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Current Alzheimer Research

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

Hybrid PET/MRI with Flutemetamol and FDG in Alzheimer's Disease Clinical Continuum

Author(s): Lutfiye Ozlem Atay*, Esen Saka, Umit Ozgur Akdemir, Ezgi Yetim, Erdem Balcı, Ethem Murat Arsava and Mehmet Akif Topcuoglu

Volume 20, Issue 7, 2023

Published on: 10 October, 2023

Page: [481 - 495] Pages: 15

DOI: 10.2174/0115672050243131230925034334

Price: $65

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Abstract

Aims: We aimed to investigate the interaction between β -amyloid (Aβ) accumulation and cerebral glucose metabolism, cerebral perfusion, and cerebral structural changes in the Alzheimer's disease (AD) clinical continuum.

Background: Utility of positron emission tomography (PET) / magnetic resonance imaging (MRI) hybrid imaging for diagnostic categorization of the AD clinical continuum including subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease dementia (ADD) has not been fully crystallized.

Objective: To evaluate the interaction between Aβ accumulation and cerebral glucose metabolism, cerebral perfusion, and cerebral structural changes such as cortex thickness or cerebral white matter disease burden and to detect the discriminative yields of these imaging modalities in the AD clinical continuum.

Methods: Fifty patients (20 women and 30 men; median age: 64 years) with clinical SCD (n=11), aMCI (n=17) and ADD (n=22) underwent PET/MRI with [18F]-fluoro-D-glucose (FDG) and [18F]- Flutemetamol in addition to cerebral blood flow (CBF) and quantitative structural imaging along with detailed cognitive assessment.

Results: High Aβ deposition (increased temporal [18F]-Flutemetamol standardized uptake value ratio (SUVr) and centiloid score), low glucose metabolism (decreased temporal lobe and posterior cingulate [18F]-FDG SUVr), low parietal CBF and right hemispheric cortical thickness were independent predictors of low cognitive test performance.

Conclusion: Integrated use of structural, metabolic, molecular (Aβ) and perfusion (CBF) parameters contribute to the discrimination of SCD, aMCI, and ADD.

