Advertisement

Testing the diagnostic accuracy of [18F]FDG-PET in discriminating spinal- and bulbar-onset amyotrophic lateral sclerosis

  • Arianna Sala
  • Leonardo Iaccarino
  • Piercarlo Fania
  • Emilia G. Vanoli
  • Federico Fallanca
  • Caterina Pagnini
  • Chiara Cerami
  • Andrea Calvo
  • Antonio Canosa
  • Marco Pagani
  • Adriano Chiò
  • Angelina Cistaro
  • Daniela PeraniEmail author
Original Article

Abstract

Purpose

The role for [18F]FDG-PET in supporting amyotrophic lateral sclerosis (ALS) diagnosis is not fully established. In this study, we aim at evaluating [18F]FDG-PET hypo- and hyper-metabolism patterns in spinal- and bulbar-onset ALS cases, at the single-subject level, testing the diagnostic value in discriminating the two conditions, and the correlations with core clinical symptoms severity.

Methods

We included 95 probable-ALS patients with [18F]FDG-PET scan and clinical follow-up. [18F]FDG-PET images were analyzed with an optimized voxel-based-SPM method. The resulting single-subject SPM-t maps were used to: (a) assess brain regional hypo- and hyper-metabolism; (b) evaluate the accuracy of regional hypo- and hyper metabolism in discriminating spinal vs. bulbar-onset ALS; (c) perform correlation analysis with motor symptoms severity, as measured by ALS-FRS-R.

Results

Primary motor cortex showed the most frequent hypo-metabolism in both spinal-onset (∼57%) and bulbar-onset (∼64%) ALS; hyper-metabolism was prevalent in the cerebellum in both spinal-onset (∼56.5%) and bulbar-onset (∼55.7%) ALS, and in the occipital cortex in bulbar-onset (∼62.5%) ALS. Regional hypo- and hyper-metabolism yielded a very low accuracy (AUC < 0.63) in discriminating spinal- vs. bulbar-onset ALS, as obtained from single-subject SPM-t-maps. Severity of motor symptoms correlated with hypo-metabolism in sensorimotor cortex in spinal-onset ALS, and with cerebellar hyper-metabolism in bulbar-onset ALS.

Conclusions

The high variability in regional hypo- and hyper-metabolism patterns, likely reflecting the heterogeneous pathology and clinical phenotypes, limits the diagnostic potential of [18F]FDG-PET in discriminating spinal and bulbar onset patients.

Keywords

Amyotrophic lateral sclerosis Biomarkers Diagnosis [18F]FDG-PET Brain metabolism 

Notes

Funding

This study was funded by the Italian Ministry of Health (Ricerca Finalizzata Progetto Reti Nazionale AD NET-2011-02346784) (D.P), EU FP7 INMIND Project (FP7-HEALTH-2011-two-stage “Imaging of Neuroinflammation in Neurodegenerative Diseases”, grant agreement no. 278850)” (D.P.), “IVASCOMAR project “Identificazione, validazione e sviluppo commerciale di nuovi biomarcatori diagnostici prognostici per malattie complesse” (grant agreement no. CTN01_00177_165430)” (D.P.), CARIPLO Project “Evaluation of autonomic, genetic, imaging and biochemical markers for Parkinson-related dementia: longitudinal assessment of a PD cohort” 2016–2020 (grant agreement no. 2014–0832)” (D.P.), Fondazione Eli-Lilly (Eli-Lilly grant 2011 “Imaging of neuroinflammation and neurodegeneration in prodromal and presymptomatic Alzheimer’s disease phases”)(C.C.), Ministero dell’Istruzione, dell’Università e della Ricerca – MIUR project “Dipartimenti di Eccellenza 2018–2022″ to Department of Neuroscience “Rita Levi Montalcini”(Ad.C, And.C, Ant.C.). This work was in part supported by the Italian Ministry of Health (Ricerca Sanitaria Finalizzata 2010, grant RF-2010e2309849) (Ad.C), the European Community’s Health Seventh Framework Programme (FP7/2007–2013 under grant agreement 259867) (Ad.C), the Joint Programme - Neurodegenerative Disease Research (Italian Ministry of Education, University and Research) (Sophia) (Ad.C.), Fondazione Vialli e Mauro (And.C.); and Fondazione Magnetto (Ant.C.).

