Advertisement

Metabolic Brain Disease

, Volume 33, Issue 6, pp 1899–1909 | Cite as

Contourlet-based hippocampal magnetic resonance imaging texture features for multivariant classification and prediction of Alzheimer’s disease

  • Ni Gao
  • Li-Xin Tao
  • Jian Huang
  • Feng Zhang
  • Xia Li
  • Finbarr O’Sullivan
  • Si-Peng Chen
  • Si-Jia Tian
  • Gehendra Mahara
  • Yan-Xia Luo
  • Qi Gao
  • Xiang-Tong Liu
  • Wei Wang
  • Zhi-Gang Liang
  • Xiu-Hua Guo
Original Article

Abstract

The study is aimed to assess whether the addition of contourlet-based hippocampal magnetic resonance imaging (MRI) texture features to multivariant models improves the classification of Alzheimer’s disease (AD) and the prediction of mild cognitive impairment (MCI) conversion, and to evaluate whether Gaussian process (GP) and partial least squares (PLS) are feasible in developing multivariant models in this context. Clinical and MRI data of 58 patients with probable AD, 147 with MCI, and 94 normal controls (NCs) were collected. Baseline contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters based on MRI, and regional CMgl measurement based on fluorine-18 fluorodeoxyglucose-positron emission tomography were included to develop GP and PLS models to classify different groups of subjects. GPR1 model, which incorporated MRI texture features and was based on GPG, performed better in classifying different groups of subjects than GPR2 model, which used the same algorithm and had the same data as GPR1 except that MRI texture features were excluded. PLS model, which included the same variables as GPR1 but was based on the PLS algorithm, performed best among the three models. GPR1 accurately predicted 82.2% (51/62) of MCI convertors confirmed during the 2-year follow-up period, while this figure was 53 (85.5%) for PLS model. GPR1 and PLS models accurately predicted 58 (79.5%) vs. 61 (83.6%) of 73 patients with stable MCI, respectively. For seven patients with MCI who converted to NCs, PLS model accurately predicted all cases (100%), while GPR1 predicted six (85.7%) cases. The addition of contourlet-based MRI texture features to multivariant models can effectively improve the classification of AD and the prediction of MCI conversion to AD. Both GPR and LPS models performed well in the classification and predictive process, with the latter having significantly higher classification and predictive accuracies. Advances in knowledge: We combined contourlet-based hippocampal MRI texture features, medical histories, symptoms, neuropsychological tests, volume-based morphometric (VBM) parameters, and regional CMgl measurement to develop models using GP and PLS algorithms to classify AD patients.

Keywords

Alzheimer’s disease Texture feature Contourlets Gaussian process Partial least squares Mild cognitive impairment 

Notes

Authors contribution

Ni Gao and Xiu-Hua Guo contributed to the conception and design of the study; Li-Xin Tao, Jian Huang, Feng Zhang and Xia Li contributed to the acquisition of data; Si-Peng Chen, Si-Jia Tian, Gehendra Mahara and Finbarr O’Sullivan performed the experiments; Yan-Xia Luo, Qi Gao, Xiang-Tong Liu, Wei Wang and Zhi-Gang Liang contributed to the analysis of data; Ni Gao wrote the manuscript; All authors reviewed and approved the final version of the manuscript.

Compliance with ethical standards

Conflict of interest

None.

