Study of the Influence of Age in F-FDG PET Images Using a Data-Driven Approach and Its Evaluation in Alzheimer's Disease

Overview

Journal Contrast Media Mol Imaging

Specialty Radiology

Date 2018 Mar 28

PMID 29581708

Citations 13

Authors

Jiehui Jiang

Yiwu Sun

Hucheng Zhou

Shaoping Li

Zhemin Huang

Ping Wu

Kuangyu Shi

Chuantao Zuo

Alzheimers Disease Neuroimaging Initiative

Affiliations

Soon will be listed here.

Abstract

Objectives: F-FDG PET scan is one of the most frequently used neural imaging scans. However, the influence of age has proven to be the greatest interfering factor for many clinical dementia diagnoses when analyzing F-FDG PET images, since radiologists encounter difficulties when deciding whether the abnormalities in specific regions correlate with normal aging, disease, or both. In the present paper, the authors aimed to define specific brain regions and determine an age-correction mathematical model.

Methods: A data-driven approach was used based on 255 healthy subjects.

Results: The inferior frontal gyrus, the left medial part and the left medial orbital part of superior frontal gyrus, the right insula, the left anterior cingulate, the left median cingulate, and paracingulate gyri, and bilateral superior temporal gyri were found to have a strong negative correlation with age. For evaluation, an age-correction model was applied to 262 healthy subjects and 50 AD subjects selected from the ADNI database, and partial correlations between SUVR mean and three clinical results were carried out before and after age correction.

Conclusion: All correlation coefficients were significantly improved after the age correction. The proposed model was effective in the age correction of both healthy and AD subjects.

Citing Articles

Exploring Brain Imaging and Genetic Risk Factors in Different Progression States of Alzheimer's Disease Through OSnetNMF-Based Methods.

Gao M, Kong W, Liu K, Wen G, Yu Y, Zhu Y J Mol Neurosci. 2025; 75(1):7.

PMID: 39815147 DOI: 10.1007/s12031-024-02274-8.

Evaluating the effect of rapamycin treatment in Alzheimer's disease and aging using in vivo imaging: the ERAP phase IIa clinical study protocol.

Svensson J, Bolin M, Thor D, Williams P, Brautaset R, Carlsson M BMC Neurol. 2024; 24(1):111.

PMID: 38575854 PMC: 10993488. DOI: 10.1186/s12883-024-03596-1.

Distance-weighted Sinkhorn loss for Alzheimer's disease classification.

Wang Z, Zhan Q, Tong B, Yang S, Hou B, Huang H iScience. 2024; 27(3):109212.

PMID: 38433927 PMC: 10906516. DOI: 10.1016/j.isci.2024.109212.

The remodeling of metabolic brain pattern in patients with extracranial diffuse large B-cell lymphoma.

Liu J, Tang M, Zhu D, Ruan G, Zou S, Cheng Z EJNMMI Res. 2023; 13(1):94.

PMID: 37902852 PMC: 10616001. DOI: 10.1186/s13550-023-01046-6.

Multi-Group Tensor Canonical Correlation Analysis.

Zhou Z, Tong B, Tarzanagh D, Hou B, Saykin A, Long Q ACM BCB. 2023; 2023.

PMID: 37876849 PMC: 10593155. DOI: 10.1145/3584371.3612962.

References

Iseki E, Murayama N, Yamamoto R, Fujishiro H, Suzuki M, Kawano M . Construction of a (18)F-FDG PET normative database of Japanese healthy elderly subjects and its application to demented and mild cognitive impairment patients. Int J Geriatr Psychiatry. 2009; 25(4):352-61. DOI: 10.1002/gps.2346. View

Kalbe E, Calabrese P, Fengler S, Kessler J . DemTect, PANDA, EASY, and MUSIC: cognitive screening tools with age correction and weighting of subtests according to their sensitivity and specificity. J Alzheimers Dis. 2013; 34(4):813-34. DOI: 10.3233/JAD-122128. View

Bittner D, Gron G, Schirrmeister H, Reske S, Riepe M . [18F]FDG-PET in patients with Alzheimer's disease: marker of disease spread. Dement Geriatr Cogn Disord. 2004; 19(1):24-30. DOI: 10.1159/000080967. View

Herholz K, Salmon E, Perani D, Baron J, Holthoff V, Frolich L . Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage. 2002; 17(1):302-16. DOI: 10.1006/nimg.2002.1208. View

Smith S, Jenkinson M, Woolrich M, Beckmann C, Behrens T, Johansen-Berg H . Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004; 23 Suppl 1:S208-19. DOI: 10.1016/j.neuroimage.2004.07.051. View

