Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images


Autoři: Yubraj Gupta aff001;  Kun Ho Lee aff002;  Kyu Yeong Choi aff002;  Jang Jae Lee aff002;  Byeong Chae Kim aff002;  Goo Rak Kwon aff00100
Působiště autorů: School of Information Communication Engineering, Chosun University, Gwangju, Republic of Korea aff001;  National Research Center for Dementia, Chosun University, Gwangju, Republic of Korea aff002;  Department of Biomedical Science, College of Natural Sciences, Chosun University, Gwangju, Republic of Korea aff003;  Department of Neurology, Chonnam National University Medical School, Gwangju, Republic of Korea aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222446

Souhrn

In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer’s disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods.

Klíčová slova:

Alzheimer's disease – Biomarkers – Cognitive impairment – Hippocampus – Imaging techniques – Magnetic resonance imaging – Neuroimaging – Voxel-based morphometry


Zdroje

1. Braak H, Braak E, Bohl J, Bratzke H. Evolution of Alzheimer’s disease related cortical lesions. In: Gertz H-J, Th Arendt, editors. Alzheimer’s Disease—From Basic Research to Clinical Applications. Vienna: Springer Vienna; 1998. pp. 97–106. doi: 10.1007/978-3-7091-7508-8_9

2. Bain LJ, Jedrziewski K, Morrison-Bogorad M, Albert M, Cotman C, Hendrie H, et al. Healthy brain aging: A meeting report from the Sylvan M. Cohen Annual Retreat of the University of Pennsylvania Institute on Aging. Alzheimer’s & Dementia. 2008;4: 443–446. doi: 10.1016/j.jalz.2008.08.006 18945646

3. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM. Forecasting the global burden of Alzheimer’s disease. Alzheimer’s & Dementia. 2007;3: 186–191. doi: 10.1016/j.jalz.2007.04.381 19595937

4. Wattmo C, Londos E, Minthon L. Risk Factors That Affect Life Expectancy in Alzheimer’s Disease: A 15-Year Follow-Up. Dementia and Geriatric Cognitive Disorders. 2014;38: 286–299. doi: 10.1159/000362926 24992891

5. Dubois B, Feldman HH, Jacova C, DeKosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS–ADRDA criteria. The Lancet Neurology. 2007;6: 734–746. doi: 10.1016/S1474-4422(07)70178-3 17616482

6. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia. 2011;7: 270–279. doi: 10.1016/j.jalz.2011.03.008 21514249

7. Petersen RC, Caracciolo B, Brayne C, Gauthier S, Jelic V, Fratiglioni L. Mild cognitive impairment: a concept in evolution. Journal of Internal Medicine. 2014;275: 214–228. doi: 10.1111/joim.12190 24605806

8. Mitchell AJ, Shiri-Feshki M. Rate of progression of mild cognitive impairment to dementia—meta-analysis of 41 robust inception cohort studies. Acta Psychiatrica Scandinavica. 2009;119: 252–265. doi: 10.1111/j.1600-0447.2008.01326.x 19236314

9. Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, et al. Mild Cognitive Impairment: Ten Years Later. Archives of Neurology. 2009;66. doi: 10.1001/archneurol.2009.266 20008648

10. Artero S, Petersen R, Touchon J, Ritchie K. Revised Criteria for Mild Cognitive Impairment: Validation within a Longitudinal Population Study. Dementia and Geriatric Cognitive Disorders. 2006;22: 465–470. doi: 10.1159/000096287 17047325

11. Petersen RC. Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine. 2004;256: 183–194. doi: 10.1111/j.1365-2796.2004.01388.x 15324362

12. Wee C-Y, Yap P-T, Shen D, for the Alzheimer’s Disease Neuroimaging Initiative. Prediction of Alzheimer’s disease and mild cognitive impairment using cortical morphological patterns: Prediction of AD and MCI using Cortical Morphological Patterns. Human Brain Mapping. 2013;34: 3411–3425. doi: 10.1002/hbm.22156 22927119

13. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert M-O, et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. NeuroImage. 2011;56: 766–781. doi: 10.1016/j.neuroimage.2010.06.013 20542124

14. Querbes O, Aubry F, Pariente J, Lotterie J-A, Démonet J-F, Duret V, et al. Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain. 2009;132: 2036–2047. doi: 10.1093/brain/awp105 19439419

15. Beheshti I, Demirel H. Feature-ranking-based Alzheimer’s disease classification from structural MRI. Magnetic Resonance Imaging. 2016;34: 252–263. doi: 10.1016/j.mri.2015.11.009 26657976

