Skip to main content

Machine Learning Techniques for AD/MCI Diagnosis and Prognosis

  • Chapter
  • First Online:
Machine Learning in Healthcare Informatics

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 56))

Abstract

In the past two decades, machine learning techniques have been extensively applied for the detection of neurologic or neuropsychiatric disorders, especially Alzheimer’s disease (AD) and its prodrome, mild cognitive impairment (MCI). This chapter presents some of the latest developments in the application of machine learning techniques to AD and MCI diagnosis and prognosis. We will divide our discussion into two parts: single modality and multimodality approaches. We will discuss how various biomarkers as well as connectivity networks can be extracted from the various modalities, such as structural T1-weighted imaging, diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI), for effective diagnosis and prognosis. We will further demonstrate how these modalities can be fused for further performance improvement.

Chong-Yaw Wee, Daoqiang Zhang and Luping Zhou contributed equally to this book chapter.

The work was performed when Luping Zhou was with the Department of Radiology and BRIC, University of North Carolina at Chapel Hill.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Brookmeyer R et al (2007) Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dementia 3(3):186–191

    Article  MathSciNet  Google Scholar 

  2. Johnson SC et al (2006) Activation of brain regions vulnerable to Alzheimer’s disease: the effect of mild cognitive impairment. Neurobio Aging 27(11):1604–1612

    Article  Google Scholar 

  3. Thompson PM, Apostolova LG (2007) Computational anatomical methods as applied to ageing and dementia. Br J Radiol 80:S78–S91

    Article  Google Scholar 

  4. Whitwell JL et al (2007) 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain 130(7):1777–1786

    Article  Google Scholar 

  5. Grundman M et al (2004) Mild cognitive impairment can be distinguished from Alzheimer’s disease and normal aging for clinical trials. Arch Neurol 61(1):59–66

    Article  MathSciNet  Google Scholar 

  6. Bischkopf J, Busse A, Angermeyer MC (2002) Mild cognitive impairment—a review of prevalence, incidence and outcome according to current approaches. Acta Psychiatr Scand 106:403–414

    Article  Google Scholar 

  7. Jack CR Jr et al (2005) Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 65(8):1227–1231

    Article  Google Scholar 

  8. Jack CR Jr et al (2010) Hepothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9(1):119–128

    Article  Google Scholar 

  9. Nestor PJ, Scheltens P, Hodges JR (2004) Advances in the early detection of Alzheimer’s disease. Nature 5:S34–S41

    Google Scholar 

  10. Davatzikos C et al (2010) Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 32:e19–e27

    Google Scholar 

  11. Davatzikos C et al (2008) Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobio Aging 29:514–523

    Article  Google Scholar 

  12. Fan Y et al (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39:1731–1743

    Article  Google Scholar 

  13. Vemuri P et al (2009) MRI and CSF biomarkers in normal, MCI, and AD subjects: predicting future clinical change. Neurology 73(4):294–301

    Article  MathSciNet  Google Scholar 

  14. Vapnik VN (1999) The nature of statistical learning theory (Statistics for Engineering and Information Science). Springer, Heidelberg

    Google Scholar 

  15. Morra JH et al (2010) Comparison of AdaBoost and support vector machines for detecting Alzheimer’s disease through automated hippocampal segmentation. IEEE Trans Med Imaging 29(1):30–43

    Article  Google Scholar 

  16. Jiang J, Trundle P, Ren J (2010) Medical image analysis with artificial neural networks. Comput Med Imaging Graph 34(8):617–631

    Article  Google Scholar 

  17. Fitzpatrick JM, Sonka M (2000) Handbook of medical imaging, vol 2. In: Sonka M (ed) Medical image processing and analysis. PM80SC. SPIEthe International Society for Optical Engineering

    Google Scholar 

  18. Bankman IN (ed) (2008) Handbook of medical image processing and analysis. Academic Press, New York

    Google Scholar 

  19. Kloppel S et al (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3):681–689

    Article  Google Scholar 

  20. Fan Y et al (2008) Unaffected family members and Schizophrenia patients share brain structure patterns: A high-dimensional pattern classification study. Biol Psychiatry 63(1):118–124

    Article  Google Scholar 

  21. Fan Y et al (2007) COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans Med Imaging 26(1):93–105

    Article  Google Scholar 

  22. Davatzikos C et al (2008) Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29:514–523

    Article  Google Scholar 

  23. Vemuri P et al (2008) Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39(3):1186–1197

    Article  MathSciNet  Google Scholar 

  24. Duchesne S et al (2005) Predicting clinical variable from MRI features: application to MMSE in MCI. Med Image Comput Comput Assist Interv 8(1):392–399

    Google Scholar 

  25. Chu C et al (2007) Regression analysis for clinical scores of Alzheimer’s disease using multivariate machine learning method. In: Human Brain Mapping, Chicago

    Google Scholar 

  26. Fan Y, Kaufer D, Shen D (2009) Estimating clinical variables from brain images using Bayesian regression. Alzheimer’s Dimentia 5(4):372

    Google Scholar 

  27. Westman E et al (2010) Multivariate analysis of MRI data for Alzheimer’s disease, mild cognitive impairment and healthy controls. Neuroimage 54(2):1178–1187

    Article  Google Scholar 

  28. Lao Z et al (2004) Morphological classification of brains via high-dimensional shape transformations and machine learning methods. Neuroimage 21:46–57

    Article  Google Scholar 

  29. Chetelat G, Baron JC (2003) Early diagnosis of Alzheimer’s disease: contribution of structural neuroimaging. Neuroimage 18(2):525–541

    Article  Google Scholar 

  30. Jack CR Jr et al (1998) Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. Neurology 51(4):993–999

    Article  Google Scholar 

  31. Thompson PM et al (2004) Mapping cortical change in Alzheimer’s disease, brain development, and Schizophrenia. J Neurosci 23:S2–S18

    Google Scholar 

  32. Dickerson BC et al (2009) The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb Cortex 19(3):497–510

    Article  Google Scholar 

  33. Thompson PM et al (2001) Cortical changes in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cereb Cortex 11(1):1–16

    Article  Google Scholar 

  34. Chupin M et al (2009) Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19(6):579–587

    Article  Google Scholar 

  35. Colliot O et al (2008) Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. Radiology 248(1):194–201

    Article  Google Scholar 

  36. Gong G et al (2009) Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19:524–536

    Article  Google Scholar 

  37. Rose SE, Janke AL, Chalk JB (2007) Gray and white matter changes in Alzheimer’s disease: a diffusion tensor imaging study. J Magn Reson Imaging 27(1):20–26

    Article  Google Scholar 

  38. Zhang Y et al (2007) Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 68(1):13–19

    Article  Google Scholar 

  39. Friston KJ et al (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13:5–14

    Article  Google Scholar 

  40. Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 21:424–430

    Article  Google Scholar 

  41. Biswal B et al (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541

    Article  Google Scholar 

  42. Sorg C et al (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. PNAS 104(47):18760–18765

    Article  Google Scholar 

  43. Greicius MD et al (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. PNAS 101(13):4637–4642

    Article  Google Scholar 

  44. Diehl J et al (2004) Cerebral metabolic patterns at early stages of frontotemporal dementia and semantic dementia. A PET study. Neurobio Aging 25(8):1051–1056

    Article  Google Scholar 

  45. Fjell AM et al (2010) CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer’s disease. J Neurosci 30(6):2088–2101

    Article  Google Scholar 

  46. Landau SM et al (2010) Comparing predictors of conversion and decline in mild cognitive impairment. Neurology 75(3):230–238

    Article  Google Scholar 

  47. Walhovd KB et al (2010) Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobio Aging 31(7):1107–1121

    Article  Google Scholar 

  48. Geroldi C et al (2006) Medial temporal atrophy but not memory deficit predicts progression to dementia in patients with mild cognitive impairment. J Neurol Neurosurg Psychiatry 77:1219–1222

    Article  Google Scholar 

  49. Hinrichs C et al (2009) Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 48(1):138–149

    Article  Google Scholar 

  50. Ye J et al (2008) Heterogeneous data fusion for Alzheimer’s disease study. In: Paper presented at the proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, 2008

    Google Scholar 

  51. Fellgiebel A et al (2007) FDG-PET and CSF phospho-tau for prediction of cognitive decline in mild cognitive impairment. Psychiatry Res Neuroimag 155(2):167–171

    Article  Google Scholar 

  52. Chetelat G et al (2005) FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment. Neurocase 11(1):14–25

    Article  Google Scholar 

  53. Walhovd KB et al (2010) Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. Am J Neuroradiol 31(2):347–354

    Article  Google Scholar 

  54. Hagmann P et al (2008) Mapping the structural core of human cerebral cortex. PLoS Comput Biol 6:e159

    Article  Google Scholar 

  55. Sporns O, Zwi JD (2004) The small world of the cerebral cortex. Neuroinformatics 2:145–161

    Article  Google Scholar 

  56. Wee CY et al (2011) Enriched white matter connectivity networks for accurate identification of MCI patients. Neuroimage 54(3):1812–1822

    Article  Google Scholar 

  57. Rubinov M, Sporns O (2010) Complex networks measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

    Article  Google Scholar 

  58. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Article  Google Scholar 

  59. Guyon I et al (2004) Gene selection for cancer classification using support vector machines. Machine Learning 46(1–3):389–422

    Google Scholar 

  60. Rakotomamonjy A (2003) Variable selection using SVM based criteria. J Mach Learn Res: Special issue on special feature 3:1357–1370

