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Brain metabolic signatures across the Alzheimer’s disease spectrum

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Abstract

Purpose

Given the challenges posed by the clinical diagnosis of atypical Alzheimer’s disease (AD) variants and the limited imaging evidence available in the prodromal phases of atypical AD, we assessed brain hypometabolism patterns at the single-subject level in the AD variants spectrum. Specifically, we tested the accuracy of [18F]FDG-PET brain hypometabolism, as a biomarker of neurodegeneration, in supporting the differential diagnosis of atypical AD variants in individuals with dementia and mild cognitive impairment (MCI).

Methods

We retrospectively collected N = 67 patients with a diagnosis of typical AD and AD variants according to the IWG-2 criteria (22 typical-AD, 15 frontal variant-AD, 14 logopenic variant-AD and 16 posterior variant-AD). Further, we included N = 11 MCI subjects, who subsequently received a clinical diagnosis of atypical AD dementia at follow-up (21 ± 11 months). We assessed brain hypometabolism patterns at group- and single-subject level, using W-score maps, measuring their accuracy in supporting differential diagnosis. In addition, the regional prevalence of cerebral hypometabolism was computed to identify the most vulnerable core regions.

Results

W-score maps pointed at distinct, specific patterns of hypometabolism in typical and atypical AD variants, confirmed by the assessment of core hypometabolism regions, showing that each variant was characterized by specific regional vulnerabilities, namely in occipital, left-sided, or frontal brain regions. ROC curves allowed discrimination among AD variants and also non-AD dementia (i.e., dementia with Lewy bodies and behavioral variant of frontotemporal dementia), with high sensitivity and specificity. Notably, we provide preliminary evidence that, even in AD prodromal phases, these specific [18F]FDG-PET patterns are already detectable and predictive of clinical progression to atypical AD variants at follow-up.

Conclusions

The AD variant-specific patterns of brain hypometabolism, highly consistent at single-subject level and already evident in the prodromal stages, represent relevant markers of disease neurodegeneration, with highly supportive diagnostic and prognostic role.

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References

  1. Warren JD, Fletcher PD, Golden HL. The paradox of syndromic diversity in Alzheimer disease. Nat Rev Neurol. 2012;8:451–64.

    CAS  PubMed  Google Scholar 

  2. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984;34:939–44.

    CAS  PubMed  Google Scholar 

  3. Crutch SJ, Schott JM, Rabinovici GD, Murray M, Snowden JS, Van Der Flier WM, et al. Consensus classification of posterior cortical atrophy. Alzheimers Dement. 2017;13:870–84.

    PubMed  PubMed Central  Google Scholar 

  4. Crutch SJ, Lehmann M, Schott JM, Rabinovici GD, Rossor MN, Fox NC. Posterior cortical atrophy. Lancet Neurol. 2012;11:170–8.

    PubMed  PubMed Central  Google Scholar 

  5. 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;11:1006–14.

    Google Scholar 

  6. Teichmann M, Kas A, Boutet C, Ferrieux S, Nogues M, Samri D, et al. Deciphering logopenic primary progressive aphasia: a clinical, imaging and biomarker investigation. Brain. 2013;136:3474–88.

    PubMed  Google Scholar 

  7. Ossenkoppele R, Pijnenburg YAL, Perry DC, Cohn-Sheehy BI, Scheltens NME, Vogel JW, et al. The behavioural/dysexecutive variant of Alzheimer’s disease: clinical, neuroimaging and pathological features. Brain. 2015;138:2732–49.

    PubMed  PubMed Central  Google Scholar 

  8. Taylor KI, Probst A, Miserez AR, Monsch AU, Tolnay M. Clinical course of neuropathologically confirmed frontal-variant Alzheimer’s disease. Nat Clin Pract Neurol. 2008;4:226–32.

    PubMed  Google Scholar 

  9. Whitwell JL, Graff-radford J, Tosakulwong N, Weigand SD, Machulda MM, Senjem ML, et al. Imaging correlations of tau, amyloid, metabolism, and atrophy in typical and atypical Alzheimer’s disease. Alzheimers Dement. 2018;14:1005–14.

    PubMed  PubMed Central  Google Scholar 

  10. 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. Alzheimers Dement. 2011;7:263–9.

    PubMed  PubMed Central  Google Scholar 

  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.