16. Beheshti I, Demirel H. Probability distribution function-based classification of structural MRI for the detection of Alzheimer’s disease. Computers in Biology and Medicine. 2015;64: 208–216. doi: 10.1016/j.compbiomed.2015.07.006 26226415

17. Voevodskaya O, Simmons A, Nordenskjold R, Kullberg J, Ahlstrom H, Lind L, et al. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease. Frontiers in Aging Neuroscience. 2014;6. doi: 10.3389/fnagi.2014.00006

18. Nozadi SH, Kadoury S, The Alzheimer’s Disease Neuroimaging Initiative. Classification of Alzheimer’s and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET. International Journal of Biomedical Imaging. 2018;2018: 1–13. doi: 10.1155/2018/1247430 29736165

19. Li Y, Rinne JO, Mosconi L, Pirraglia E, Rusinek H, DeSanti S, et al. Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer’s disease. European Journal of Nuclear Medicine and Molecular Imaging. 2008;35: 2169–2181. doi: 10.1007/s00259-008-0833-y 18566819

20. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Annals of Neurology. 2009;65: 403–413. doi: 10.1002/ana.21610 19296504

21. Herukka S-K, Simonsen AH, Andreasen N, Baldeiras I, Bjerke M, Blennow K, et al. Recommendations for cerebrospinal fluid Alzheimer’s disease biomarkers in the diagnostic evaluation of mild cognitive impairment. Alzheimer’s & Dementia. 2017;13: 285–295. doi: 10.1016/j.jalz.2016.09.009 28341066

22. Zhang D, Shen D, Alzheimer’s Disease Neuroimaging Initiative. Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers. Chen K, editor. PLoS ONE. 2012;7: e33182. doi: 10.1371/journal.pone.0033182 22457741

23. Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage. 2011;55: 856–867. doi: 10.1016/j.neuroimage.2011.01.008 21236349

24. Lu D, Popuri K, Ding GW, Balachandar R, Beg MF. Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images. Scientific Reports. 2018;8. doi: 10.1038/s41598-017-18329-3

25. Forlenza OV, Radanovic M, Talib LL, Aprahamian I, Diniz BS, Zetterberg H, et al. Cerebrospinal fluid biomarkers in Alzheimer’s disease: Diagnostic accuracy and prediction of dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 2015;1: 455–463. doi: 10.1016/j.dadm.2015.09.003 27239524

26. Young J, Modat M, Cardoso MJ, Mendelson A, Cash D, Ourselin S. Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment. NeuroImage: Clinical. 2013;2: 735–745. doi: 10.1016/j.nicl.2013.05.004 24179825

27. Westman E, Muehlboeck J-S, Simmons A. Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. NeuroImage. 2012;62: 229–238. doi: 10.1016/j.neuroimage.2012.04.056 22580170

28. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, et al. 2014 Update of the Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer’s & Dementia. 2015;11: e1–e120. doi: 10.1016/j.jalz.2014.11.001 26073027

29. Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D, et al. Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer’s Disease. Oreja-Guevara C, editor. PLoS ONE. 2011;6: e25446. doi: 10.1371/journal.pone.0025446 22022397

30. Sørensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, et al. Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NeuroImage: Clinical. 2017;13: 470–482. doi: 10.1016/j.nicl.2016.11.025 28119818

31. Gupta Y, Lee KH, Choi KY, Lee JJ, Kim BC, Kwon G-R. Alzheimer’s Disease Diagnosis Based on Cortical and Subcortical Features. Journal of Healthcare Engineering. 2019;2019: 1–13. doi: 10.1155/2019/2492719 30944718

32. Lama RK, Gwak J, Park J-S, Lee S-W. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features. Journal of Healthcare Engineering. 2017;2017: 1–11. doi: 10.1155/2017/5485080 29065619

33. Lebedeva AK, Westman E, Borza T, Beyer MK, Engedal K, Aarsland D, et al. MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression. Frontiers in Aging Neuroscience. 2017;9. doi: 10.3389/fnagi.2017.00009

34. Wang W-Y, Yu J-T, Liu Y, Yin R-H, Wang H-F, Wang J, et al. Voxel-based meta-analysis of grey matter changes in Alzheimer’s disease. Translational Neurodegeneration. 2015;4. doi: 10.1186/2047-9158-4-4

35. Matsuda H. Voxel-based Morphometry of Brain MRI in Normal Aging and Alzheimer’s Disease. Aging and Disease. 2013;4: 29–37. 23423504

