The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer’s disease using Alzheimer’s Disease Neuroimaging Initiative database
Autoři:
Ilker Ozsahin aff001; Boran Sekeroglu aff003; Greta S. P. Mok aff001
Působiště autorů:
Biomedical Imaging Laboratory, Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China
aff001; Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia, Turkey
aff002; Department of Information Systems Engineering, Near East University, Nicosia, Turkey
aff003; Faculty of Health Sciences, University of Macau, Macau SAR, China
aff004
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0226577
Souhrn
Amyloid beta (Aβ) plaques aggregation is considered as the “start” of the degenerative process that manifests years before the clinical symptoms appear in Alzheimer’s Disease (AD). The aim of this study is to use back propagation neural networks (BPNNs) in 18F-florbetapir PET data for automated classification of four patient groups including AD, late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI), and significant memory concern (SMC), versus normal control (NC) for early AD detection. Five hundred images for AD, LMCI, EMCI, SMC, and NC, i.e., 100 images for each group, were used from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The results showed that the automated classification of NC/AD produced a high accuracy of 87.9%, while the results for the prodromal stages of the disease were 66.4%, 60.0%, and 52.9% for NC/LCMI, NC/EMCI and NC/SMC, respectively. The proposed method together with the image preparation steps can be used for early AD detection and classification with high accuracy using Aβ PET dataset.
Klíčová slova:
Alzheimer's disease – Biomarkers – Neuroimaging – Magnetic resonance imaging – Neurons – Neural networks – Positron emission tomography – Artificial neural networks
Zdroje
1. Billones CD, Demetria OJLD, Hostallero DED, Naval PC. DemNet: A convolutional neural network for the detection of Alzheimer’s Disease and Mild Cognitive Impairment. 2016: 2016 IEEE Region 10 Conference (TENCON), Singapore, 3724–3727.
2. Luo S, Li X, Li J. Automatic Alzheimer’s Disease Recognition from MRI Data Using Deep Learning Method. JAMP 2017; 05: 1892–1898.
3. Brookmeyer, Johnson E, Ziegler-Graham K, Arrighi H. Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement. 2007; 3(3): 186–191. doi: 10.1016/j.jalz.2007.04.381 19595937
4. Busquets M, Sabaté R, Estelrich J. Potential applications of magnetic particles to detect and treat Alzheimer’s disease. Nanoscale Res. Lett. 2014; 9: 538. doi: 10.1186/1556-276X-9-538 25288921
5. Cohen A, Klunk W. Early detection of Alzheimer’s disease using PiB and FDG PET. Neurobiol Dis. 2014; 72: 117–122. doi: 10.1016/j.nbd.2014.05.001 24825318
6. Hardy J, Duff K, Hardy K, Perez-Tur J, Hutton M. Genetic dissection of Alzheimer’s disease and related dementias: amyloid and its relationship to tau. Nat Neurosci. 1998; 1: 355–358. doi: 10.1038/1565 10196523
7. Delacourte A et al. The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer’s disease. Neurology. 1999; 52: 1158–1165. doi: 10.1212/wnl.52.6.1158 10214737
8. Singh S et al. Deep-learning-based classification of FDG-PET data for Alzheimer’s disease categories. Proc SPIE Int Soc Opt Eng. 2017.
9. Rathore S, Habes M, Iftikhar M, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. NeuroImage. 2017; 155: 530–548. doi: 10.1016/j.neuroimage.2017.03.057 28414186
10. Toussaint P et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer’s disease using conjoint univariate and independent component analyses. NeuroImage. 2012; 63: 936–946. doi: 10.1016/j.neuroimage.2012.03.091 22510256
11. Passamonti L et al. 18F-AV-1451 positron emission tomography in Alzheimer’s disease and progressive supranuclear palsy. Brain. 2017; 140(3): 781–791. doi: 10.1093/brain/aww340 28122879
12. Zhang S et al. 11C-PIB-PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2014; 7: CD010386.
13. Choi S et al. Preclinical properties of 18F-AV-45: A PET agent for a plaques in the brain. J. Nucl. Med. 2009; 50: 1887–1894. doi: 10.2967/jnumed.109.065284 19837759
14. Sarraf S, Tofighi G. Classification of Alzheimer’s Disease Structural MRI Data by Deep Learning Convolutional Neural Networks. [arXiv preprint, 2016] https://arxiv.org/abs/1607.06583.