References

  1. Alzheimer’s A. 2015 Alzheimer’s disease facts and figures. Alzheimer’s Dementia. 2015;11:332–384Google Scholar
  2. Aoki C, Mahadomrongkul V, Fujisawa S, Habersat R, Shirao T (2007) Chemical and morphological alterations of spines within the hippocampus and entorhinal cortex precede the onset of Alzheimer's disease pathology in double knock-in mice. J Comp Neurol 505:352–362CrossRefGoogle Scholar
  3. Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®): American psychiatric pub; 2013CrossRefGoogle Scholar
  4. Blennow K (2004) CSF biomarkers for mild cognitive impairment. J Intern Med 256:224–234CrossRefGoogle Scholar
  5. Challis E, Hurley P, Serra L, Bozzali M, Oliver S, Cercignani M (2015) Gaussian process classification of Alzheimer's disease and mild cognitive impairment from resting-state fMRI. NeuroImage 112:232–243CrossRefGoogle Scholar
  6. Cho Y, Seong JK, Jeong Y, Shin SY, Alzheimer's Disease Neuroimaging Initiative (2012) Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 59:2217–2230CrossRefGoogle Scholar
  7. Costafreda SG, Dinov ID, Tu Z, Shi Y, Liu CY, Kloszewska I, Mecocci P, Soininen H, Tsolaki M, Vellas B, Wahlund LO, Spenger C, Toga AW, Lovestone S, Simmons A (2011) Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. NeuroImage 56:212–219CrossRefGoogle Scholar
  8. Cui Y, Liu B, Luo S, Zhen X, Fan M, Liu T, Zhu W, Park M, Jiang T, Jin JS, Alzheimer's disease neuroimaging I. Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors. PloS One 2011;6:e21896CrossRefGoogle Scholar
  9. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S, Habert MO, Chupin M, Benali H, Colliot O (2011) Alzheimer's disease neuroimaging I. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. NeuroImage 56:766–781CrossRefGoogle Scholar
  10. Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM (2008) Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol Aging 29:514–523CrossRefGoogle Scholar
  11. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ (2011) Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 32(2322):e19–e27Google Scholar
  12. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14:2091–2106CrossRefGoogle Scholar
  13. Eskildsen SF, Coupe P, Garcia-Lorenzo D, Fonov V, Pruessner JC, Collins DL, Alzheimer's Disease Neuroimaging Initiative (2013) Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. NeuroImage 65:511–521CrossRefGoogle Scholar
  14. Ewers M, Walsh C, Trojanowski JQ, Shaw LM, Petersen RC, Jack CR Jr, Feldman HH, Bokde AL, Alexander GE, Scheltens P, Vellas B, Dubois B, Weiner M, Hampel H, North American Alzheimer's Disease Neuroimaging Initiative (2012) Prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging 33:1203–1214CrossRefGoogle Scholar
  15. Fan Y, Batmanghelich N, Clark CM, Davatzikos C, Alzheimer's Disease Neuroimaging Initiative (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39:1731–1743CrossRefGoogle Scholar
  16. Jack CR Jr, Vemuri P, Wiste HJ, Weigand SD, Lesnick TG, Lowe V, Kantarci K, Bernstein MA, Senjem ML, Gunter JL, Boeve BF, Trojanowski JQ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Knopman DS (2012) Alzheimer's disease neuroimaging I. Shapes of the trajectories of 5 major biomarkers of Alzheimer disease. Arch Neurol 69:856–867PubMedPubMedCentralGoogle Scholar
  17. Majumdar A, Bhattacharya A (2009) A comparative study in wavelets, curvelets and contourlets as feature sets for pattern recognition. Int Arab J Inf Technol 6:47–51Google Scholar
  18. Majumder SK, Ghosh N, Gupta PK (2005) Support vector machine for optical diagnosis of cancer. J Biomed Opt 10:024034CrossRefGoogle Scholar
  19. Mangialasche F, Westman E, Kivipelto M, Muehlboeck JS, Cecchetti R, Baglioni M, Tarducci R, Gobbi G, Floridi P, Soininen H, Kloszewska I, Tsolaki M, Vellas B, Spenger C, Lovestone S, Wahlund LO, Simmons A, Mecocci P, AddNeuroMed c (2013) Classification and prediction of clinical diagnosis of Alzheimer's disease based on MRI and plasma measures of alpha−/gamma-tocotrienols and gamma-tocopherol. J Intern Med 273:602–621CrossRefGoogle Scholar
