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| Title: | 運用函數主成分分析於阿茲海默症之診斷
Application of functional principal component analysis to diagnosis of Alzheimer’s disease |
| Authors: | 李詠玄
Lee, Yong-Shiuan |
| Contributors: | 劉惠美
Liu, Hui-Mei
李詠玄
Lee, Yong-Shiuan |
| Keywords: | 阿茲海默症
函數主成分分析
遞迴類神經網路
長短期記憶類神經網路
長期追蹤資料
Alzheimer’s disease
Functional principal component analysis
Recurrent neural networks
Long short-term memory networks
Longitudinal data |
| Date: | 2022 |
| Issue Date: | 2022-08-01 17:13:51 (UTC+8) |
| Abstract: | 自二十世紀晚期對於探討阿茲海默症成因、病情發展與有效治療方式的研究大量增加。其中最重要的目標之一即為於早期診斷出阿茲海默症，也就是輕度認知障礙。診斷輕度認知障礙或阿茲海默症即是統計上的分類問題。通常阿茲海默症的相關研究資料皆為長期追蹤資料，由於資料收集的方式，使得資料多為稀疏性且不規則間隔的資料。再者，基於近年來醫學診斷工具，尤其是腦部顯影的技術進步與普及，阿茲海默症資料更常為具有高維度的資料。傳統上常用的統計分類方法對於此類資料型態有其侷限性。本研究首先將資料的變數視為只具有少數觀察值的函數，使用函數主成分分析工具來重建高維度、稀疏且不規則間隔的資料，使資料收集區間內的所有觀察時間點皆能有函數的估計值。接續再利用遞迴類神經網路中專門針對時間序列資料的長短期記憶類神經網路，來對研究對象做診斷的分類。本研究的實證結果指出在最佳情境下，此作法使用較多觀察值於訓練資料集，以及使用較多的輸入變數，能夠正確辨認出最多的早期輕度認知障礙者（十一個患者中正確辨認出五個）。顯示此法對於辨認早期的輕度認知障礙有較大的潛力。針對阿茲海默症此類醫學研究中常見的不平衡資料，未來可考慮加入重新採樣的方法或是成本考量的分類方法進一步發展優化本文所提出之程序。
Since the late 20th century, researches of Alzheimer’s disease intending to better understand the causes, the progression, and effective treatments of this disease have boosted. One of the most important purposes of these researches is to detect the disease at early stages, that is, the diagnosis of mild cognitive impairment. The diagnosis is certainly the classification problem in statistics. The research data of Alzheimer’s disease are usually longitudinal, which can be very sparse and irregularlyspaced as a result of data collection process. Additionally, the research data can also have high imensional features due to improvement in clinical neuroimaging techniques. Classical approaches for classification have limitations in using the sparse and irregular, highdimensional, longitudinal data. This study is the first to implement the tool of the functional principal component analysis to reconstruct the whole
functions of all variables during the period, and then to apply the long shortterm memory networks, a recurrent neural network designed for time series data, for classification. The empirical results show that in the bestcase scenario this method identifies 5 out of 11 MCI cases in the testing dataset while the other methods only accurately predict 0 or 1 MCI case. The results suggest that this procedure has great potential for early detection of Alzheimer’s disease. The proposed method can further be developed for imbalanced data with resampling or costsensitive classification techniques. |
| Reference: | [1] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat,
G. Irving, M. Isard, et al. Tensorflow: A system for largescale
machine learning.
In 12th {USENIX} Symposium on Operating Systems Design and Implementation
({OSDI} 16), pages 265–283, 2016.
[2] A. Anoop, P. K. Singh, R. S. Jacob, and S. K. Maji. CSF biomarkers for Alzheimer’s
disease diagnosis. International journal of Alzheimer’s disease, 2010:Article ID
606802, 12 pages, 2010.
[3] A. Association. 2020 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia,
16(3):391–460, 2020.
[4] A. Association. 2021 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia,
17(3):327–406, 2021.
[5] S. Balakrishnan and D. Madigan. Decision trees for functional variables. In Sixth
International Conference on Data Mining (ICDM’06), pages 798–802. IEEE, 2006.