    PubMed  Google Scholar 

  12. Koedam ELGE, Lauffer V, Van Der Vlies AE, Van Der Flier WM, Scheltens P, Pijnenburg YAL. Early-versus late-onset Alzheimer’s disease: more than age alone. J Alzheimers Dis. 2010;19:1401–8.

    PubMed  Google Scholar 

  13. Lukic AS, Andrews RD, Bourakova V, Rabinovici GD, Matthews DC. MRI, FDG and early frame amyloid image classifiers to characterize and differentiate Alzheimer’s disease variants and non-AD dementias. Alzheimers Dement. 2018;14:1429–30.

    Google Scholar 

  14. Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14:535–62.

    PubMed  PubMed Central  Google Scholar 

  15. Stoessl AJ. Glucose utilization: still in the synapse. Nat Neurosci. 2017;20:382–4.

    CAS  PubMed  Google Scholar 

  16. Perani D. FDG-PET and amyloid-PET imaging: the diverging paths. Curr Opin Neurol. 2014;27:405–13.

    CAS  PubMed  Google Scholar 

  17. Taswell C, Villemagne VL, Yates P, Shimada H, Leyton CE, Ballard KJ, et al. 18F-FDG PET improves diagnosis in patients with focal-onset dementias. J Nucl Med. 2015;56:1547–53.

    CAS  PubMed  Google Scholar 

  18. Iaccarino L, Sala A, Caminiti SP, Perani D. The emerging role of PET imaging in dementia. F1000Research. 2017;6:1830.

    PubMed  PubMed Central  Google Scholar 

  19. Caminiti SP, Sala A, Iaccarino L, Beretta L, Pilotto A, Gianolli L, et al. Brain glucose metabolism in Lewy body dementia : implications for diagnostic criteria. Alzheimers Res Ther. 2019;11:20.

    PubMed  PubMed Central  Google Scholar 

  20. 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. 2018;28:167–77.

    Google Scholar 

  21. 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. 2014;5:187–94.

    Google Scholar 

  22. Sörensen A, Blazhenets G, Rücker G, Schiller F, Meyer PT, Frings L. Prognosis of conversion of mild cognitive impairment to Alzheimer’ s dementia by voxel-wise cox regression based on FDG PET data. NeuroImage Clin. 2019;21:101637.

    PubMed  Google Scholar 

  23. Nestor PJ, Altomare D, Festari C, Drzezga A, Rivolta J, Walker Z, et al. Clinical utility of FDG-PET for the differential diagnosis among the main forms of dementia. Eur J Nucl Med Mol Imaging. 2018;45:1509–25.

    PubMed  Google Scholar 

  24. La Joie R, Perrotin A, Barre L, Hommet C, Mezenge F, Ibazizene M, et al. Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (Aβ) load in Alzheimer’s disease dementia. J Neurosci. 2012;32:16265–73.

    PubMed  PubMed Central  Google Scholar 

  25. Sjogren M, Vanderstichele H, Hans Å, Zachrisson O, Edsbagge M, Wikkelsø C, et al. Tau and Ab42 in cerebrospinal fluid from healthy adults 21–93 years of age : establishment of reference values. Clin Chem. 2001;47:1776–81.

    CAS  PubMed  Google Scholar 

  26. Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, et al. Mild cognitive impairment ten years later. Arch Neurol. 2009;66:1447–55.

    PubMed  PubMed Central  Google Scholar 

  27. Borruat FX. Posterior cortical atrophy : review of the recent literature. Curr Neurol Neurosci Rep. 2013;13:406.

    PubMed  Google Scholar 

  28. Sabbagh MN, Schäuble B, Anand K, Richards D, Murayama S, Akatsu H, et al. Histopathology and florbetaben PET in patients incorrectly diagnosed with Alzheimer’s disease. J Alzheimers Dis. 2017;56:441–6.

    CAS  PubMed  Google Scholar 

  29. McKeith IG, Dickson DW, Lowe J, Emre M, Brien JTO, Feldman H, et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium. Neurology. 2005;65:1863–72.

    CAS  PubMed  Google Scholar 

  30. 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.

    PubMed  PubMed Central  Google Scholar 

  31. Suárez-González A, Crutch SJ, Franco-Macias E, Gil-Néciga E. Neuropsychiatric symptoms in posterior cortical atrophy and Alzheimer disease. J Geriatr Psychiatry Neurol. 2016;29:65–71.