Compliance with ethical standards

Conflict of interest

A. Chiò reports grants from the Italian Ministry of Health (Ricerca Finalizzata), EU JPND through the Ministry of Education, University, and Research, and the Italy-Israel Scientific Collaboration through the Italian Foreign Ministry, as well as personal fees from Biogen Idec, Cytokinetics, Italfarmaco, Mitsubishi Tanabe, and Neuraltus. All other authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Chiò A, Pagani M, Agosta F, Calvo A, Cistaro A, Filippi M. Neuroimaging in amyotrophic lateral sclerosis: insights into structural and functional changes. Lancet Neurol. 2014;13:1228–40.CrossRefGoogle Scholar
  2. 2.
    Ludolph A, Drory V, Hardiman O, Nakano I, Ravits J, Robberecht W, et al. A revision of the El Escorial criteria - 2015. Amyotroph Lateral Scler Front Degener. 2015;16:291–2.Google Scholar
  3. 3.
    Brooks BR, Miller RG, Swash M, Munsat TL. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. 2000;1:293–9.CrossRefGoogle Scholar
  4. 4.
    Zoccolella S, Beghi E, Palagano G, Fraddosio A, Samarelli V, Lamberti P, et al. Predictors of delay in the diagnosis and clinical trial entry of amyotrophic lateral sclerosis patients: a population-based study. J Neurol Sci. 2006;250:45–9.CrossRefGoogle Scholar
  5. 5.
    Turner MR, Kiernan MC, Leigh PN, Talbot K. Biomarkers in amyotrophic lateral sclerosis. Lancet Neurol. Elsevier Ltd. 2009;8:94–109.CrossRefGoogle Scholar
  6. 6.
    Swinnen B, Robberecht W. The phenotypic variability of amyotrophic lateral sclerosis. Nat. Rev. Neurol. Nature Publishing Group. 2014;10:661–70.CrossRefGoogle Scholar
  7. 7.
    Chiò AISIS. Survey: an international study on the diagnostic process and its implications in amyotrophic lateral sclerosis. J Neurol. 1999;246:1–5.CrossRefGoogle Scholar
  8. 8.
    Belsh JM, Schiffman PL. The amyotrophic lateral sclerosis (ALS) patient perspective on misdiagnosis and its repercussions. J Neurol Sci. 1996;139:110–6.CrossRefGoogle Scholar
  9. 9.
    Nzwalo H, De Abreu D, Swash M, Pinto S, De Carvalho M. Delayed diagnosis in ALS: the problem continues. J Neurol Sci. 2014;343:173–5.CrossRefGoogle Scholar
  10. 10.
    Hardiman O, van den Berg LH, Kiernan MC. Clinical diagnosis and management of amyotrophic lateral sclerosis. Nat Rev Neurol Nature Publishing Group. 2011;7:639–49.CrossRefGoogle Scholar
  11. 11.
    Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014;13:614–29.CrossRefGoogle Scholar
  12. 12.
    McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s dement. Elsevier Ltd. 2011;7:263–9.Google Scholar
  13. 13.
    McKeith I, Boeve B, Dickson D, Lowe J, Emre M, Al E. Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium. Neurology. 2017;89:88–100.CrossRefGoogle Scholar
  14. 14.
    Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging and Alzheimer’s association workgroup. Alzheimers Dement. 2011;7:270–9.CrossRefGoogle Scholar
  15. 15.
    Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, et al. Classification of primary progressive aphasia and its variants. Neurology. 2011;76:1006–14.CrossRefGoogle Scholar
  16. 16.
    Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain. 2011;134:2456–77.CrossRefGoogle Scholar
  17. 17.
    Stoessl AJ. Glucose utilization: still in the synapse. Nat Neurosci Nature Publishing Group. 2017;20:382–4.CrossRefGoogle Scholar
  18. 18.
    Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s dement. Elsevier Ltd. 2011;7:280–92.Google Scholar
  19. 19.
    Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B, et al. Criteria for the diagnosis of corticobasal degeneration. Neurology. 2013;80:496–503.CrossRefGoogle Scholar
  20. 20.
    Caminiti SP, Alongi P, Majno L, Volontè MA, Cerami C, Gianolli L, et al. Evaluation of an optimized [18F]fluoro-deoxy-glucose positron emission tomography voxel-wise method to early support differential diagnosis in atypical Parkinsonian disorders. Eur J Neurol. 2017;24:687–e26.CrossRefGoogle Scholar
  21. 21.
    Cerami C, Dodich A, Greco L, Iannaccone S, Magnani G, Marcone A, et al. The role of single-subject brain metabolic patterns in the early differential diagnosis of primary progressive aphasias and in prediction of progression to dementia. J Alzheimers Dis. 2016;55:183–97.CrossRefGoogle Scholar
  22. 22.
    Perani D, Della Rosa PA, Cerami C, Gallivanone F, Fallanca F, Vanoli EG, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. NeuroImage. Clin. Elsevier BV. 2014;6:445–54.Google Scholar
  23. 23.
    Perani D, Cerami C, Caminiti SP, Santangelo R, Coppi E, Ferrari L, et al. Cross-validation of biomarkers for the early differential diagnosis and prognosis of dementia in a clinical setting. Eur J Nucl Med Mol Imaging. 2016;43:499–508.CrossRefGoogle Scholar
  24. 24.
    Cerami C, Della Rosa PA, Magnani G, Santangelo R, Marcone A, Cappa SF, et al. Brain metabolic maps in mild cognitive impairment predict heterogeneity of progression to dementia. NeuroImage Clin Elsevier BV. 2015;7:187–94.CrossRefGoogle Scholar
  25. 25.
    Caminiti SP, Ballarini T, Sala A, Cerami C, Presotto L, Santangelo R, et al. FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort. NeuroImage Clin Elsevier. 2018;18:167–77.CrossRefGoogle Scholar
  26. 26.
    Pilotto A, Premi E, Caminiti SP, Presotto L, Alberici A, Paghera B, et al. Single-subject SPM FDG-PET patterns predict risk of dementia progression in Parkinson’s disease. Neurology. 2018;90:e1029–37.CrossRefGoogle Scholar
  27. 27.
    Iaccarino L, Chiotis K, Alongi P, Almkvist O, Wall A, Cerami C, et al. A cross-validation of FDG- and amyloid-PET biomarkers in mild cognitive impairment for the risk prediction to dementia due to Alzheimer’s disease in a clinical setting. J Alzheimers Dis. 2017:1–12.Google Scholar
  28. 28.
    Pagani M, Chiò A, Valentini MC, Öberg J, Nobili F, Calvo A, et al. Functional pattern of brain FDG-PET in amyotrophic lateral sclerosis. Neurology. 2014;83:1067–74.CrossRefGoogle Scholar
  29. 29.
    Cistaro A, Valentini MC, Chiò A, Nobili F, Calvo A, Moglia C, et al. Brain hypermetabolism in amyotrophic lateral sclerosis: a FDG PET study in ALS of spinal and bulbar onset. Eur J Nucl Med Mol Imaging. 2012;39:251–9.CrossRefGoogle Scholar
  30. 30.