[6] E. Belli and S. Vantini. Measure inducing classification and regression trees for
functional data. Statistical Analysis and Data Mining: The ASA Data Science Journal,
2021.
[7] Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Signal
Processing, 2(1):1–127, 2009.
[8] J. R. Berrendero, A. Justel, and M. Svarc. Principal components for multivariate
functional data. Computational Statistics & Data Analysis, 55(9):2619–2634, 2011.
[9] M. Bertoux, J. Lagarde, F. Corlier, L. Hamelin, J.F.
Mangin, O. Colliot, M. Chupin,
M. N. Braskie, P. M. Thompson, M. Bottlaender, et al. Sulcal morphology in
Alzheimer’s disease: An effective marker of diagnosis and cognition. Neurobiology
of Aging, 84:41–49, 2019.
[10] M. C. Biagioni and J. E. Galvin. Using biomarkers to improve detection of
Alzheimer’s disease. Neurodegenerative Disease Management, 1(2):127–139,
2011.
[11] S. Borson, J. Scanlan, M. Brush, P. Vitaliano, and A. Dokmak. The MiniCog:
A
cognitive ‘vital signs’measure for dementia screening in multilingual
elderly.
International journal of geriatric psychiatry, 15(11):1021–1027, 2000.
[12] S. Borson, J. M. Scanlan, P. Chen, and M. Ganguli. The MiniCog
as a screen
for dementia: Validation in a populationbased
sample. Journal of the American
Geriatrics Society, 51(10):1451–1454, 2003.
[13] L. Breiman. Bagging predictors. Machine Learning, 24(2):123–140, 1996.
[14] L. Breiman. Random forests. Machine Learning, 45(1):5–32, 2001.
[15] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and
Regression Trees. Chapman & Hall/CRC, New York., 1984.
[16] A. M. Brickman, J. J. Manly, L. S. Honig, D. Sanchez, D. ReyesDumeyer,
R. A.
Lantigua, P. J. Lao, Y. Stern, J. P. Vonsattel, A. F. Teich, et al. Plasma ptau181,ptau217,
and other bloodbased
Alzheimer’s disease biomarkers in a multiethnic,
community study. Alzheimer’s & Dementia, 17(8):1353–1364, 2021.
[17] R. S. Bucks, D. Ashworth, G. Wilcock, and K. Siegfried. Assessment of activities
of daily living in dementia: Development of the bristol activities of daily living
scale. Age and ageing, 25(2):113–120, 1996.
[18] H. Buschke, G. Kuslansky, M. Katz, W. F. Stewart, M. J. Sliwinski, H. M. Eckholdt,
and R. B. Lipton. Screening for dementia with the memory impairment screen.
Neurology, 52(2):231–231, 1999.
[19] B. D. Carpenter, C. Xiong, E. K. Porensky, M. M. Lee, P. J. Brown, M. Coats,
D. Johnson, and J. C. Morris. Reaction to a dementia diagnosis in individuals with
Alzheimer’s disease and mild cognitive impairment. Journal of the American Geriatrics
Society, 56(3):405–412, 2008.
[20] L.H.
Chen and C.R.
Jiang. Multidimensional
functional principal component
analysis. Statistics and Computing, 27(5):1181–1192, 2017.
[21] W.C.
Cheng, L.H.
Chen, C.R.
Jiang, Y.M.
Deng, D.W.
Wang, C.H.
Lin, R. Jou,
J.K.
Wang, and Y.L.
Wang. Sensible functional linear discriminant analysis effectively
discriminates enhanced Raman spectra of Mycobacterium species. Analytical
Chemistry, 93(5):2785–2792, 2021. PMID: 33480698.
[22] R. Chin, A. Ng, K. Narasimhalu, and N. Kandiah. Utility of the AD8 as a selfrating
tool for cognitive impairment in an Asian population. American Journal of
Alzheimer’s Disease & Other Dementias®, 28(3):284–288, 2013.
[23] J.M.
Chiou, Y.T.
Chen, and Y.F.