  20. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, Klunk WE, Koroshetz WJ, Manly JJ, Mayeux R, Mohs RC, Morris JC, Rossor MN, Scheltens P, Carrillo MC, Thies B, Weintraub S, Phelps CH (2011) The diagnosis of dementia 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 7:263–269CrossRefGoogle Scholar
  21. Misra C, Fan Y, Davatzikos C (2009) Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. NeuroImage 44:1415–1422CrossRefGoogle Scholar
  22. Ray M, Zhang W (2010) Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks. BMC Syst Biol 4:136CrossRefGoogle Scholar
  23. Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A (2016) Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol 85:824–829CrossRefGoogle Scholar
  24. Sorensen L, Igel C, Liv Hansen N, Osler M, Lauritzen M, Rostrup E, Nielsen M (2016) Alzheimer's disease neuroimaging I, the Australian imaging B, lifestyle flagship study of a. Early detection of Alzheimer's disease using MRI hippocampal texture. Hum Brain Mapp 37:1148–1161CrossRefGoogle Scholar
  25. Tatsuoka C, Tseng H, Jaeger J, Varadi F, Smith MA, Yamada T, Smyth KA, Lerner AJ (2013) Alzheimer's disease neuroimaging I. Modeling the heterogeneity in risk of progression to Alzheimer's disease across cognitive profiles in mild cognitive impairment. Alzheimer's Res Ther 5:14CrossRefGoogle Scholar
  26. Wang HW. Methods and applications of partial least squares: National Defence Industry Press; 1999Google Scholar
  27. Wang J, Hu J (2015) A robust combination approach for short-term wind speed forecasting and analysis–combination of the ARIMA (autoregressive integrated moving average), ELM (extreme learning machine), SVM (support vector machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian process regression) model. Energy 93:41–56CrossRefGoogle Scholar
  28. Wang H, Huang G (2011) Application of support vector machine in cancer diagnosis. Med Oncol 28(Suppl 1):S613–S618CrossRefGoogle Scholar
  29. Waugh SA, Purdie CA, Jordan LB, Vinnicombe S, Lerski RA, Martin P, Thompson AM (2016) Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol 26:322–330CrossRefGoogle Scholar
  30. Westman E, Simmons A, Muehlboeck JS, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Weiner MW, Lovestone S, Spenger C, Wahlund LO, AddNeuroMed consortium, Alzheimer's Disease Neuroimaging Initiative (2011) AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America. Neuroimage 58:818–828CrossRefGoogle Scholar
  31. Westman E, Muehlboeck JS, Simmons A (2012) Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion. NeuroImage 62:229–238CrossRefGoogle Scholar
  32. Yang Z, Wen W, Jiang J, Crawford JD, Reppermund S, Levitan C, Slavin MJ, Kochan NA, Richmond RL, Brodaty H, Trollor JN, Sachdev PS (2016) Structural MRI biomarkers of mild cognitive impairment from young elders to centenarians. Curr Alzheimer Res 13:256–267CrossRefGoogle Scholar
  33. Zhang J, Liu M, An L, Gao Y, Shen D (2017) Alzheimer's disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J Biomed Health Inform 21:1607–1616CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ni Gao
    • 1
    • 2
  • Li-Xin Tao
    • 1
    • 2
  • Jian Huang
    • 3
  • Feng Zhang
    • 1
    • 2
  • Xia Li
    • 3
  • Finbarr O’Sullivan
    • 3
  • Si-Peng Chen
    • 1
    • 2
  • Si-Jia Tian
    • 1
    • 2
  • Gehendra Mahara
    • 1
    • 2
  • Yan-Xia Luo
    • 1
    • 2
  • Qi Gao
    • 1
    • 2
  • Xiang-Tong Liu
    • 1
    • 2
  • Wei Wang
    • 4
  • Zhi-Gang Liang
    • 5
  • Xiu-Hua Guo
    • 1
    • 2
  1. 1.School of Public HealthCapital Medical UniversityBeijingChina
  2. 2.Beijing Municipal Key Laboratory of Clinical EpidemiologyBeijingChina
  3. 3.Department of Epidemiology & Public HealthUniversity College CorkCorkIreland
  4. 4.School of Medical ScienceEdith Cowan UniversityPerthAustralia
  5. 5.Department of Radiology, Xuanwu HospitalCapital Medical UniversityBeijingChina

Personalised recommendations