    PubMed  PubMed Central  Google Scholar 

  32. Josephs KA, Whitwell JL, Boeve BF, Knopman DS, Tang-Wai DF, Drubach DA, et al. Visual hallucinations in posterior cortical atrophy. Arch Neurol. 2006;63:1427–32.

    PubMed  PubMed Central  Google Scholar 

  33. Riedl V, Bienkowska K, Strobel C, Tahmasian M, Grimmer T, Friston KJ, et al. Local activity determines functional connectivity in the resting human brain : a simultaneous FDG-PET / fMRI study. J Neurosci. 2014;34:6260–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Della Rosa PA, Cerami C, Gallivanone F, Prestia A, Caroli A, Castiglioni I, et al. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics. 2014;12:575–93.

    PubMed  Google Scholar 

  35. Buchert R, Wilke F, Chakrabarti B, Martin B, Brenner W, Mester J, et al. Adjusted scaling of FDG positron emission tomography images for statistical evaluation in patients with suspected Alzheimer’s disease. J Neuroimaging. 2005;15:348–55.

    PubMed  Google Scholar 

  36. 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.

    CAS  PubMed  Google Scholar 

  37. Phillips JS, Da Re F, Dratch L, Xie SX, Irwin DJ, McMillan CT, et al. Neocortical origin and progression of gray matter atrophy in nonamnestic Alzheimer’s disease. Neurobiol Aging. 2018;63:75–87.

    PubMed  Google Scholar 

  38. Phillips JS, Das SR, McMillan CT, Irwin DJ, Roll EE, Da Re F, et al. Tau PET imaging predicts cognition in atypical variants of Alzheimer’s disease. Hum Brain Mapp. 2018;39:691–708.

    PubMed  Google Scholar 

  39. 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.

    CAS  PubMed  Google Scholar 

  40. 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;59:603–14.

    CAS  PubMed  Google Scholar 

  41. 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. 2017;55:183–97.

    PubMed  Google Scholar 

  42. Cerami C, Crespi C, Della Rosa PA, Dodich A, Marcone A, Magnani G, et al. Brain changes within the visuo-spatial attentional network in posterior cortical atrophy. J Alzheimers Dis. 2015;43:385–95.

    PubMed  Google Scholar 

  43. Smailagic N, Lafortune L, Kelly S, Hyde C, Brayne C. 18F-FDG PET for prediction of conversion to Alzheimer’s disease dementia in people with mild cognitive impairment: an updated systematic review of test accuracy. J Alzheimers Dis. 2018.

  44. 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. 2014;6:445–54.

    PubMed  PubMed Central  Google Scholar 

  45. 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.

    CAS  PubMed  Google Scholar 

  46. Iaccarino L, Sala A, Perani D. Predicting long-term clinical stability in amyloid-positive subjects by FDG-PET. Ann Clin Transl Neurol. 2019;6:1113–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 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. 2015;7:187–94.

    PubMed  Google Scholar 

  48. Drzezga A, Lautenschlager N, Siebner H, Riemenschneider M, Willoch F, Minoshima S, et al. Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study. Eur J Nucl Med Mol Imaging. 2003;30:1104–13.

    PubMed  Google Scholar 

  49. Mosconi L, Mistur R, Switalski R, Tsui WH, Glodzik L, Li Y, et al. FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2009;36:811–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Nestor PJ, Caine D, Fryer TD, Clarke J, Hodges JR. The topography of metabolic deficits in posterior cortical atrophy (the visual variant of Alzheimer’s disease) with FDG-PET. J Neurol Neurosurg Psychiatry. 2003;74:1521–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wilson SM, Henry ML, Besbris M, Ogar JM, Dronkers NF, Jarrold W, et al. Connected speech production in three variants of primary progressive aphasia. Brain. 2010;133:2069–88.

    PubMed  PubMed Central  Google Scholar 

  52. Rogalski E, Cobia D, Harrison TM, Wieneke C, Thompson CK, Weintraub S, et al. Anatomy of language impairments in primary progressive aphasia. J Neurosci. 2011;31:3344–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Madhavan A, Whitwell JL, Weigand SD, Duffy JR, Strand EA, Machulda MM, et al. FDG PET and MRI in Logopenic primary progressive aphasia versus dementia of the Alzheimer’s type. PLoS One. 2013;8:e62471.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Rabinovici GD, Jagust WJ, Furst AJ, Ogar JM, Racine CA, Mormino EC, et al. Aβ amyloid and glucose metabolism in three variants of primary progressive aphasia. Ann Neurol. 2008;64:388–401.