Yang. Multivariate functional principal component
analysis: A normalization approach. Statistica Sinica, pages 1571–1596,
2014.
[24] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk,
and Y. Bengio. Learning phrase representations using RNN encoderdecoder
for
statistical machine translation. In Proceedings of the 2014 Conference on Empirical
Methods in Natural Language Processing (EMNLP), page 1724–1734. Association
for Computational Linguistics (ACL), Oct. 2014.
[25] S. H. Cho, S. Woo, C. Kim, H. J. Kim, H. Jang, B. C. Kim, S. E. Kim, S. J. Kim, J. P.
Kim, Y. H. Jung, et al. Disease progression modelling from preclinical Alzheimer’
s disease (AD) to AD dementia. Scientific reports, 11(1):1–10, 2021.
[26] F. Chollet et al. Keras. urlhttps://github.com/fchollet/keras, 2015.
[27] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. Empirical evaluation of gated recurrent
neural networks on sequence modeling. arXiv preprint arXiv:1412.3555,
2014.
[28] M. Conceição, A. KroneMartins,
and A. da Silva. FPCA emulation of cosmological
simulations. In 2021 IEEE 17th International Conference on eScience
(eScience), pages 225–226. IEEE, 2021.
[29] C. Cortes and V. Vapnik. Support vector machine. Machine Learning, 20(3):273–
297, 1995.
[30] R. Cui, M. Liu, A. D. N. Initiative, et al. RNNbased
longitudinal analysis for
diagnosis of Alzheimer’s disease. Computerized Medical Imaging and Graphics,
73:1–10, 2019.
[31] J. M. Cuttler, E. Abdellah, Y. Goldberg, S. AlShamaa,
S. P. Symons, S. E. Black,
and M. Freedman. Low doses of ionizing radiation as a treatment for Alzheimer’
s disease: A pilot study. Journal of Alzheimer’s Disease, 80(3):1119–1128, 2021.
[32] A. Delaigle and P. Hall. Achieving near perfect classification for functional
data. Journal of the Royal Statistical Society: Series B (Statistical Methodology),
74(2):267–286, 2012.
[33] A. Delaigle and P. Hall. Classification using censored functional data. Journal of
the American Statistical Association, 108(504):1269–1283, 2013.
[34] A. Delaigle, P. Hall, and N. Bathia. Componentwise classification and clustering
of functional data. Biometrika, 99(2):299–313, 2012.
[35] L. Deng and D. Yu. Deep learning: Methods and applications. Foundations and
Trends in Signal Processing, 7(3–4):197–387, 2014.
[36] B. Dunn, P. Stein, and P. Cavazzoni. Approval of Aducanumab for Alzheimer
disease—The FDA’s perspective. JAMA Internal Medicine, 181(10):1276–1278,
2021.
[37] S. ElSappagh,
T. Abuhmed, S. R. Islam, and K. S. Kwak. Multimodal multitask
deep learning model for Alzheimer’s disease progression detection based on time
series data. Neurocomputing, 412:197–215, 2020.
[38] A. Ezzati, M. J. Katz, A. R. Zammit, M. L. Lipton, M. E. Zimmerman, M. J. Sliwinski,
and R. B. Lipton. Differential association of left and right hippocampal
volumes with verbal episodic and spatial memory in older adults. Neuropsychologia,
93:380–385, 2016.
[39] J. Fan and I. Gijbels. Local Polynomial Modelling and Its Applications. Chapman
& Hall/CRC, London, 1996.
[40] C. Feng, A. Elazab, P. Yang, T. Wang, F. Zhou, H. Hu, X. Xiao, and B. Lei. Deep
learning framework forAlzheimer’s disease diagnosis via 3DCNN
and FSBiLSTM.
IEEE Access, 7:63605–63618, 2019.
[41] A. Field, J. Miles, and Z. Field. Discovering statistics using R. Sage Publications,
2012.
[42] M. F. Folstein, S. E. Folstein, and P. R. McHugh. “Minimental
state”: A practical
method for grading the cognitive state of patients for the clinician. Journal of
psychiatric research, 12(3):189–198, 1975.