    Van LK, Vanhee A, Verschueren J, De CL, Driesen A, Dupont P, et al. Value of 18fluorodeoxyglucose-positron-emission tomography in amyotrophic lateral sclerosis a prospective study. JAMA Neurol. 2014;71:553–61.CrossRefGoogle Scholar
  31. 31.
    Matías-Guiu JA, Pytel V, Cabrera-Martín MN, Galán L, Valles-Salgado M, Guerrero A, et al. Amyloid- and FDG-PET imaging in amyotrophic lateral sclerosis. Eur J Nucl Med Mol Imaging. 2016;43:2050–60.CrossRefGoogle Scholar
  32. 32.
    Canosa A, Pagani M, Cistaro A, Montuschi A, Iazzolino B, Fania P, et al. 18F-FDG-PET correlates of cognitive impairment in ALS. Neurology. 2015;86:44–9.CrossRefGoogle Scholar
  33. 33.
    Agosta F, Altomare D, Festari C, Orini S, Gandolfo F, Boccardi M, et al. Clinical utility of FDG-PET in amyotrophic lateral sclerosis and Huntington disease. Eur J Nucl Med Mol Imaging. 2018;45:1546–56.CrossRefGoogle Scholar
  34. 34.
    Varrone A, Asenbaum S, Vander Borght T, Booij J, Nobili F, Någren K, et al. EANM procedure guidelines for PET brain imaging using [18F] FDG, version 2. Eur J Nucl Med Mol Imaging Springer. 2009;36:2103–10.CrossRefGoogle Scholar
  35. 35.
    Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al. A standardized [(18)F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics. 2014;12:575–93.CrossRefGoogle Scholar
  36. 36.
    Gallivanone F, Della Rosa P, Perani D, Gilardi MC, Castiglioni I. The impact of different 18FDG PET healthy subject scans for comparison with single patient in SPM analysis. Q J Nucl Med Mol Imaging. 2017;6(1):115–32.Google Scholar
  37. 37.
    Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 2002;15:273–89.CrossRefGoogle Scholar
  38. 38.
    Sallet J, Mars RB, Noonan MP, Neubert F-X, Jbabdi S, O’Reilly JX, et al. The organization of dorsal frontal cortex in humans and macaques. J Neurosci. 2013;33:12255–74.CrossRefGoogle Scholar
  39. 39.
    Mayka MA, Corcos DM, Leurgans SE, Vaillancourt DE. Three-dimensional locations and boundaries of motor and premotor cortices as defined by functional brain imaging: a meta-analysis. NeuroImage. 2006;31:1453–74.CrossRefGoogle Scholar
  40. 40.
    Tziortzi AC, Searle GE, Tzimopoulou S, Salinas C, Beaver JD, Jenkinson M, et al. Imaging dopamine receptors in humans with [11C]-(+)-PHNO: dissection of D3 signal and anatomy. NeuroImage. 2011;54:264–77.CrossRefGoogle Scholar
  41. 41.
    Lancaster J, Rainey L, Summerlin J, Freitas C. Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp. 1997;5:238–42.CrossRefGoogle Scholar
  42. 42.
    Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10:120–31.CrossRefGoogle Scholar
  43. 43.
    Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage. 2003;19:1233–9.CrossRefGoogle Scholar
  44. 44.
    Diedrichsen J, Maderwald S, Küper M, Thürling M, Rabe K, Gizewski ER, et al. Imaging the deep cerebellar nuclei: a probabilistic atlas and normalization procedure. NeuroImage. 2011;54:1786–94.CrossRefGoogle Scholar
  45. 45.
    Pagani M, Öberg J, De Carli F, Calvo A, Moglia C, Canosa A, et al. Metabolic spatial connectivity in amyotrophic lateral sclerosis as revealed by independent component analysis. Hum Brain Mapp. 2016;37:942–53.CrossRefGoogle Scholar
  46. 46.