« Previous Next »
[1]
Hyman BT, Phelps CH, Beach TG, et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement 2012; 8(1): 1-13.
[http://dx.doi.org/10.1016/j.jalz.2011.10.007] [PMID: 22265587]
[2]
Jack CR Jr, Bennett DA, Blennow K, et al. NIA‐AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14(4): 535-62.
[http://dx.doi.org/10.1016/j.jalz.2018.02.018] [PMID: 29653606]
[3]
Ossenkoppele R, Schonhaut DR, Schöll M, et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain 2016; 139(5): 1551-67.
[http://dx.doi.org/10.1093/brain/aww027] [PMID: 26962052]
[4]
Varrone A, Asenbaum S, Vander Borght T, et al. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging 2009; 36(12): 2103-10.
[http://dx.doi.org/10.1007/s00259-009-1264-0] [PMID: 19838705]
[5]
Mainta IC, Vargas MI, Trombella S, Frisoni GB, Unschuld PG, Garibotto V. Hybrid PET-MRI in Alzheimer’s disease research. Methods Mol Biol 2018; 1750: 185-200.
[http://dx.doi.org/10.1007/978-1-4939-7704-8_12] [PMID: 29512073]
[6]
Drzezga A, Barthel H, Minoshima S, Sabri O. Potential clinical applications of PET/MR imaging in neurodegenerative diseases. J Nucl Med 2014; 55 (Suppl. 2): 47S-55S.
[http://dx.doi.org/10.2967/jnumed.113.129254] [PMID: 24819417]
[7]
Chen Y, Wang J, Cui C, et al. Evaluating the association between brain atrophy, hypometabolism, and cognitive decline in Alzheimer’s disease: A PET/MRI study. Aging 2021; 13(5): 7228-46.
[http://dx.doi.org/10.18632/aging.202580] [PMID: 33640881]
[8]
Yan S, Zheng C, Cui B, et al. Multiparametric imaging hippocampal neurodegeneration and functional connectivity with simultaneous PET/MRI in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2020; 47(10): 2440-52.
[http://dx.doi.org/10.1007/s00259-020-04752-8] [PMID: 32157432]
[9]
Marchitelli R, Aiello M, Cachia A, et al. Simultaneous resting-state FDG-PET/fMRI in Alzheimer Disease: Relationship between glucose metabolism and intrinsic activity. Neuroimage 2018; 176: 246-58.
[http://dx.doi.org/10.1016/j.neuroimage.2018.04.048] [PMID: 29709628]
[10]
Okazawa H, Ikawa M, Jung M, et al. Multimodal analysis using [11C]PiB-PET/MRI for functional evaluation of patients with Alzheimer’s disease. EJNMMI Res 2020; 10(1): 30.
[http://dx.doi.org/10.1186/s13550-020-00619-z] [PMID: 32232573]
[11]
van Bergen JMG, Li X, Quevenco FC, et al. Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and β-amyloid as an indicator of cognitive performance at high age. Neuroimage 2018; 174: 308-16.
[http://dx.doi.org/10.1016/j.neuroimage.2018.03.021] [PMID: 29548847]
[12]
Prato FS, Pavlosky WF, Foster SC, Thiessen JD, Beaujot RP. Screening for dementia caused by modifiable lifestyle choices using hybrid PET/MRI. J Alzheimers Dis Rep 2019; 3(1): 31-45.
[http://dx.doi.org/10.3233/ADR-180098] [PMID: 30842996]
[13]
McKhann GM, Knopman DS, Chertkow H, 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(3): 263-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.005] [PMID: 21514250]
[14]
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”.A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12(3): 189-98.
[http://dx.doi.org/10.1016/0022-3956(75)90026-6] [PMID: 1202204]
[15]
Gungen C, Ertan T, Eker E, Yasar R, Engin F. Reliability and validity of the standardized Mini Mental State Examination in the diagnosis of mild dementia in Turkish population. Turk Psikiyatr Derg 2002; 13(4): 273-81.
[16]
Saka E, Mihci E, Topcuoglu M, Balkan S. Enhanced cued recall has a high utility as a screening test in the diagnosis of Alzheimer’s disease and mild cognitive impairment in Turkish people. Arch Clin Neuropsychol 2006; 21(7): 745-51.
[http://dx.doi.org/10.1016/j.acn.2006.08.007] [PMID: 16979317]
[17]
Cangoz B, Karakoc E, Selekler K. Trail Making Test: Normative data for Turkish elderly population by age, sex and education. J Neurol Sci 2009; 283(1-2): 73-8.