[43] P. Forouzannezhad, A. Abbaspour, C. Fang, M. Cabrerizo, D. Loewenstein,
R. Duara, and M. Adjouadi. A survey on applications and analysis methods of
functional magnetic resonance imaging for Alzheimer’s disease. Journal of neuroscience
methods, 317:121–140, 2019.
[44] S. Förster, B. H. Yousefi, H.J.
Wester, E. Klupp, A. Rominger, H. Förstl, A. Kurz,
T. Grimmer, and A. Drzezga. Quantitative longitudinal interrelationships between
brain metabolism and amyloid deposition during a 2year
followup
in patients with
early Alzheimer’s disease. European journal of nuclear medicine and molecular
imaging, 39(12):1927–1936, 2012.
[45] J. H. Friedman. Regularized discriminant analysis. Journal of the American Statistical
Association, 84(405):165–175, 1989.
[46] A. Gajardo, C. Carroll, Y. Chen, X. Dai, J. Fan, P. Z. Hadjipantelis, K. Han, H. Ji,
H.G.
Müller, and J.L.
Wang. fdapace: Functional Data Analysis and Empirical
Dynamics, 2021. R package version 0.5.7.
[47] T. P. Garcia and K. Marder. Statistical approaches to longitudinal data analysis in
neurodegenerative diseases: Huntington’s disease as a model. Current Neurology
and Neuroscience Reports, 17(2):1–9, 2017.
[48] S. Gauthier, P. RosaNeto,
J. A. Morais, C. Webster, et al. World Alzheimer report
2021 Journey
through the diagnosis of dementia. https://www.alzint.org/
resource/world-alzheimer-report-2021/. Accessed: 20210928.
[49] I. Gélinas, L. Gauthier, M. McIntyre, and S. Gauthier. Development of a functional
measure for persons with Alzheimer’s disease: the disability assessment
for dementia. American Journal of Occupational Therapy, 53(5):471–481, 1999.
[50] M. M. Ghazi, M. Nielsen, A. Pai, M. J. Cardoso, M. Modat, S. Ourselin,
L. Sørensen, A. D. N. Initiative, et al. Training recurrent neural networks robust
to incomplete data: Application to Alzheimer’s disease progression modeling.
Medical Image Analysis, 53:39–46, 2019.
[51] Y. Gupta, R. K. Lama, G.R.
Kwon, M. W. Weiner, P. Aisen, M. Weiner, R. Petersen,
C. R. Jack Jr, W. Jagust, J. Q. Trojanowki, et al. Prediction and classification
of Alzheimer’s disease based on combined features from apolipoproteinE
genotype,
cerebrospinal fluid, MR, and FDGPET
imaging biomarkers. Frontiers in
Computational Neuroscience, 13:72, 2019.
[52] Y. Gupta, K. H. Lee, K. Y. Choi, J. J. Lee, B. C. Kim, G. R. Kwon, N. R. C. for
Dementia, and A. D. N. Initiative. Early diagnosis of Alzheimer’s disease using
combined features from voxelbased
morphometry and cortical, subcortical, and
hippocampus regions of MRI T1 brain images. PLoS One, 14(10):e0222446, 2019.
[53] C. Happ and S. Greven. Multivariate functional principal component analysis for
data observed on different (dimensional) domains. Journal of the American Statistical
Association, 113(522):649–659, 2018.
[54] C. HappKurz.
Objectoriented
software for functional data. Journal of Statistical
Software, 93(5):1–38, 2020.
[55] C. HappKurz.
MFPCA: Multivariate Functional Principal Component Analysis
for Data Observed on Different Dimensional Domains, 2021. R package version
1.39.
[56] J. A. Hardy and G. A. Higgins. Alzheimer’s disease: The amyloid cascade hypothesis.
Science, 256(5054):184–186, 1992.
[57] K. Hasenstab, A. Scheffler, D. Telesca, C. A. Sugar, S. Jeste, C. DiStefano, and
D. Şentürk. A multidimensional
functional principal components analysis of EEG
data. Biometrics, 73(3):999–1009, 2017.