15. Wachinger C, Reuter M. Domain adaptation for Alzheimer’s disease diagnostics. NeuroImage. 2016; 139: 470–479. doi: 10.1016/j.neuroimage.2016.05.053 27262241
16. Casanova R et al. Alzheimer’s Disease risk assessment using large-scale machine learning methods. PLOS ONE. 2013; 8: e77949. doi: 10.1371/journal.pone.0077949 24250789
17. Khvostikov A, Aderghal K, Benois-Pineau J, Krylov A, Catheline G. 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. [arXiv preprint, 2018] https://arxiv.org/abs/1801.05968.
18. Risacher S et al. APOE effect on Alzheimer’s disease biomarkers in older adults with significant memory concern. Alzheimers Dement. 11(12), 1417–1429 (2015). doi: 10.1016/j.jalz.2015.03.003 25960448
19. Ithapu V et al. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Alzheimers Dement. 2015; 11(12): 1489–1499. doi: 10.1016/j.jalz.2015.01.010 26093156
20. Sperling R et al. Toward defining the preclinical stages of 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: 280–292. doi: 10.1016/j.jalz.2011.03.003 21514248
21. Pereira F, Mitchell T. Botvinick M. Machine learning classifiers and fMRI: A tutorial overview. NeuroImage. 2009; 45: 199–209.
22. Bianchini M, Scarselli F. On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures. IEEE Trans Neural Netw Learn Syst. 2014; 25: 1553–1565. doi: 10.1109/TNNLS.2013.2293637 25050951
23. Mhaskar H, Liao Q, Poggio T. Learning Functions: When Is Deep Better Than Shallow [arXiv preprint, 2016]. https://arxiv.org/abs/1603.00988.
24. Kůrková V, Sanguineti M. Probabilistic lower bounds for approximation by shallow perceptron networks. Neural Netw. 2017; 91: 34–41. doi: 10.1016/j.neunet.2017.04.003 28482227
25. Jack C et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010; 9: 119–128. doi: 10.1016/S1474-4422(09)70299-6 20083042
26. Liu M, Zhang D, Shen D. Hierarchical fusion of features and classifier decisions for Alzheimer’s disease diagnosis. Hum Brain Mapp. 2013; 35: 1305–1319. doi: 10.1002/hbm.22254 23417832
27. Suk H, Lee S, Shen D. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage. 2014; 101: 569–582. doi: 10.1016/j.neuroimage.2014.06.077 25042445
28. Lei B, Chen S, Ni D, Wang T. Discriminative Learning for Alzheimer’s Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion. Front Aging Neurosci. 2016; 8: 77. doi: 10.3389/fnagi.2016.00077 27242506
29. Nozadi S, Kadoury S. Classification of Alzheimer’s and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET. Int J Biomed Imaging. 2018; 1247430: 1–13.
30. Xu L, Wu X, Chen K, Yao L. Multi-modality sparse representation-based classification for Alzheimer’s disease and mild cognitive impairment. Comput Methods Programs Biomed. 2015; 122(2): 182–90. doi: 10.1016/j.cmpb.2015.08.004 26298855
31. Li Q, Wu X, Xu L, Chen K, Yao L; Alzheimer’s Disease Neuroimaging Initiative. Classification of Alzheimer’s Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning. Front Comput Neurosci. 2018; 11: 117. doi: 10.3389/fncom.2017.00117 29375356
32. Jansen W et al. Prevalence of Cerebral Amyloid Pathology in Persons Without Dementia. JAMA. 2015; 313: 1924. doi: 10.1001/jama.2015.4668 25988462
33. Fantoni E, Chalkidou A, O’ Brien J, Farrar G, Hammers A. A Systematic Review and Aggregated Analysis on the Impact of Amyloid PET Brain Imaging on the Diagnosis, Diagnostic Confidence, and Management of Patients being Evaluated for Alzheimer’s Disease. J Alzheimers Dis. 2018; 63: 783–796. doi: 10.3233/JAD-171093 29689725
34. Jack C et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018; 14: 535–562. doi: 10.1016/j.jalz.2018.02.018 29653606
Článok vyšiel v časopise
PLOS One
2019 Číslo 12
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Masturbační chování žen v ČR − dotazníková studie
- Nejasný stín na plicích – kazuistika
- Těžké menstruační krvácení může značit poruchu krevní srážlivosti. Jaký management vyšetření a léčby je v takovém případě vhodný?
- Somatizace stresu – typické projevy a možnosti řešení
Najčítanejšie v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts