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Cited by 109Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Imaging Techniques and Applications

Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration

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Levin, Fedor, Ferreira, Daniel, Lange, Catharina, Dyrba, Martin, Westman, Eric, Buchert, Ralph, Teipel, Stefan J., Grothe, Michel J. +1 more·2021·Alzheimer's Research & Therapy
DOI 10.1186/s13195-021-00785-9PMC PMC7896407

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Abstract

Background: Previous research has described distinct subtypes of Alzheimer’s disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes. Methods: Hierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment (“prodromal AD”) according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months. Results: Three main hypometabolic subtypes were identified: (i) “typical” (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) “limbic-predominant” (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare “cortical-predominant” subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline. Conclusions: These findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.

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  • Levin et al. used FDG-PET in the ADNI dataset to subtype AD into typical, limbic-predominant, and cortical-predominant types that correlate with brain atrophy subtypes and clinical trajectories.

    “Levin et al. used FDG-PET in the ADNI dataset to subtype AD in ‘typical’, ‘limbic-predominant’ and ‘cortical-predominant’ types that correlate with the brain atrophy subtypes and the different clinical trajectories [117].”

    From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [117]

    The Abstract Methods and Results describe FDG-PET subtyping in ADNI into the three named groups and report different cognitive trajectories in the prodromal AD sample. The Discussion (‘Distinct hypometabolic subtypes’) says the limbic-predominant pattern resembles an MRI-defined atrophy subtype but also differs from it; it does not establish a correlation between all three FDG-PET subtypes and brain-atrophy subtypes. (LLM, conf=0.83)