36. Guo X, Wang Z, Li K, Li Z, Qi Z, Jin Z, et al. Voxel-based assessment of gray and white matter volumes in Alzheimer’s disease. Neuroscience Letters. 2010;468: 146–150. doi: 10.1016/j.neulet.2009.10.086 19879920

37. Hwang J, Kim CM, Jeon S, Lee JM, Hong YJ, Roh JH, et al. Prediction of Alzheimer’s disease pathophysiology based on cortical thickness patterns. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 2016;2: 58–67. doi: 10.1016/j.dadm.2015.11.008 27239533

38. Desikan RS, Cabral HJ, Hess CP, Dillon WP, Glastonbury CM, Weiner MW, et al. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease. Brain. 2009;132: 2048–2057. doi: 10.1093/brain/awp123 19460794

39. Zheng W, Yao Z, Hu B, Gao X, Cai H, et al. Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer’s Disease. Zhang Z, editor. Journal of Alzheimer’s Disease. 2015;48: 995–1008. doi: 10.3233/JAD-150311 26444768

40. Chupin M, Gérardin E, Cuingnet R, Boutet C, Lemieux L, Lehéricy S, et al. Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus. 2009;19: 579–587. doi: 10.1002/hipo.20626 19437497

41. Boutet C, Chupin M, Lehéricy S, Marrakchi-Kacem L, Epelbaum S, Poupon C, et al. Detection of volume loss in hippocampal layers in Alzheimer’s disease using 7 T MRI: A feasibility study. NeuroImage: Clinical. 2014;5: 341–348. doi: 10.1016/j.nicl.2014.07.011 25161900

42. Ben Ahmed O, Benois-Pineau J, Allard M, Ben Amar C, Catheline G. Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features. Multimedia Tools and Applications. 2015;74: 1249–1266. doi: 10.1007/s11042-014-2123-y

43. Sørensen L, Igel C, Liv Hansen N, Osler M, Lauritzen M, Rostrup E, et al. Early detection of Alzheimer’s disease using MRI hippocampal texture: Early AD Detection Using Hippocampal Texture. Human Brain Mapping. 2016;37: 1148–1161. doi: 10.1002/hbm.23091 26686837

44. Korolev IO, Symonds LL, Bozoki AC, Alzheimer’s Disease Neuroimaging Initiative. Predicting Progression from Mild Cognitive Impairment to Alzheimer’s Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. Herholz K, editor. PLOS ONE. 2016;11: e0138866. doi: 10.1371/journal.pone.0138866 26901338

45. Kälin AM, Park MTM, Chakravarty MM, Lerch JP, Michels L, Schroeder C, et al. Subcortical Shape Changes, Hippocampal Atrophy and Cortical Thinning in Future Alzheimer’s Disease Patients. Frontiers in Aging Neuroscience. 2017;9. doi: 10.3389/fnagi.2017.00009

46. Long X, Chen L, Jiang C, Zhang L, Alzheimer’s Disease Neuroimaging Initiative. Prediction and classification of Alzheimer disease based on quantification of MRI deformation. Chen K, editor. PLOS ONE. 2017;12: e0173372. doi: 10.1371/journal.pone.0173372 28264071

47. Morris JC. The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology. 1993;43: 2412–2412. doi: 10.1212/WNL.43.11.2412-a 8232972

48. Seo EH, Kim H, Lee KH, Choo IH. Altered Executive Function in Pre-Mild Cognitive Impairment. Seo SW, editor. Journal of Alzheimer’s Disease. 2016;54: 933–940. doi: 10.3233/JAD-160052 27567814

49. Seo EH, Kim H, Choi KY, Lee KH, Choo IH. Association of subjective memory complaint and depressive symptoms with objective cognitive functions in prodromal Alzheimer’s disease including pre-mild cognitive impairment. Journal of Affective Disorders. 2017;217: 24–28. doi: 10.1016/j.jad.2017.03.062 28380342

50. Elwood RW. The Wechsler Memory Scale Revised: Psychometric characteristics and clinical application. Neuropsychology Review. 1991;2: 179–201. doi: 10.1007/BF01109053 1844708

51. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984 Jul; 34: 939–944. doi: 10.1212/wnl.34.7.939 6610841

52. Tustison NJ, Avants BB, Cook PA, Yuanjie Zheng, Egan A, Yushkevich PA, et al. N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging. 2010;29: 1310–1320. doi: 10.1109/TMI.2010.2046908 20378467