Gardener S, Sohrabi H, Shen K, Rainey-Smith S, Weinborn M, Bates K . Cerebral Glucose Metabolism is Associated with Verbal but not Visual Memory Performance in Community-Dwelling Older Adults. J Alzheimers Dis. 2016; 52(2):661-72. DOI: 10.3233/JAD-151084. View

Giorgio A, Santelli L, Tomassini V, Bosnell R, Smith S, De Stefano N . Age-related changes in grey and white matter structure throughout adulthood. Neuroimage. 2010; 51(3):943-51. PMC: 2896477. DOI: 10.1016/j.neuroimage.2010.03.004. View

Silverman D, Truong C, Kim S, Chang C, Chen W, Kowell A . Prognostic value of regional cerebral metabolism in patients undergoing dementia evaluation: comparison to a quantifying parameter of subsequent cognitive performance and to prognostic assessment without PET. Mol Genet Metab. 2003; 80(3):350-5. DOI: 10.1016/S1096-7192(03)00139-2. View

Rodriguez-Vieitez E, Leuzy A, Chiotis K, Saint-Aubert L, Wall A, Nordberg A . Comparability of [F]THK5317 and [C]PIB blood flow proxy images with [F]FDG positron emission tomography in Alzheimer's disease. J Cereb Blood Flow Metab. 2016; 37(2):740-749. PMC: 5381463. DOI: 10.1177/0271678X16645593. View

10.

Petersen R . Aging, mild cognitive impairment, and Alzheimer's disease. Neurol Clin. 2000; 18(4):789-806. DOI: 10.1016/s0733-8619(05)70226-7. View

11.

Douaud G, Groves A, Tamnes C, Westlye L, Duff E, Engvig A . A common brain network links development, aging, and vulnerability to disease. Proc Natl Acad Sci U S A. 2014; 111(49):17648-53. PMC: 4267352. DOI: 10.1073/pnas.1410378111. View

12.

Shen X, Liu H, Hu Z, Hu H, Shi P . The relationship between cerebral glucose metabolism and age: report of a large brain PET data set. PLoS One. 2013; 7(12):e51517. PMC: 3527454. DOI: 10.1371/journal.pone.0051517. View

13.

Oh H, Madison C, Villeneuve S, Markley C, Jagust W . Association of gray matter atrophy with age, β-amyloid, and cognition in aging. Cereb Cortex. 2013; 24(6):1609-18. PMC: 4014182. DOI: 10.1093/cercor/bht017. View

14.

Li H, Becker N, Raman S, Chan T, Bissonnette J . The value of nodal information in predicting lung cancer relapse using 4DPET/4DCT. Med Phys. 2015; 42(8):4727-33. DOI: 10.1118/1.4926755. View

15.

Lustig C, Snyder A, Bhakta M, OBrien K, McAvoy M, Raichle M . Functional deactivations: change with age and dementia of the Alzheimer type. Proc Natl Acad Sci U S A. 2003; 100(24):14504-9. PMC: 283621. DOI: 10.1073/pnas.2235925100. View

16.

Petrella J, Coleman R, Doraiswamy P . Neuroimaging and early diagnosis of Alzheimer disease: a look to the future. Radiology. 2003; 226(2):315-36. DOI: 10.1148/radiol.2262011600. View

17.

Jack Jr C, Wiste H, Weigand S, Knopman D, Vemuri P, Mielke M . Age, Sex, and APOE ε4 Effects on Memory, Brain Structure, and β-Amyloid Across the Adult Life Span. JAMA Neurol. 2015; 72(5):511-9. PMC: 4428984. DOI: 10.1001/jamaneurol.2014.4821. View

18.

Nasreddine Z, Phillips N, Bedirian V, Charbonneau S, Whitehead V, Collin I . The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005; 53(4):695-9. DOI: 10.1111/j.1532-5415.2005.53221.x. View

19.

Dukart J, Kherif F, Mueller K, Adaszewski S, Schroeter M, Frackowiak R . Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease. PLoS Comput Biol. 2013; 9(4):e1002987. PMC: 3616972. DOI: 10.1371/journal.pcbi.1002987. View

20.

Groves A, Smith S, Fjell A, Tamnes C, Walhovd K, Douaud G . Benefits of multi-modal fusion analysis on a large-scale dataset: life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure. Neuroimage. 2012; 63(1):365-80. DOI: 10.1016/j.neuroimage.2012.06.038. View