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  1. [2]Whitwell JL Dickson DW Murray ME Weigand SD Tosakulwong N Senjem ML Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study Lancet Neurol 2012 11 10 868 877 10.1016/S1474-4422(12)70200-4 22951070 PMC3490201 · DOI
  2. [3]Habes M, Grothe MJ, Tunc B, McMillan C, Wolk DA, Davatzikos C. Disentangling heterogeneity in Alzheimer’s disease and related dementias using data-driven methods. Biol Psychiatry. 2020;88(1):70–82. PMID: 32201044. PMC7305953 32201044
  3. [4]Ten Kate M Dicks E Visser PJ van der Flier WM Teunissen CE Barkhof F Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline Brain. 2018 141 12 3443 3456 10.1093/brain/awy264 30351346 PMC6669409 · DOI
  4. [5]Zhang X Mormino EC Sun N Sperling RA Sabuncu MR Yeo BT Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease Proc Natl Acad Sci U S A 2016 113 42 E6535 E6E44 10.1073/pnas.1611073113 27702899 PMC5081632 · DOI
  5. [7]Nobili F Arbizu J Bouwman F Drzezga A Agosta F Nestor P European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus Eur J Neurol 2018 25 10 1201 1217 10.1111/ene.13728 29932266 · DOI
  6. [8]Kljajevic V Grothe MJ Ewers M Teipel S Alzheimer’s disease neuroimaging I. Distinct pattern of hypometabolism and atrophy in preclinical and predementia Alzheimer’s disease Neurobiol Aging 2014 35 9 1973 1981 10.1016/j.neurobiolaging.2014.04.006 24811241 · DOI
  7. [9]Perani D FDG-PET and amyloid-PET imaging: the diverging paths Curr Opin Neurol 2014 27 4 405 413 10.1097/WCO.0000000000000109 24927239 · DOI
  8. [10]Shaffer JL Petrella JR Sheldon FC Choudhury KR Calhoun VD Coleman RE Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers Radiology. 2013 266 2 583 591 10.1148/radiol.12120010 23232293 PMC3558874 · DOI
  9. [11]Collette F Van der Linden M Delrue G Salmon E Frontal hypometabolism does not explain inhibitory dysfunction in Alzheimer disease Alzheimer Dis Assoc Disord 2002 16 4 228 238 10.1097/00002093-200210000-00004 12468897 · DOI
  10. [12]Mosconi L Tsui WH Herholz K Pupi A Drzezga A Lucignani G Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias J Nucl Med 2008 49 3 390 398 10.2967/jnumed.107.045385 18287270 PMC3703818 · DOI · Paper page
  11. [13]Meyer F Wehenkel M Phillips C Geurts P Hustinx R Bernard C Characterization of a temporoparietal junction subtype of Alzheimer’s disease Hum Brain Mapp 2019 40 14 4279 4286 10.1002/hbm.24701 31243829 PMC6865402 · DOI
  12. [14]Cerami C Della Rosa PA Magnani G Santangelo R Marcone A Cappa SF Brain metabolic maps in mild cognitive impairment predict heterogeneity of progression to dementia NeuroImage Clin 2015 7 187 194 10.1016/j.nicl.2014.12.004 25610780 PMC4300010 · DOI · Paper page
  13. [15]Perani D Cerami C Caminiti SP Santangelo R Coppi E Ferrari L 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 3 499 508 10.1007/s00259-015-3170-y 26341365 · DOI
  14. [16]Nestor PJ Altomare D Festari C Drzezga A Rivolta J Walker Z Clinical utility of FDG-PET for the differential diagnosis among the main forms of dementia Eur J Nucl Med Mol Imaging 2018 45 9 1509 1525 10.1007/s00259-018-4035-y 29736698 · DOI
  15. [17]Teipel S Grothe MJ Alzheimer s Disease Neuroimaging I Does posterior cingulate hypometabolism result from disconnection or local pathology across preclinical and clinical stages of Alzheimer’s disease? Eur J Nucl Med Mol Imaging 2016 43 3 526 536 10.1007/s00259-015-3222-3 26555082 PMC6166099 · DOI
  16. [18]Crane PK Carle A Gibbons LE Insel P Mackin RS Gross A Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Brain Imaging Behav 2012 6 4 502 516 10.1007/s11682-012-9186-z 22782295 PMC3806057 · DOI
  17. [19]Gibbons LE Carle AC Mackin RS Harvey D Mukherjee S Insel P A composite score for executive functioning, validated in Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment Brain Imaging Behav. 2012 6 4 517 527 10.1007/s11682-012-9176-1 22644789 PMC3684181 · DOI
  18. [20]Choi SE Mukherjee S Gibbons LE Sanders RE Jones RN Tommet D Development and validation of language and visuospatial composite scores in ADNI Alzheimers Dement (N Y) 2020 6 1 e12072 33313380 PMC7718716
  19. [21]Landau SM Thomas BA Thurfjell L Schmidt M Margolin R Mintun M Amyloid PET imaging in Alzheimer’s disease: a comparison of three radiotracers Eur J Nucl Med Mol Imaging 2014 41 7 1398 1407 10.1007/s00259-014-2753-3 24647577 PMC4055504 · DOI
  20. [23]Salvado G Molinuevo JL Brugulat-Serrat A Falcon C Grau-Rivera O Suarez-Calvet M Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers Alzheimers Res Ther 2019 11 1 27 10.1186/s13195-019-0478-z 30902090 PMC6429814 · DOI · Paper page
  21. [24]Saykin AJ Shen L Foroud TM Potkin SG Swaminathan S Kim S Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: genetics core aims, progress, and plans Alzheimers Dement 2010 6 3 265 273 10.1016/j.jalz.2010.03.013 20451875 PMC2868595 · DOI · Paper page
  22. [25]Grothe MJ Villeneuve S Dyrba M Bartres-Faz D Wirth M Alzheimer’s Disease Neuroimaging I Multimodal characterization of older APOE2 carriers reveals selective reduction of amyloid load Neurology. 2017 88 6 569 576 10.1212/WNL.0000000000003585 28062720 PMC5304459 · DOI
  23. [26]Wolf D Bocchetta M Preboske GM Boccardi M Grothe MJ Alzheimer’s Disease Neuroimaging I Reference standard space hippocampus labels according to the European Alzheimer’s Disease Consortium-Alzheimer’s Disease Neuroimaging Initiative harmonized protocol: utility in automated volumetry Alzheimers Dement 2017 13 8 893 902 10.1016/j.jalz.2017.01.009 28238738 · DOI
  24. [29]Schwarz C Fletcher E DeCarli C Carmichael O Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR Inf Process Med Imaging 2009 21 239 251 19694267 PMC2864489
  25. [30]DeCarli C Fletcher E Ramey V Harvey D Jagust WJ Anatomical mapping of white matter hyperintensities (WMH): exploring the relationships between periventricular WMH, deep WMH, and total WMH burden Stroke. 2005 36 1 50 55 10.1161/01.STR.0000150668.58689.f2 15576652 PMC3816357 · DOI