    Cauda F, Giuliano G, Federico D, Sergio D, Katiuscia S. Discovering the somatotopic organization of the motor areas of the medial wall using low-frequency bold fluctuations. Hum Brain Mapp. 2011;32:1566–79.CrossRefGoogle Scholar
  47. 47.
    Bennett CM, Wolford GL, Miller MB. The principled control of false positives in neuroimaging. Soc Cogn Affect Neurosci. 2009;4:417–22.CrossRefGoogle Scholar
  48. 48.
    Endo H, Sekiguchi K, Ueda T, Kowa H, Kanda F, Toda T. Regional glucose hypometabolic spread within the primary motor cortex is associated with amyotrophic lateral sclerosis disease progression: a fluoro-deoxyglucose positron emission tomography study. eNeurologicalSci. 2017;6:74–9.CrossRefGoogle Scholar
  49. 49.
    Verstraete E, Veldink JH, Hendrikse J, Schelhaas HJ, Van Den Heuvel MP, Van Den Berg LH. Structural MRI reveals cortical thinning in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2012;83:383–8.CrossRefGoogle Scholar
  50. 50.
    Genç B, Jara JH, Lagrimas AKB, Pytel P, Roos RP, Mesulam MM, et al. Apical dendrite degeneration, a novel cellular pathology for Betz cells in ALS. Sci Rep Nature Publishing Group. 2017;7:41765.Google Scholar
  51. 51.
    Baker MR. ALS—dying forward, backward or outward? Nat Rev Neurol. 2014;10:660–0.Google Scholar
  52. 52.
    Chou SM, Norris FH. Amyotrophic lateral sclerosis: lower motor neuron disease spreading to upper motor neurons. Muscle Nerve. 1993;16:864–9.CrossRefGoogle Scholar
  53. 53.
    Eisen A, Weber M. The motor cortex and amyotrophic lateral sclerosis. Muscle Nerve. 2001;24:564–73.CrossRefGoogle Scholar
  54. 54.
    Yamanaka K, Chun SJ, Boillee S, Fujimori-Tonou N, Yamashita H, Gutmann DH, et al. Astrocytes as determinants of disease progression in inherited amyotrophic lateral sclerosis. Nat Neurosci. 2008;11:251–3.CrossRefGoogle Scholar
  55. 55.
    Philips T, Robberecht W. Neuroinflammation in amyotrophic lateral sclerosis: role of glial activation in motor neuron disease. Lancet Neurol Elsevier Ltd. 2011;10:253–63.CrossRefGoogle Scholar
  56. 56.
    Turner MR, Cagnin A, Turkheimer FE, Miller CCJ, Shaw CE, Brooks DJ, et al. Evidence of widespread cerebral microglial activation in amyotrophic lateral sclerosis: an [11C](R)-PK11195 positron emission tomography study. Neurobiol Dis. 2004;15:601–9.CrossRefGoogle Scholar
  57. 57.
    Schroeter M, Dennin MA, Walberer M, Backes H, Neumaier B, Fink GR, et al. Neuroinflammation extends brain tissue at risk to vital peri-infarct tissue: a double tracer [11C]PK11195- and [18F]FDG-PET study. J Cereb Blood Flow Metab. 2009;29:1216–25.CrossRefGoogle Scholar
  58. 58.
    Turner MR, Kiernan MC. Does interneuronal dysfunction contribute to neurodegeneration in amyotrophic lateral sclerosis? Amyotroph Lateral Scler. 2012;13:245–50.CrossRefGoogle Scholar
  59. 59.
    Sibson NR, Dhankhar A, Mason GF, Rothman DL, Behar KL, Shulman RG. Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity. Neurobiology. 1998;95:316–21.Google Scholar
  60. 60.
    Schreiber H, Gaigalat T, Wiedemuth-Catrinescu U, Graf M, Uttner I, Muche R, et al. Cognitive function in bulbar- and spinal-onset amyotrophic lateral sclerosis: a longitudinal study in 52 patients. J Neurol. 2005;252:772–81.CrossRefGoogle Scholar