[http://dx.doi.org/10.1016/j.jns.2009.02.313] [PMID: 19264326]
[18]
Stroop JR. Studies of interference in serial verbal reactions. J Experimen Psychol 1935; 18(6): 643-62.
[19]
Cangoz BKE, Selekler K. The norm determination and validityreliability studies of clock drawing test on Turkish adults and elderlys (ages 50 and over). Turk Geriatri Derg 2006; 9: 136-42.
[20]
Hisli N. A reliability and validity study of Beck Depression Inventory in a university student sample. J Psychol 1989; 7: 3-13.
[21]
Ertan T, Eker E, Şar V. The validity and reliability of the geriatric depression scale in the Turkish elderly population. Noro Psikiyatri Arsivi 1997; 34(2): 62-71.
[22]
O’Bryant SE, Waring SC, Cullum CM, et al. Staging dementia using clinical dementia rating scale sum of boxes scores: A Texas Alzheimer’s research consortium study. Arch Neurol 2008; 65(8): 1091-5.
[http://dx.doi.org/10.1001/archneur.65.8.1091] [PMID: 18695059]
[23]
Lancaster JL, Woldorff MG, Parsons LM, et al. Automated Talairach Atlas labels for functional brain mapping. Hum Brain Mapp 2000; 10(3): 120-31.
[http://dx.doi.org/10.1002/1097-0193(200007)10:3<120:AID-HBM30>3.0.CO;2-8] [PMID: 10912591]
[24]
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(3): 1233-9.
[http://dx.doi.org/10.1016/S1053-8119(03)00169-1] [PMID: 12880848]
[25]
Klunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD Sr, Jagust WJ, et al. he centiloid Project: Standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement 2015; 11(1): 1-15.
[http://dx.doi.org/10.1016/j.jalz.2014.07.003]
[26]
Battle MR, Pillay LC, Lowe VJ, et al. Centiloid scaling for quantification of brain amyloid with [18F]flutemetamol using multiple processing methods. EJNMMI Res 2018; 8(1): 107.
[http://dx.doi.org/10.1186/s13550-018-0456-7] [PMID: 30519791]
[27]
Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 1999; 9(2): 179-94.
[http://dx.doi.org/10.1006/nimg.1998.0395] [PMID: 9931268]
[28]
Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006; 31(3): 968-80.
[http://dx.doi.org/10.1016/j.neuroimage.2006.01.021] [PMID: 16530430]
[29]
Schmidt P, Gaser C, Arsic M, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage 2012; 59(4): 3774-83.
[http://dx.doi.org/10.1016/j.neuroimage.2011.11.032] [PMID: 22119648]
[30]
Mato Abad V, García-Polo P, O’Daly O, Hernández-Tamames JA, Zelaya F. ASAP (Automatic Software for ASL Processing): A toolbox for processing Arterial Spin Labeling images. Magn Reson Imaging 2016; 34(3): 334-44.
[http://dx.doi.org/10.1016/j.mri.2015.11.002] [PMID: 26612079]
[31]
Minoshima S, Mosci K, Cross D, Thientunyakit T. Brain [F-18]FDG PET for clinical dementia workup: Differential diagnosis of alzheimer’s disease and other types of dementing disorders. Semin Nucl Med 2021; 51(3): 230-40.
[http://dx.doi.org/10.1053/j.semnuclmed.2021.01.002] [PMID: 33546814]
[32]
Brown RKJ, Bohnen NI, Wong KK, Minoshima S, Frey KA. Brain PET in suspected dementia: Patterns of altered FDG metabolism. Radiographics 2014; 34(3): 684-701.
[http://dx.doi.org/10.1148/rg.343135065] [PMID: 24819789]
[33]
Bucci M, Savitcheva I, Farrar G, et al. A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [18F]flutemetamol amyloid PET images. Eur J Nucl Med Mol Imaging 2021; 48(7): 2183-99.
[http://dx.doi.org/10.1007/s00259-021-05311-5] [PMID: 33844055]
[34]
Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 1987; 149(2): 351-6.
[http://dx.doi.org/10.2214/ajr.149.2.351] [PMID: 3496763]
[35]
Doubal FN, MacLullich AMJ, Ferguson KJ, Dennis MS, Wardlaw JM. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke 2010; 41(3): 450-4.
[http://dx.doi.org/10.1161/STROKEAHA.109.564914] [PMID: 20056930]
[36]