  26. [31]Lange C Suppa P Frings L Brenner W Spies L Buchert R Optimization of statistical single subject analysis of brain FDG PET for the prognosis of mild cognitive impairment-to-Alzheimer’s disease conversion J Alzheimers Dis 2016 49 4 945 959 10.3233/JAD-150814 26577523 · DOI
  27. [32]Matias-Guiu JA Diaz-Alvarez J Ayala JL Risco-Martin JL Moreno-Ramos T Pytel V Clustering analysis of FDG-PET imaging in primary progressive aphasia Front Aging Neurosci 2018 10 230 10.3389/fnagi.2018.00230 30108500 PMC6079194 · DOI
  28. [33]Noh Y Jeon S Lee JM Seo SW Kim GH Cho H Anatomical heterogeneity of Alzheimer disease: based on cortical thickness on MRIs Neurology. 2014 83 21 1936 1944 10.1212/WNL.0000000000001003 25344382 PMC4248459 · DOI
  29. [34]Cerami C Dodich A Lettieri G Iannaccone S Magnani G Marcone A Different FDG-PET metabolic patterns at single-subject level in the behavioral variant of fronto-temporal dementia Cortex 2016 83 101 112 10.1016/j.cortex.2016.07.008 27498041 · DOI
  30. [35]Davies DL Bouldin DW A cluster separation measure IEEE Trans Pattern Anal Mach Intell 1979 1 2 224 227 10.1109/TPAMI.1979.4766909 21868852 · DOI · Paper page
  31. [36]Rousseeuw PJ Silhouettes: a graphical aid to the interpretation and validation of cluster analysis J Comput Appl Math 1987 20 53 65 10.1016/0377-0427(87)90125-7 · DOI · Paper page
  32. [37]Dukart J Mueller K Horstmann A Vogt B Frisch S Barthel H Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies NeuroImage. 2010 49 2 1490 1495 10.1016/j.neuroimage.2009.09.017 19770055 · DOI · Paper page
  33. [38]Yakushev I Landvogt C Buchholz HG Fellgiebel A Hammers A Scheurich A Choice of reference area in studies of Alzheimer’s disease using positron emission tomography with fluorodeoxyglucose-F18 Psychiatry Res 2008 164 2 143 153 10.1016/j.pscychresns.2007.11.004 18930634 · DOI
  34. [39]Benjamini Y Yekutieli D The control of the false discovery rate in multiple testing under dependency Ann Stat 2001 29 4 1165 1188 10.1214/aos/1013699998 · DOI · Paper page
  35. [40]Teipel SJ Dyrba M Chiesa PA Sakr F Jelistratova I Lista S In vivo staging of regional amyloid deposition predicts functional conversion in the preclinical and prodromal phases of Alzheimer’s disease Neurobiol Aging 2020 93 98 108 10.1016/j.neurobiolaging.2020.03.011 32291113 · DOI
  36. [41]Ray NJ Bradburn S Murgatroyd C Toseeb U Mir P Kountouriotis GK In vivo cholinergic basal forebrain atrophy predicts cognitive decline in de novo Parkinson’s disease Brain. 2018 141 1 165 176 10.1093/brain/awx310 29228203 PMC5837422 · DOI
  37. [42]Mosconi L Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease. FDG-PET studies in MCI and AD Eur J Nucl Med Mol Imaging 2005 32 4 486 510 10.1007/s00259-005-1762-7 15747152 · DOI
  38. [43]Poulakis K Pereira JB Mecocci P Vellas B Tsolaki M Kloszewska I Heterogeneous patterns of brain atrophy in Alzheimer’s disease Neurobiol Aging 2018 65 98 108 10.1016/j.neurobiolaging.2018.01.009 29455029 · DOI
  39. [44]Hwang J Kim CM Jeon S Lee JM Hong YJ Roh JH Prediction of Alzheimer’s disease pathophysiology based on cortical thickness patterns Alzheimers Dement (Amst) 2016 2 58 67 10.1016/j.dadm.2015.11.008 27239533 PMC4879518 · DOI
  40. [45]Klupp E Grimmer T Tahmasian M Sorg C Yakushev I Yousefi BH Prefrontal hypometabolism in Alzheimer disease is related to longitudinal amyloid accumulation in remote brain regions J Nucl Med 2015 56 3 399 404 10.2967/jnumed.114.149302 25678488 · DOI
  41. [46]La Joie R Landeau B Perrotin A Bejanin A Egret S Pélerin A Intrinsic connectivity identifies the hippocampus as a main crossroad between Alzheimer’s and semantic dementia-targeted networks Neuron. 2014 81 6 1417 1428 10.1016/j.neuron.2014.01.026 24656258 · DOI
  42. [47]Villain N Fouquet M Baron JC Mezenge F Landeau B de La Sayette V Sequential relationships between grey matter and white matter atrophy and brain metabolic abnormalities in early Alzheimer’s disease Brain. 2010 133 11 3301 3314 10.1093/brain/awq203 20688814 PMC3291528 · DOI · Paper page
  43. [48]Botha H Mantyh WG Murray ME Knopman DS Przybelski SA Wiste HJ FDG-PET in tau-negative amnestic dementia resembles that of autopsy-proven hippocampal sclerosis Brain 2018 141 4 1201 1217 10.1093/brain/awy049 29538658 PMC5889045 · DOI
  44. [49]Buciuc M Botha H Murray ME Schwarz CG Senjem ML Jones DT Utility of FDG-PET in diagnosis of Alzheimer-related TDP-43 proteinopathy Neurology. 2020 95 1 e23 e34 10.1212/WNL.0000000000009722 32518145 PMC7371379 · DOI
  45. [50]Ferreira D Shams S Cavallin L Viitanen M Martola J Granberg T The contribution of small vessel disease to subtypes of Alzheimer’s disease: a study on cerebrospinal fluid and imaging biomarkers Neurobiol Aging 2018 70 18 29 10.1016/j.neurobiolaging.2018.05.028 29935417 · DOI
  46. [51]Ossenkoppele R Pijnenburg YA Perry DC Cohn-Sheehy BI Scheltens NM Vogel JW The behavioural/dysexecutive variant of Alzheimer’s disease: clinical, neuroimaging and pathological features Brain. 2015 138 Pt 9 2732 2749 10.1093/brain/awv191 26141491 PMC4623840 · DOI · Paper page
  47. [52]Dong A Toledo JB Honnorat N Doshi J Varol E Sotiras A Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: links to cognition, progression and biomarkers Brain. 2017 140 3 735 747 28003242 PMC5837514
  48. [53]Morbelli S Bauckneht M Arnaldi D Picco A Pardini M Brugnolo A 18F-FDG PET diagnostic and prognostic patterns do not overlap in Alzheimer’s disease (AD) patients at the mild cognitive impairment (MCI) stage Eur J Nucl Med Mol Imaging 2017 44 12 2073 2083 10.1007/s00259-017-3790-5 28785843 · DOI
  49. [54]Young AL Marinescu RV Oxtoby NP Bocchetta M Yong K Firth NC Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with subtype and stage inference Nat Commun 2018 9 1 4273 10.1038/s41467-018-05892-0 30323170 PMC6189176 · DOI · Paper page

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Authors
Levin, F., Ferreira, D., Lange, C., Dyrba, M., Westman, E., Buchert, R., Teipel, S. J., Grothe, M. J., & Initiative, F. T. A. D. N.
Affiliations
  • German Center for Neurodegenerative Diseases (DE)
  • Karolinska Institutet (SE)
  • Humboldt-Universität zu Berlin (DE)
  • King's College London (GB)
  • University Medical Center Hamburg-Eppendorf (DE)
  • Consejo Superior de Investigaciones Científicas (ES)
Corresponding author
Grothe, Michel J.
ORCID
Venue
Alzheimer's Research & Therapy
Publisher
Springer Science and Business Media LLC
Published
2021-02-19
Volume
13
Issue
1
Pages
49-49
ISSN
1758-9193 (ISSN-L)
DOI
10.1186/s13195-021-00785-9
PMC
PMC7896407
OpenAlex
W3129955977
Cited by
109
References
5
Citations by year
2026: 13 · 2025: 26 · 2024: 26 · 2023: 22 · 2022: 13 · 2021: 9
FWCI
12.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Alzheimer s Research & Therapy (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RISalud Institutional Health Repository of Andalucía (submittedVersion, cc-by)
  • Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DZNE Pub (submittedVersion, cc-by)
Funders
  • Instituto de Salud Carlos III (CP19/00031)
  • Foundation for the National Institutes of Health (U01 AG024904)
  • U.S. Department of Defense (W81XWH-12-2-0012)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Keywords
Alzheimer's disease, FDG-PET hypometabolism, mild cognitive impairment, prodromal Alzheimer's disease, cognitive decline trajectories, executive dysfunction, molecular imaging biomarkers
MeSH terms
Alzheimer Disease, Brain, Humans, Magnetic Resonance Imaging, Fluorodeoxyglucose F18, Positron-Emission Tomography, Cognitive Dysfunction

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