Abstract
There has always been a need for discovering efficient and dependable Alzheimer’s disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10–15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
For the Alzheimer’s Disease Neuroimaging Initiative.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aguilar C, Westman E, Muehlboeck J, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Lovestone S, Spenger C, Simmons A, Wahlund L (2013) Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment. Psychiatry Res. Neuroimaging 212(2):89–98
Aksu Y, Miller D, Kesidis G, Bigler D, Yang Q (2011) An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients. PLoS One 6(10):e25074
Boser B, Guyon I, Vapnik V (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th annual workshop on Computational learning theory – COLT’92
Braak H, Braak E (1995) Staging of alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging 16(3):271–278
Cho Y, Seong J, Jeong Y, Shin S (2012) Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. NeuroImage 59(3):2217–2230
Christensen GE, Joshi SC, Miller MI (1997) Volumetric transformation of brain anatomy. IEEE Trans Med Imaging 16:864–877
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Csernansky JG, Hamstra J, Wang L, McKeel D, Price JL, Gado M, Morris JC (2004) Correlations between antemortem hippocampal volume and postmortem neuropathology in AD subjects. Alzheimer Dis Assoc Disord 4:190–195
Cui Y, Sachdev P, Lipnicki D, Jin J, Luo S, Zhu W, Kochan N, Reppermund S, Liu T, Trollor J, Brodaty H, Wen W (2012) Predicting the development of mild cognitive impairment: a new use of pattern recognition. NeuroImage 60(2):894–901
Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehéricy S, Habert M, Chupin M, Benali H, Colliot O (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
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3):968–980
Falahati F, Westman E, Simmons A (2014) Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J Alzheimers Dis 41(3):685–708
Fan Y, Shen D, Gur RC, Davatzikosa C (2007) COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans Med Imaging 26(1):93–105
Fan Y, Resnick SM, Wu X, Davatzikos C (2008a) Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. NeuroImage 41(2):277–285
Fan Y, Batmanghelich N, Clark CM, Davatzikos C, Alzheimer’s Disease Neuroimaging Initiative (2008b) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39(4):1731–1743
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355
Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM (2004) Sequence-independent segmentation of magnetic resonance images. NeuroImage 23(Suppl 1):S69–S84
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I (2009) The WEKA data mining software. ACM SIGKDD Explor Newsl 11(1):10
Hanley J, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143:29–36
Hartig M, Truran-Sacrey D, Raptentsetsang S, Simonson A, Mezher A, Schuff N, Weiner M (2014) UCSF FreeSurfer methods. [online] ADNI, pp 1–11. Available at: https://ida.loni.usc.edu/pages/access/studyData.jsp?searchDescription=ucsf. Accessed 2 May 2018
Jack C, Knopman D, Jagust W, Shaw L, Aisen P, Weiner M, Petersen R, Trojanowski J (2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9(1):119–128
Jack C, Knopman D, Jagust W, Petersen R, Weiner M, Aisen P, Shaw L, Vemuri P, Wiste H, Weigand S, Lesnick T, Pankratz V, Donohue M, Trojanowski J (2013) Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 12(2):207–216
Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR Jr, Ashburner J, Frackowiak RSJ (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3):681–689
Kourou K, Exarchos T, Exarchos K, Karamouzis M, Fotiadis D (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8–17
Lao Z, Shen D, Xue Z, Karacali B, Resnick SM, Davatzikos C (2004) Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage 21(1):46–57
Lee S, Bachman A, Yu D, Lim J, Ardekani B (2016) Predicting progression from mild cognitive impairment to Alzheimer’s disease using longitudinal callosal atrophy. Alzheimers Dement 2:68–74
Long X, Chen L, Jiang C, Zhang L (2017) Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One 12(3):e0173372
Louridas P, Ebert C (2016) Machine learning. IEEE Softw 33(5):110–115
Metz C (2006) Receiver operating characteristic (ROC) analysis: a tool for quantitative evaluation of observer performance and imaging systems. J Am Coll Radiol 3:413–422
Murphy M, LeVine H (2010) Alzheimer’s disease and the amyloid-β peptide. J Alzheimers Dis 19(1):311–323
Petersen RC (2004) Mild cognitive impairment: aging to Alzheimer’s disease. Oxford University Press, Madison Avenue, New York
Querbes O, Aubry F, Pariente J, Lotterie J, Démonet J, Duret V, Puel M, Berry I, Fort J, Celsis P (2009) Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 132(8):2036–2047
Quinlan J (1986) Induction of decision trees. Mach Learn 1(1):81–106
Reuter M, Rosas HD, Fischl B (2010) Highly accurate inverse consistent registration: a robust approach. NeuroImage 53(4):1181–1196. http://reuter.mit.edu/papers/reuter-robreg10.pdf
Ritchie K, Ritchie C, Yaffe K, Skoog I, Scarmeas N (2015) Is late-onset Alzheimer’s disease really a disease of midlife? Alzheimers Dement 1(2):122–130
Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27(4):623–656
Skolariki K, Avramouli A (2017) The use of translational research platforms in clinical and biomedical data exploration. Adv Exp Med Biol 988:301–311
Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97
Thompson PM, Mega MS, Woods RP, Zoumalan CI, Lindshield CJ, Blanton RE, Moussai J, Holmes CJ, Cummings JL, Toga AW (2001) Cortical change in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cereb Cortex 11(1):1–16
Tondelli M, Wilcock G, Nichelli P, De Jager C, Jenkinson M, Zamboni G (2012) Structural MRI changes detectable up to ten years before clinical Alzheimer’s disease. Neurobiol Aging 33(4):825.e25–825.e36
Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, Knopman DS, Boeve BF, Petersen RC, Jack CR Jr (2008) Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. NeuroImage 39(3):1186–1197
Walhovd KB, Fjell AM, Brewer J, McEvoy LK, Fennema-Notestine C, Hagler DJ Jr, Jennings RG, Karow D, Dale AM (2010) Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease. AJNR Am J Neuroradiol 31:347–354
Westman E, Cavallin L, Muehlboeck JS, Zhang Y, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Spenger C, Lovestone S, Simmons A, Wahlund LO (2011) Sensitivity and specificity of medial temporal lobe visual ratings and multivariate regional MRI classification in Alzheimer’s disease. PLoS One 6:e22506
Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang D, Rueckert D, Soininen H, Lötjönen J (2011) Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One 6(10):e25446
Zhang D, Wang Y, Zhou L, Yuan H, Shen D (2011) Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55:856–867
Acknowledgments
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Supplementary Data
Supplementary Data
Methods
Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD).
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Skolariki, K., Terrera, G.M., Danso, S. (2020). Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-32622-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-32622-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32621-0
Online ISBN: 978-3-030-32622-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)