[58] T. Hastie. [Flexible Parsimonious Smoothing and Additive Modeling]: Discussion.
Technometrics, 31(1):23–29, 1989.
[59] T. Hastie, A. Buja, and R. Tibshirani. Penalized discriminant analysis. The Annals
of Statistics, 23(1):73–102, 1995.
[60] T. Hastie, R. Tibshirani, and A. Buja. Flexible discriminant analysis by optimal
scoring. Journal of the American Statistical Association, 89(428):1255–1270,
1994.
[61] S. Hochreiter and J. Schmidhuber. Long shortterm
memory. Neural Computation,
9(8):1735–1780, 1997.
[62] H. Hodkinson. Evaluation of a mental test score for assessment of mental impairment
in the elderly. Age and ageing, 1(4):233–238, 1972.
[63] W. Huang, Y. Zhou, L. Tu, Z. Ba, J. Huang, N. Huang, and Y. Luo. TDP43:
From
Alzheimer’s disease to limbicpredominant
agerelated
TDP43
encephalopathy.
Frontiers in Molecular Neuroscience, 13:26, 2020.
[64] S. Iddi, D. Li, P. S. Aisen, M. S. Rafii, W. K. Thompson, and M. C. Donohue.
Predicting the course of Alzheimer’s progression. Brain Informatics, 6(1):1–18,
2019.
[65] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training
by reducing internal covariate shift. In International Conference on Machine
Learning, pages 448–456. PMLR, 2015.
[66] Z. Ismail, L. AgüeraOrtiz,
H. Brodaty, A. Cieslak, J. Cummings, C. E. Fischer,
S. Gauthier, Y. E. Geda, N. Herrmann, J. Kanji, et al. The Mild Behavioral
Impairment Checklist (MBIC):
A rating scale for neuropsychiatric symptoms in
predementia
populations. Journal of Alzheimer’s disease, 56(3):929–938, 2017.
[67] Z. Ismail, T. K. Rajji, and K. I. Shulman. Brief cognitive screening instruments: An
update. International Journal of Geriatric Psychiatry: A journal of the psychiatry
of late life and allied sciences, 25(2):111–120, 2010.
[68] C. R. Jack Jr, D. A. Bennett, K. Blennow, M. C. Carrillo, B. Dunn, S. B. Haeberlein,
D. M. Holtzman, W. Jagust, F. Jessen, J. Karlawish, et al. NIAAA
research
framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s &
Dementia, 14(4):535–562, 2018.
[69] C. R. Jack Jr, D. S. Knopman, W. J. Jagust, R. C. Petersen, M. W. Weiner, P. S.
Aisen, L. M. Shaw, P. Vemuri, H. J. Wiste, S. D. Weigand, et al. Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of
dynamic biomarkers. The Lancet Neurology, 12(2):207–216, 2013.
[70] C. R. Jack Jr, D. S. Knopman, W. J. Jagust, L. M. Shaw, P. S. Aisen, M. W. Weiner,
R. C. Petersen, and J. Q. Trojanowski. Hypothetical model of dynamic biomarkers
of the Alzheimer’s pathological cascade. The Lancet Neurology, 9(1):119–128,
2010.
[71] C. R. Jack Jr, P. Vemuri, H. J. Wiste, S. D. Weigand, P. S. Aisen, J. Q. Trojanowski,
L. M. Shaw, M. A. Bernstein, R. C. Petersen, M. W. Weiner, et al. Evidence for
ordering of Alzheimer disease biomarkers. Archives of Neurology, 68(12):1526–
1535, 2011.
[72] J. Jacques and C. Preda. Modelbased
clustering for multivariate functional data.
Computational Statistics & Data Analysis, 71:92–106, 2014.
[73] C.R.
Jiang, J. A. Aston, and J.L.
Wang. A functional approach to deconvolve
dynamic neuroimaging data. Journal of the American Statistical Association,
111(513):1–13, 2016.
[74] C.R.
Jiang and L.H.
Chen. Filteringbased
approaches for functional data classification.
Wiley Interdisciplinary Reviews: Computational Statistics, 12(4):e1490,
2020.