  61. 61.
    Iaccarino L, Sala A, Caminiti SP, Perani D. The emerging role of PET imaging in dementia. F1000Research. 2017;6:1830.CrossRefGoogle Scholar
  62. 62.
    Takeuchi R, Tada M, Shiga A, Toyoshima Y, Konno T, Sato T, et al. Heterogeneity of cerebral TDP-43 pathology in sporadic amyotrophic lateral sclerosis: evidence for clinico-pathologic subtypes. Acta Neuropathol Commun Acta Neuropathologica Communications. 2016;4:61.CrossRefGoogle Scholar
  63. 63.
    Nishihira Y, Tan CF, Onodera O, Toyoshima Y, Yamada M, Morita T, et al. Sporadic amyotrophic lateral sclerosis: two pathological patterns shown by analysis of distribution of TDP-43-immunoreactive neuronal and glial cytoplasmic inclusions. Acta Neuropathol. 2008;116:169–82.CrossRefGoogle Scholar
  64. 64.
    Sabatelli M, Conte A, Zollino M. Clinical and genetic heterogeneity of amyotrophic lateral sclerosis. Clin Genet. 2013;83:408–16.CrossRefGoogle Scholar
  65. 65.
    Willekens SMA, Van Weehaeghe D, Van Damme P, Van Laere K. Positron emission tomography in amyotrophic lateral sclerosis: towards targeting of molecular pathological hallmarks. Eur J Nucl Med Mol Imaging. 2016:1–15.Google Scholar
  66. 66.
    Hoffman JM, Mazziotta JC, Hawk TC, Sumida R. Cerebral glucose utilization in motor neuron disease. Arch Neurol. 1992;49:849–54.CrossRefGoogle Scholar
  67. 67.
    Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013;80:807–15.CrossRefGoogle Scholar
  68. 68.
    Mottolese C, Richard N, Harquel S, Szathmari A, Sirigu A, Desmurget M. Mapping motor representations in the human cerebellum. Brain. 2013;136:330–42.CrossRefGoogle Scholar
  69. 69.
    Prell T, Grosskreutz J. The involvement of the cerebellum in amyotrophic lateral sclerosis. Amyotroph Lateral Scler Front Degener. 2013;14:507–15.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Arianna Sala
    • 1
    • 2
  • Leonardo Iaccarino
    • 1
    • 2
  • Piercarlo Fania
    • 3
  • Emilia G. Vanoli
    • 4
  • Federico Fallanca
    • 4
  • Caterina Pagnini
    • 2
  • Chiara Cerami
    • 2
    • 5
  • Andrea Calvo
    • 6
  • Antonio Canosa
    • 6
  • Marco Pagani
    • 7
    • 8
  • Adriano Chiò
    • 6
    • 7
    • 9
  • Angelina Cistaro
    • 10
  • Daniela Perani
    • 1
    • 2
    • 4
    Email author
  1. 1.Vita-Salute San Raffaele UniversityMilanItaly
  2. 2.In Vivo Human Molecular and Structural Neuroimaging Unit, Division of NeuroscienceIRCCS San Raffaele Scientific InstituteMilanItaly
  3. 3.Positron Emission Tomography Centre IRMET, AffideaTurinItaly
  4. 4.Nuclear Medicine Unit, IRCCS San Raffaele HospitalMilanItaly
  5. 5.Clinical Neuroscience DepartmentSan Raffaele Turro HospitalMilanItaly
  6. 6.ALS Center, ‘Rita Levi Montalcini’ Department of NeuroscienceUniversity of TurinTurinItaly
  7. 7.Institute of Cognitive Sciences and Technologies, C.N.RRomeItaly
  8. 8.Department of Nuclear MedicineKarolinska HospitalStockholmSweden
  9. 9.Neuroscience Institute of TurinTurinItaly
  10. 10.Department of Neuroscience, Advisor Nuclear Medicine for Amiotrophic Lateral SclerosisRegional Expert CenterUniversity of TurinTurinItaly

Personalised recommendations