamara Soto, and Maria Bastianello each declare no potential conflicts of interest.

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Peralta, C., Biafore, F., Depetris, T.S. et al. Recent Advancement and Clinical Implications of 18FDG-PET in Parkinson’s Disease, Atypical Parkinsonisms, and Other Movement Disorders. Curr Neurol Neurosci Rep 19, 56 (2019). https://doi.org/10.1007/s11910-019-0966-3

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