[75] M. Jo, S. Lee, Y.M.
Jeon, S. Kim, Y. Kwon, and H.J.
Kim. The role of TDP43
propagation in neurodegenerative diseases: Integrating insights from clinical and
experimental studies. Experimental & Molecular Medicine, 52(10):1652–1662,
2020.
[76] K. A. Josephs, D. W. Dickson, N. Tosakulwong, S. D. Weigand, M. E. Murray,
L. Petrucelli, A. M. Liesinger, M. L. Senjem, A. J. Spychalla, D. S. Knopman, et al. Rates of hippocampal atrophy and presence of postmortem
TDP43
in patients with
Alzheimer’s disease: A longitudinal retrospective study. The Lancet Neurology,
16(11):917–924, 2017.
[77] N. Kandiah, A. Zhang, D. C. Bautista, E. Silva, S. K. S. Ting, A. Ng, and P. Assam.
Early detection of dementia in multilingual populations: Visual Cognitive
Assessment Test (VCAT). Journal of Neurology, Neurosurgery & Psychiatry,
87(2):156–160, 2016.
[78] K. Karhunen. Über lineare methoden in der wahrscheinlichkeitsrechnung. Annales
Academiae Scientiarum Fennicae. Series A. 1: MathematicaPhysica,
37:1–
79, 1947.
[79] M. Khanzadeh, S. Chowdhury, M. Marufuzzaman, M. A. Tschopp, and L. Bian.
Porosity prediction: Supervisedlearning
of thermal history for direct laser deposition.
Journal of manufacturing systems, 47:69–82, 2018.
[80] H. Kim and H. Kim. Functional logistic regression with fused lasso penalty. Journal
of Statistical Computation and Simulation, 88(15):2982–2999, 2018.
[81] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization in proceedings
of the 3rd international conference on learning representations (san diego, ca).
2015.
[82] W. E. Klunk, H. Engler, A. Nordberg, Y. Wang, G. Blomqvist, D. P. Holt,
M. Bergström, I. Savitcheva, G.F.
Huang, S. Estrada, et al. Imaging brain amyloid
in Alzheimer’s disease with Pittsburgh CompoundB.
Annals of Neurology: Official
Journal of the American Neurological Association and the Child Neurology
Society, 55(3):306–319, 2004.
[83] P. Kokoszka and M. Reimherr. Introduction to Functional Data Analysis. Chapman
and Hall/CRC, Boca Raton, 2017.
[84] M. Krzyśko, P. Nijkamp, W. Ratajczak, and W. Wołyński. Multidimensional economic
indicators and multivariate functional principal component analysis (MFPCA)
in a comparative study of countries’competitiveness. Journal of Geographical
Systems, 24:49–65, 2022.
[85] J. K. Kueper, M. Speechley, and M. MonteroOdasso.
The Alzheimer’s disease
assessment scale–cognitive subscale (ADASCog):
Modifications and responsiveness
in predementia
populations. A narrative review. Journal of Alzheimer’s Disease,
63(2):423–444, 2018.
[86] N. M. Laird and J. H. Ware. Randomeffects
models for longitudinal data. Biometrics,
38:963–974, 1982.
[87] K. L. Lanctôt, J. Amatniek, S. AncoliIsrael,
S. E. Arnold, C. Ballard, J. CohenMansfield,
Z. Ismail, C. Lyketsos, D. S. Miller, E. Musiek, et al. Neuropsychiatric
signs and symptoms of Alzheimer’s disease: New treatment paradigms.
Alzheimer’s & Dementia: Translational Research & Clinical Interventions,
3(3):440–449, 2017.
[88] J. LanteroRodriguez,
A. Snellman, A. L. Benedet, M. MilàAlomà,
E. Camporesi,
L. MontoliuGaya,
N. J. Ashton, A. Vrillon, T. K. Karikari, J. D. Gispert, et al. Ptau235:
A novel biomarker for staging preclinical Alzheimer’s disease. EMBO
molecular medicine, 13(12):e15098, 2021.