53. Ashburner J, Friston KJ. Why Voxel-Based Morphometry Should Be Used. NeuroImage. 2001;14: 1238–1243. doi: 10.1006/nimg.2001.0961 11707080

54. Whitwell JL. Voxel-Based Morphometry: An Automated Technique for Assessing Structural Changes in the Brain. Journal of Neuroscience. 2009;29: 9661–9664. doi: 10.1523/JNEUROSCI.2160-09.2009 19657018

55. Schmitter D, Roche A, Maréchal B, Ribes D, Abdulkadir A, Bach-Cuadra M, et al. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. NeuroImage: Clinical. 2015;7: 7–17. doi: 10.1016/j.nicl.2014.11.001 25429357

56. Xiao Z, Ding Y, Lan T, Zhang C, Luo C, Qin Z. Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion. Computational and Mathematical Methods in Medicine. 2017;2017: 1–13. doi: 10.1155/2017/1952373 28611848

57. Maintz JBA, Viergever MA. A survey of medical image registration. Med Image Anal. 1998; 2: 1–36. doi: 10.1016/S1361-8415(01)80026-8 10638851

58. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole Brain Segmentation. Neuron. 2002;33: 341–355. doi: 10.1016/s0896-6273(02)00569-x 11832223

59. Fischl B. FreeSurfer. NeuroImage. 2012;62: 774–781. doi: 10.1016/j.neuroimage.2012.01.021 22248573

60. Dale AM, Fischl B, Sereno MI. Cortical Surface-Based Analysis. Neuroimage. 1999; 9: 179–194. doi: 10.1006/nimg.1998.0395 9931268

61. Iglesias JE, Augustinack JC, Nguyen K, Player CM, Player A, Wright M, et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage. 2015;115: 117–137. doi: 10.1016/j.neuroimage.2015.04.042 25936807

62. Saygin ZM, Kliemann D, Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M, et al. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. NeuroImage. 2017;155: 370–382. doi: 10.1016/j.neuroimage.2017.04.046 28479476

63. Jack CR, Barkhof F, Bernstein MA, Cantillon M, Cole PE, DeCarli C, et al. Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimer’s & Dementia. 2011;7: 474–485.e4. doi: 10.1016/j.jalz.2011.04.007 21784356

64. Hunsaker MR, Rosenberg JS, Kesner RP. The role of the dentate gyrus, CA3a,b, and CA3c for detecting spatial and environmental novelty. Hippocampus. 2008;18: 1064–1073. doi: 10.1002/hipo.20464 18651615

65. Andersen AH, Gash DM, Avison MJ. Principal component analysis of the dynamic response measured by fMRI: a generalized linear systems framework. Magnetic Resonance Imaging. 1999;17: 795–815. doi: 10.1016/s0730-725x(99)00028-4 10402587

66. Lebedev AV, Westman E, Van Westen GJP, Kramberger MG, Lundervold A, Aarsland D, et al. Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage: Clinical. 2014;6: 115–125. doi: 10.1016/j.nicl.2014.08.023 25379423

67. Chen X, Ishwaran H. Random forests for genomic data analysis. Genomics. 2012;99: 323–329. doi: 10.1016/j.ygeno.2012.04.003 22546560

68. Zhang S, Li X, Zong M, Zhu X, Wang R. Efficient kNN Classification with Different Numbers of Nearest Neighbors. IEEE Transactions on Neural Networks and Learning Systems. 2018;29: 1774–1785. doi: 10.1109/TNNLS.2017.2673241 28422666

69. Mohammed A, Al-Azzo F, Milanova M. Classification of Alzheimer Disease based on Normalized Hu Moment Invariants and Multiclassifier. International Journal of Advanced Computer Science and Applications. 2017;8. doi: 10.14569/IJACSA.2017.081102

70. Cohen J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement. 1960;20: 37–46. doi: 10.1177/001316446002000104

71. Jha D, Alam S, Pyun J-Y, Lee KH, Kwon G-R. Alzheimer’s Disease Detection Using Extreme Learning Machine, Complex Dual Tree Wavelet Principal Coefficients and Linear Discriminant Analysis. Journal of Medical Imaging and Health Informatics. 2018;8: 881–890. doi: 10.1166/jmihi.2018.2381

72. Beheshti I, Demirel H, Farokhian F, Yang C, Matsuda H. Structural MRI-based detection of Alzheimer’s disease using feature ranking and classification error. Computer Methods and Programs in Biomedicine. 2016;137: 177–193. doi: 10.1016/j.cmpb.2016.09.019 28110723


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