Oldan JD, Jewells VL, Pieper B, Wong TZ. Complete evaluation of dementia: PET and MRI correlation and diagnosis for the neuroradiologist. AJNR Am J Neuroradiol 2021; 42(6): 998-1007.
[http://dx.doi.org/10.3174/ajnr.A7079] [PMID: 33926896]
[37]
Smailagic N, Vacante M, Hyde C, Martin S, Ukoumunne O, Sachpekidis C. 18F-FDG PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Libr 2015; 1(1): CD010632.
[http://dx.doi.org/10.1002/14651858.CD010632.pub2] [PMID: 25629415]
[38]
Khosravi M, Peter J, Wintering NA, et al. 18F-FDG Is a superior indicator of cognitive performance compared to 18F-florbetapir in alzheimer’s disease and mild cognitive impairment evaluation: A global quantitative analysis. J Alzheimers Dis 2019; 70(4): 1197-207.
[http://dx.doi.org/10.3233/JAD-190220] [PMID: 31322568]
[39]
Leuzy A, Savitcheva I, Chiotis K, et al. Clinical impact of [18F]flutemetamol PET among memory clinic patients with an unclear diagnosis. Eur J Nucl Med Mol Imaging 2019; 46(6): 1276-86.
[http://dx.doi.org/10.1007/s00259-019-04297-5] [PMID: 30915522]
[40]
Hanseeuw BJ, Malotaux V, Dricot L, et al. Defining a Centiloid scale threshold predicting long-term progression to dementia in patients attending the memory clinic: An [18F] flutemetamol amyloid PET study. Eur J Nucl Med Mol Imaging 2021; 48(1): 302-10.
[http://dx.doi.org/10.1007/s00259-020-04942-4] [PMID: 32601802]
[41]
Jagust W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci 2018; 19(11): 687-700.
[http://dx.doi.org/10.1038/s41583-018-0067-3] [PMID: 30266970]
[42]
Forsberg A, Engler H, Almkvist O, et al. PET imaging of amyloid deposition in patients with mild cognitive impairment. Neurobiol Aging 2008; 29(10): 1456-65.
[http://dx.doi.org/10.1016/j.neurobiolaging.2007.03.029] [PMID: 17499392]
[43]
Jagust WJ, Landau SM. Temporal dynamics of β-amyloid accumulation in aging and alzheimer disease. Neurology 2021; 96(9): e1347-57.
[http://dx.doi.org/10.1212/WNL.0000000000011524] [PMID: 33408147]
[44]
Tosun D, Schuff N, Jagust W, Weiner MW. Discriminative power of arterial spin labeling magnetic resonance imaging and 18F-fluorodeoxyglucose positron emission tomography changes for amyloid-β-positive subjects in the alzheimer's disease continuum. Neurodegener Dis 2016; 16(1-2): 87-94.
[http://dx.doi.org/10.1159/000439257] [PMID: 26560336]
[45]
Duan W, Zhou GD, Balachandrasekaran A, et al. Cerebral blood flow predicts conversion of mild cognitive impairment into alzheimer’s disease and cognitive decline: An arterial spin labeling follow-up study. J Alzheimers Dis 2021; 82(1): 293-305.
[http://dx.doi.org/10.3233/JAD-210199] [PMID: 34024834]
[46]
Camargo A, Wang Z. Longitudinal cerebral blood flow changes in normal aging and the alzheimer’s disease continuum identified by arterial spin labeling MRI. J Alzheimers Dis 2021; 81(4): 1727-35.
[http://dx.doi.org/10.3233/JAD-210116] [PMID: 33967053]
[47]
Kang SH, Kim ME, Jang H, et al. Amyloid positivity in the alzheimer/subcortical-vascular spectrum. Neurology 2021; 96(17): e2201-11.
[http://dx.doi.org/10.1212/WNL.0000000000011833] [PMID: 33722997]
[48]
Nichols JB, Malek-Ahmadi M, Tariot PN, Serrano GE, Sue LI, Beach TG. Vascular lesions, APOE ε4, and tau pathology in alzheimer disease. J Neuropathol Exp Neurol 2021; 80(3): 240-6.
[http://dx.doi.org/10.1093/jnen/nlaa160] [PMID: 33617650]
[49]
Bozzali M, Serra L, Cercignani M. Quantitative MRI to understand Alzheimer’s disease pathophysiology. Curr Opin Neurol 2016; 29(4): 437-44.
[http://dx.doi.org/10.1097/WCO.0000000000000345] [PMID: 27228309]
[50]
Ghazi-Saidi L, Walsh RR, Shan G, Banks SJ. Biomarkers of cognitive impairment. Alzheimer Dis Assoc Disord 2018; 32(3): 255-7.
[http://dx.doi.org/10.1097/WAD.0000000000000226] [PMID: 29189303]

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