[89] A. J. Larner. The usage of cognitive screening instruments: Test characteristics and
suspected diagnosis. In Cognitive Screening Instruments, pages 219–238. Springer,
London, 2013.
[90] C. Ledig, A. Schuh, R. Guerrero, R. A. Heckemann, and D. Rueckert. Structural
brain imaging in Alzheimer’s disease and mild cognitive impairment: Biomarker
analysis and shared morphometry database. Scientific reports, 8(1):1–16, 2018.
[91] G. Lee, K. Nho, B. Kang, K.A.
Sohn, and D. Kim. Predicting Alzheimer’s disease
progression using multimodal
deep learning approach. Scientific Reports,
9(1):1–12, 2019.
[92] J. C. Lee, S. J. Kim, S. Hong, and Y. Kim. Diagnosis of Alzheimer’s disease
utilizing amyloid and tau as fluid biomarkers. Experimental & Molecular Medicine,
51(5):1–10, 2019.
[93] X. Leng and H.G.
Müller. Classification using functional data analysis for temporal
gene expression data. Bioinformatics, 22(1):68–76, 2006.
[94] A. Li, F. Li, F. Elahifasaee, M. Liu, and L. Zhang. Hippocampal shape and asymmetry
analysis by cascaded convolutional neural networks for Alzheimer’s disease
diagnosis. Brain Imaging and Behavior, 15(5):2330–2339, 2021.
[95] B. Li and Q. Yu. Classification of functional data: A segmentation approach. Computational
Statistics & Data Analysis, 52(10):4790–4800, 2008.
[96] C. Li, L. Xiao, and S. Luo. Fast covariance estimation for multivariate sparse functional
data. Stat, 9(1):e245, 2020.
[97] D. Li, S. Iddi, W. K. Thompson, M. C. Donohue, and A. D. N. Initiative. Bayesian
latent time joint mixed effect models for multicohort longitudinal data. Statistical
Methods in Medical Research, 28(3):835–845, 2019.
[98] H. Li, T. Pan, Y. Li, S. Chen, and G. Li. Functional principal component analysis for
nearinfrared
spectral data: A case study on Tricholoma matsutakeis. International
Journal of Food Engineering, 16(8), 2020.
[99] K. Li and S. Luo. Dynamic prediction of Alzheimer’s disease progression using
features of multiple longitudinal outcomes and timetoevent
data. Statistics in
Medicine, 38(24):4804–4818, 2019.
[100] W. Li, X. Lin, and X. Chen. Detecting Alzheimer’s disease based on 4d fMRI: An
exploration under deep learning framework. Neurocomputing, 388:280–287, 2020.
[101] X. Li, G. Qi, C. Yu, G. Lian, H. Zheng, S. Wu, T.F.
Yuan, and D. Zhou. Cortical
plasticity is correlated with cognitive improvement in Alzheimer’s disease
patients after rTMS treatment. Brain Stimulation, 14(3):503–510, 2021.
[102] M. P. Lichtenstein, P. Carriba, R. Masgrau, A. Pujol, and E. Galea. Staging antiinflammatory
therapy in Alzheimer’s disease. Frontiers in Aging Neuroscience,
2:142, 2010.
[103] W. Liggett, L. Cazares, and O. J. Semmes. A look at mass spectral measurement.
Chance, 16(4):24–28, 2003.
[104] N. Lin, J. Jiang, S. Guo, and M. Xiong. Functional principal component analysis
and randomized sparse clustering algorithm for medical image analysis. PLoS One,
10(7):e0132945, 2015.
[105] M. Liu, D. Cheng, W. Yan, A. D. N. Initiative, et al. Classification of Alzheimer’s
disease by combination of convolutional and recurrent neural networks using FDGPET
images. Frontiers in Neuroinformatics, 12:35, 2018.
[106] Y. Liu, L. Tan, H.F.
Wang, Y. Liu, X.K.
Hao, C.C.
Tan, T. Jiang, B. Liu, D.Q.
Zhang, and J.T.
Yu. Multiple effect of APOE genotype on clinical and neuroimaging
biomarkers across Alzheimer’s disease spectrum. Molecular Neurobiology,
53(7):4539–4547, 2016.