    MathSciNet  MATH  Google Scholar 

  61. Wee CY et al (2011) Classification of MCI patients via functional connectivity networks. In: ISMRM’ 2011 Québec, Canada

    Google Scholar 

  62. Bassett DS, Bullmore E (2006) Small-world brain networks. The Neuroscientist 12(6):512–523

    Article  Google Scholar 

  63. Courchesne E et al (2000) Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 216:672–682

    Article  Google Scholar 

  64. Karas GB et al (2003) A comprehensive study of gray matter loss in patients with Alzheimer’s disease using optimized voxel-based morphometry. Neuroimage 18(4):895–907

    Article  Google Scholar 

  65. Thompson PM et al (2003) Dynamics of gray matter loss in Alzheimer’s disease. J Neuroscience 23(3):994–1005

    Google Scholar 

  66. Whitwell JL et al (2008) MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology 70(7):512–520

    Article  Google Scholar 

  67. Van Dijk KRA et al (2010) Intrinsic functional connectivity as a tool for human connectomics: theory, properties and optimization. J Neurophysiol 103:297–321

    Article  Google Scholar 

  68. Cordes D et al (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am J Neuroradiol 22:1326–1333

    Google Scholar 

  69. Achard S et al (2008) Fractal connectivity of long-memory networks. Phys Rev E Stat Nonlin Soft Matter Phys 77(3 Pt 2):036104

    Article  Google Scholar 

  70. Zhou L et al (2011) Hierarchical anatomical brain networks for MCI prediction by partial least square analysis. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  71. Rosipal R, Kramer N (2006) Overview and recent advances in partial least squares. Lect Notes Comput Sci 3940:34–51

    Article  Google Scholar 

  72. Wold S et al (1993) PLS—partial least-squares projections to latent structures. In: Kubinyi H (ed) 3D QSAR in drug design: theory methods and applications, vol 1. ESCOM, Leiden, pp 523–550

    Google Scholar 

  73. Hinrichs C et al (2009) MKL for robust multi-modality AD classification. Med Image Comput Comput Assist Interv Part II:786–794

    Google Scholar 

  74. Zhang D et al (2011) Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3):856–867

    Article  Google Scholar 

  75. Scholkopf B, Smola AJ (2002) Learning with Kernels. MIT Press, Massachusetts

    Google Scholar 

  76. Lanckriet GRG et al (2004) Learning the Kernel matrix with semidefinite programming. J Mach Learn Res 5:27–72

    MathSciNet  MATH  Google Scholar 

  77. Bach FR, Lanckriet GRG, Jordan MI (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the twenty-first international conference on Machine learning (ICLM’04), p 6

    Google Scholar 

  78. Wang Z, Chen S, Sun T (2008) MultiK-MHKS: a novel multiple kernel learning algorithm. IEEE Trans Pattern Analysis Mach Intell 30(2):348–353

    Article  Google Scholar 

  79. Lanckriet GR et al (2004) Kernel-based data fusion and its application to protein function prediction in yeast. Pac Symp Biocomput, In, pp 300–311

    Google Scholar 

  80. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines

    Google Scholar 

  81. Zhang D, Shen D (2011) Semi-supervised multimodal classification of Alzheimer’s disease. In: IEEE international symposium on biomedical imaging (ISBI’11)

    Google Scholar 

  82. Tiwari P et al (2010) Semi supervised multi kernel (SeSMiK) graph embedding: identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy. Med Image Comput Comput Assist Interv 2010:666–673

    Google Scholar 

  83. Chapelle O, Scholkopf B, Zien A (eds) (2006) Semi-supervised learning. MIT Press, Cambridge

    Google Scholar 

  84. Belkin M et al (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434

    MathSciNet  MATH  Google Scholar 

  85. McKhann G et al (1984) 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 34(7):939–944

    Article  Google Scholar 

  86. Cuingnet R et al (2011) Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 56(2):766–781

    Article  Google Scholar 

  87. Pengas G et al (2010) Focal posterior cingulate atrophy in incipient Alzheimer’s disease. Neurobio Aging 31(1):25–33

    Article  Google Scholar 

  88. Nestor SM et al (2008) Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. Brain 131(9):2443–2454

    Article  Google Scholar 

  89. Bozzali M et al (2002) White matter damage in Alzheimer’s disease assessed in vivo using diffusion tensor magnetic resonance imaging. J Neurol Neurosurg Psychiatry 72(6):742–746

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dinggang Shen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Shen, D., Wee, CY., Zhang, D., Zhou, L., Yap, PT. (2014). Machine Learning Techniques for AD/MCI Diagnosis and Prognosis. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40017-9_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40016-2

  • Online ISBN: 978-3-642-40017-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics