Evaluation and Prediction of Early Alzheimer’s Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping

Author(s): Hyug-Gi Kim, Soonchan Park, Hak Y. Rhee, Kyung M. Lee, Chang-Woo Ryu, Soo Y. Lee, Eui J. Kim, Yi Wang, Geon-Ho Jahng*

Journal Name: Current Alzheimer Research

Volume 17 , Issue 5 , 2020

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Abstract:

Background: Because Alzheimer’s Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations.

Objective: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD.

Materials and Methods: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data.

Results: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319).

Conclusion: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

Keywords: Alzheimer`s disease (AD), mild cognitive impairment (MCI), quantitative susceptibility mapping (QSM), gray matter volume (GMV), neurodegenerative disorder, memory loss.

[1]
Albertini V, Benussi L, Paterlini A, et al. Distinct cerebrospinal fluid amyloid-beta peptide signatures in cognitive decline associated with Alzheimer’s disease and schizophrenia. Electrophoresis 2012; 33(24): 3738-44.
[http://dx.doi.org/10.1002/elps.201200307] [PMID: 23161113]
[2]
Jahn H. Memory loss in Alzheimer’s disease. Dialogues Clin Neurosci 2013; 15(4): 445-54.
[PMID: 24459411]
[3]
Petersen RC, Doody R, Kurz A, et al. Current concepts in mild cognitive impairment. Arch Neurol 2001; 58(12): 1985-92.
[http://dx.doi.org/10.1001/archneur.58.12.1985] [PMID: 11735772]
[4]
Shah Y, Tangalos EG, Petersen RC. Mild cognitive impairment. When is it a precursor to Alzheimer’s disease? Geriatrics 2000; 55(9): 62-, 65-68.
[PMID: 10997127]
[5]
Sherwin BB. Mild cognitive impairment: Potential pharmacological treatment options. J Am Geriatr Soc 2000; 48(4): 431-41.
[http://dx.doi.org/10.1111/j.1532-5415.2000.tb04703.x] [PMID: 10798472]
[6]
Haacke EM, Cheng NY, House MJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging 2005; 23(1): 1-25.
[http://dx.doi.org/10.1016/j.mri.2004.10.001] [PMID: 15733784]
[7]
Carmeli C, Fornari E, Jalili M, Meuli R, Knyazeva MG. Structural covariance of superficial white matter in mild Alzheimer’s disease compared to normal aging. Brain Behav 2014; 4(5): 721-37.
[http://dx.doi.org/10.1002/brb3.252] [PMID: 25328848]
[8]
Kim HG, Park S, Rhee HY, et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer’s disease. Neuroimage Clin 2017; 16: 429-38.
[http://dx.doi.org/10.1016/j.nicl.2017.08.019] [PMID: 28879084]
[9]
Saarlas KN, Paluku KM, Roungou JB, Bryce JW, Naimoli JF, Benzerroug H. Multiple methods for workshop evaluation, 1994-95. Int Q Community Health Educ 2006-2007; 27(3): 245-64.
[http://dx.doi.org/10.2190/IQ.27.3.e] [PMID: 18434277]
[10]
Dawson-Elli N, Lee SB, Pathak M, Mitra K, Subramanian VR. Data science approaches for electrochemical engineers: An introduction through surrogate model development for lithium-ion batteries. J Electrochem Soc 2018; 165(2): A1-A15.
[http://dx.doi.org/10.1149/2.1391714jes]
[11]
Alam S, Kwon GR. Initi AsDN. Alzheimer disease classification using KPCA, LDA, and multi-kernel learning SVM. Int J Imaging Syst Technol 2017; 27(2): 133-43.
[http://dx.doi.org/10.1002/ima.22217]
[12]
Schmitter D, Roche A, Maréchal B, et al. Alzheimer’s Disease Neuroimaging Initiative. An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer’s disease. Neuroimage Clin 2014; 7: 7-17.
[http://dx.doi.org/10.1016/j.nicl.2014.11.001] [PMID: 25429357]
[13]
Jo T, Nho K, Saykin AJ. Deep learning in Alzheimer’s disease: Diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci 2019; 11: 220.
[http://dx.doi.org/10.3389/fnagi.2019.00220] [PMID: 31481890]
[14]
Han JS, Lee JJ, Anandan T, et al. Characterization of a chromosomal toxin-antitoxin, Rv1102c-Rv1103c system in Mycobacterium tuberculosis. Biochem Biophys Res Commun 2010; 400(3): 293-8.
[http://dx.doi.org/10.1016/j.bbrc.2010.08.023] [PMID: 20705052]
[15]
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan E. 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; 34(7): 939-44.
[16]
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol 1999; 56(3): 303-8.
[http://dx.doi.org/10.1001/archneur.56.3.303] [PMID: 10190820]
[17]
Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007; 38(1): 95-113.
[http://dx.doi.org/10.1016/j.neuroimage.2007.07.007] [PMID: 17761438]
[18]
Wei Xu IC. A region-growing algorithm for InSAR phase unwrapping. IEEE Trans Geosci Remote Sens 1999; 37(1): 124-34.
[http://dx.doi.org/10.1109/36.739143]
[19]
Acosta-Cabronero J, Williams GB, Cardenas-Blanco A, Arnold RJ, Lupson V, Nestor PJ. In vivo quantitative susceptibility mapping (QSM) in Alzheimer’s disease. PLoS One 2013; 8(11) e81093
[http://dx.doi.org/10.1371/journal.pone.0081093] [PMID: 24278382]
[20]
Connor JR, Snyder BS, Beard JL, Fine RE, Mufson EJ. Regional distribution of iron and iron-regulatory proteins in the brain in aging and Alzheimer’s disease. J Neurosci Res 1992; 31(2): 327-35.
[http://dx.doi.org/10.1002/jnr.490310214] [PMID: 1573683]
[21]
Vandenberghe R, Adamczuk K, Dupont P, Laere KV, Chételat G. Amyloid PET in clinical practice: Its place in the multidimensional space of Alzheimer’s disease. Neuroimage Clin 2013; 2: 497-511.
[http://dx.doi.org/10.1016/j.nicl.2013.03.014] [PMID: 24179802]
[22]
Conover WJ. Practical Nonparametric Statistics. 3rd ed. New York: John Wiley & Sons 1999.
[23]
Juottonen K, Lehtovirta M, Helisalmi S, Riekkinen PJ Sr, Soininen H. Major decrease in the volume of the entorhinal cortex in patients with Alzheimer’s disease carrying the apolipoprotein E epsilon4 allele. J Neurol Neurosurg Psychiat 1998; 65(3): 322-7.
[http://dx.doi.org/10.1136/jnnp.65.3.322] [PMID: 9728943]
[24]
Ward RJ, Zucca FA, Duyn JH, Crichton RR, Zecca L. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol 2014; 13(10): 1045-60.
[http://dx.doi.org/10.1016/S1474-4422(14)70117-6] [PMID: 25231526 ]


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Article Details

VOLUME: 17
ISSUE: 5
Year: 2020
Page: [428 - 437]
Pages: 10
DOI: 10.2174/1567205017666200624204427
Price: $65

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