    PubMed  PubMed Central  Google Scholar 

  55. Matias-Guiu JA, Cabrera-Martín MN, Moreno-Ramos T, García-Ramos R, Porta-Etessam J, Carreras JL, et al. Clinical course of primary progressive aphasia: clinical and FDG-PET patterns. J Neurol. 2015;262:570–7.

    PubMed  Google Scholar 

  56. Rogalski E, Sridhar J, Rader B, Martersteck A, Chen K, Cobia D, et al. Aphasic variant of Alzheimer disease: clinical, anatomic, and genetic features. Neurology. 2016;87:1337–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Sajjadi SA, Sheikh-Bahaei N, Cross J, Gillard JH, Scoffings D, Nestor PJ. Can MRI visual assessment differentiate the variants of primary-progressive aphasia? Am J Neuroradiol. 2017;38:954–60.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Dronse J, Fliessbach K, Bischof GN, Von Reutern B, Faber J, Hammes J, et al. In vivo patterns of tau pathology, amyloid-β burden, and neuronal dysfunction in clinical variants of Alzheimer’s disease. J Alzheimers Dis. 2017;55:465–71.

    CAS  PubMed  Google Scholar 

  59. Woodward MC, Rowe CC, Jones G, Villemagne VL, Varos TA. Differentiating the frontal presentation of Alzheimer’s disease with FDG-PET. J Alzheimers Dis. 2015;44:233–42.

    PubMed  Google Scholar 

  60. Dickerson BC, Wolk DA. Dysexecutive versus amnesic phenotypes of very mild Alzheimer’s disease are associated with distinct clinical, genetic and cortical thinning characteristics. J Neurol Neurosurg Psychiatry. 2011;82:45–51.

    PubMed  Google Scholar 

  61. Lehmann M, Ghosh PM, Madison C, Laforce R, Corbetta-Rastelli C, Weiner MW, et al. Diverging patterns of amyloid deposition and hypometabolism in clinical variants of probable Alzheimer’s disease. Brain. 2013;136:844–58.

    PubMed  PubMed Central  Google Scholar 

  62. Hof PR, Vogt BA, Bouras C, Morrison JH. Atypical form of Alzheimer’s disease with prominent posterior cortical atrophy: a review of lesion distribution and circuit disconnection in cortical visual pathways. Vis Res. 1997;37:3609–25.

    CAS  PubMed  Google Scholar 

  63. Johnson JK, Head E, Kim R, Starr A, Cotman CW. Clinical and pathological evidence for a frontal variant of Alzheimer disease. Arch Neurol. 1999;56:1233–9.

    CAS  PubMed  Google Scholar 

  64. Mesulam MM, Weintraub S, Rogalski EJ, Wieneke C, Geula C, Bigio EH. Asymmetry and heterogeneity of Alzheimer’s and frontotemporal pathology in primary progressive aphasia. Brain. 2014;137:1176–92.

    PubMed  PubMed Central  Google Scholar 

  65. Silverman DHS, Gambhir SS, Huang HC, Schwimmer J, Kim S, Small GW, et al. Evaluating early dementia with and without assessment of regional cerebral metabolism by PET: a comparison of predicted costs and benefits. J Nucl Med. 2002;43:253–67.

    PubMed  Google Scholar 

  66. Cerami C, Dodich A, Lettieri G, Cappa SF, Perani D. Different FDG-PET metabolic patterns at single-subject level in the behavioral variant of frontotemporal dementia. Cortex. 2016;83:101–12.

    PubMed  Google Scholar 

  67. Teune LK, Bartels AL, De Jong BM, Willemsen ATM, Eshuis SA, De Vries JJ, et al. Typical cerebral metabolic patterns in neurodegenerative brain diseases. Mov Disord. 2010;25:2395–404.

