4.1 Article

Study of the Influence of Age in 18F-FDG PET Images Using a Data-Driven Approach and Its Evaluation in Alzheimer's Disease

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WILEY-HINDAWI
DOI: 10.1155/2018/3786083

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资金

  1. National Science Foundation of China [61603236, 81671239, 81361120393, 81401135]
  2. Shanghai Technology and Science Key Project in Healthcare [17441902100]
  3. Development Fund for Shanghai Talents [201448]
  4. Shanghai Key Laboratory of Psychotic Disorders [13dz2260500]
  5. Sino-German Institute for Brain Molecular Imaging and Clinical Translation
  6. Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
  7. DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
  8. National Institute of Aging
  9. National Institute of Biomedical Imaging and Bioengineering
  10. AbbVie
  11. Alzheimer's Association
  12. Alzheimer's Drug Discovery Foundation
  13. Araclon Biotech
  14. BioClinica, Inc.
  15. Biogen
  16. Bristol-Myers Squibb Company
  17. CereSpir, Inc.
  18. Eisai Inc.
  19. Elan Pharmaceuticals, Inc.
  20. Eli Lilly and Company
  21. EuroImmun
  22. F. Hoffmann-La Roche Ltd.
  23. Genentech, Inc.
  24. Fujirebio
  25. GE Healthcare
  26. IXICO Ltd.
  27. Janssen Alzheimer Immunotherapy Research & Development, LLC.
  28. Johnson & Johnson Pharmaceutical Research & Development LLC.
  29. Lumosity
  30. Lundbeck
  31. Merck Co., Inc.
  32. Meso Scale Diagnostics, LLC.
  33. NeuroRx Research
  34. Neurotrack Technologies
  35. Novartis Pharmaceuticals Corporation
  36. Pfizer Inc.
  37. Piramal Imaging
  38. Servier
  39. Takeda Pharmaceutical Company
  40. Transition Therapeutics
  41. Canadian Institutes of Health Research

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Objectives. F-18-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-18-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.

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