    PubMed  Google Scholar 

  68. Whitwell JL, Graff-Radford J, Singh TD, Drubach DA, Senjem ML, Spychalla AJ, et al. 18 F-FDG PET in posterior cortical atrophy and dementia with Lewy bodies. J Nucl Med. 2017;58:632–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Gupta V, Verma R, Ranjan R, Belho E, Mahajan H. Lewy body dementia and posterior cortical variant of Alzheimer’s disease: distinguishing imaging patterns based on 18F-FDG PET/CT and 99mTc-TRODAT SPECT scan. J Nucl Med. 2019;60:1491.

    Google Scholar 

  70. Nedelska Z, Ferman TJ, Boeve BF, Przybelski SA, Lesnick TG, Murray ME, et al. Pattern of brain atrophy rates in autopsy-confirmed dementia with Lewy bodies. Neurobiol Aging. 2015;36:452–61.

    PubMed  Google Scholar 

  71. Middelkoop HAM, Van der Flier WM, Burton EJ, Lloyd AJ, Paling S, Barber R, et al. Dementia with Lewy bodies and AD are not associated with occipital lobe atrophy on MRI. Neurology. 2001;57:2117–20.

    CAS  PubMed  Google Scholar 

  72. O’Donovan J, Watson R, Colloby SJ, Firbank MJ, Burton EJ, Barber R, et al. Does posterior cortical atrophy on MRI discriminate between Alzheimer’ s disease, dementia with Lewy bodies, and normal aging? Int Psychogeriatr. 2012;25:111–9.

    PubMed  Google Scholar 

  73. Nordlund A, Rolstad S, Hellstro P, Sjo M, Hansen S, Wallin A. The Goteborg MCI study : mild cognitive impairment is a heterogeneous condition. J Neurol Neurosurg Psychiatry. 2005;76:1485–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Anchisi D, Borroni B, Franchesci M, Nasser K, Ferruccio F, Perani D. Heterogeneity of brain glucose metabolism in mild cognitive impairment and clinical progression to Alzheimer disease. Arch Neurol. 2005;62:1728–33.

    PubMed  Google Scholar 

  75. Shaffer JL, Petrella JR, Sheldon FC, Choudhury KR, Calhoun VD, Coleman RE, et al. Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers. Radiology. 2013;266:583–91.

    PubMed  PubMed Central  Google Scholar 

  76. Jagust W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci. 2018;19:687–700.

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Kljajevic V, Jan M, Ewers M, Teipel S. Distinct pattern of hypometabolism and atrophy in preclinical and predementia Alzheimer’s disease. Neurobiol Aging. 2014;35:1973–81.

    CAS  PubMed  Google Scholar 

  78. Raj A, Kuceyeski A, Weiner M. A network diffusion model of disease progression in dementia. Neuron. 2012;73:1204–15.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Sala A, Perani D. Brain molecular connectivity in neurodegenerative diseases: recent advances and new perspectives using Positron Emission Tomography. Front Neurosci. 2019;in press.

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Funding

This study was funded by the Italian Ministry of Health (Ricerca Finalizzata Progetto Reti Nazionale AD NET-2011-02346784), “IVASCOMAR project “Identificazione, validazione e sviluppo commerciale di nuovi biomarcatori diagnostici prognostici per malattie complesse” (grant agreement no. CTN01_00177_165430)”.

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  1. Arianna Sala and Camilla Caprioglio contributed equally to this work.

Authors and Affiliations

  1. Vita-Salute San Raffaele University, Milan, Italy

    Arianna Sala, Camilla Caprioglio & Daniela Perani

  2. In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Arianna Sala & Daniela Perani

  3. Department of Neurology and INSPE, San Raffaele Scientific Institute, Milan, Italy

    Roberto Santangelo & Giuseppe Magnani

  4. Nuclear Medicine Unit, San Raffaele Hospital, Via Olgettina 60, Milan, Italy

    Emilia Giovanna Vanoli & Daniela Perani

  5. Neurological Rehabilitation Unit, Clinical Neurosciences Department, IRCCS San Raffaele Hospital, Via Olgettina 60, 20132, Milan, Italy

    Sandro Iannaccone

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  1. Arianna Sala
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Correspondence to Daniela Perani.

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Sala, A., Caprioglio, C., Santangelo, R. et al. Brain metabolic signatures across the Alzheimer’s disease spectrum. Eur J Nucl Med Mol Imaging 47, 256–269 (2020). https://doi.org/10.1007/s00259-019-04559-2

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