[107] M. Loève. Fonctions aléatoires à décomposition orthogonale exponentielle. La
Revue Scientifique, 84:159–162, 1946.
[108] Mayo Clinic Staff. Alzheimer’s stages: How the disease progresses.
https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/
in-depth/alzheimers-stages/art-20048448. Accessed: 20211101.
[109] M. Mehdipour Ghazi, M. Nielsen, A. Pai, M. Modat, M. Jorge Cardoso, S. Ourselin,
and L. Sørensen. Robust parametric modeling of Alzheimer’s disease progression.
NeuroImage, 225:117460, 2021.
[110] S. A. Mofrad, A. J. Lundervold, A. Vik, and A. S. Lundervold. Cognitive and MRI
trajectories for prediction of Alzheimer’s disease. Scientific Reports, 11(1):1–10,
2021.
[111] R. C. Mohs, D. Knopman, R. C. Petersen, S. H. Ferris, C. Ernesto, M. Grundman,
M. Sano, L. Bieliauskas, D. Geldmacher, C. Clark, et al. Development of cognitive
instruments for use in clinical trials of antidementia drugs: Additions to the
Alzheimer’s disease assessment scale that broaden its scope. Alzheimer Disease
and Associated Disorders, 1997.
[112] M. Mojirsheibani and C. Shaw. Classification with incomplete functional covariates.
Statistics & Probability Letters, 139:40–46, 2018.
[113] A. Möller, G. Tutz, and J. Gertheiss. Random forests for functional covariates.
Journal of Chemometrics, 30(12):715–725, 2016.
[114] H.g.
Müller. Functional modelling and classification of longitudinal data. Scandinavian
Journal of Statistics, 32(2):223–240, 2005.
[115] Z. S. Nasreddine, N. A. Phillips, V. Bédirian, S. Charbonneau, V. Whitehead,
I. Collin, J. L. Cummings, and H. Chertkow. The Montreal Cognitive Assessment,
MoCA: A brief screening tool for mild cognitive impairment. Journal of the American
Geriatrics Society, 53(4):695–699, 2005.
[116] M. Nguyen, T. He, L. An, D. C. Alexander, J. Feng, B. T. Yeo, A. D. N. Initiative,
et al. Predicting Alzheimer’s disease progression using deep recurrent neural
networks. NeuroImage, 222:117203, 2020.
[117] NIH National Institute on Aging (NIA). How biomarkers help diagnose dementia.
https://www.nia.nih.gov/health/how-biomarkers-help-diagnose-dementia#
future_biomarkers. Accessed: 20220201.
[118] NIH National Institute on Aging (NIA). How is alzheimer’s disease treated? https:
//www.nia.nih.gov/health/how-alzheimers-disease-treated. Accessed: 20220201.
[119] M. B. T. Noor, N. Z. Zenia, M. S. Kaiser, S. A. Mamun, and M. Mahmud. Application
of deep learning in detecting neurological disorders from magnetic resonance
images: A survey on the detection of Alzheimer’s disease, Parkinson’s disease
and schizophrenia. Brain Informatics, 7(1):1–21, 2020.
[120] T. Noori, A. R. Dehpour, A. Sureda, E. SobarzoSanchez,
and S. Shirooie. Role
of natural products for the treatment of Alzheimer’s disease. European Journal of
Pharmacology, 898:173974, 2021.
[121] H.J.
Park, K. J. Friston, C. Pae, B. Park, and A. Razi. Dynamic effective connectivity
in resting state fMRI. NeuroImage, 180:594–608, 2018.
[122] Penn Medicine. The 7 stages of Alzheimer’s disease. https://www.pennmedicine.
org/updates/blogs/neuroscience-blog/2019/november/stages-of-alzheimers.
Accessed: 20211101.
[123] R. C. Petersen. Alzheimer’s disease: Progress in prediction. The Lancet Neurology,
9(1):4–5, 2010.
[124] J. Pinheiro and D. Bates. Mixedeffects
models in S and SPLUS.
Springer, New
York, 2006.
[125] J. Pinheiro, D. Bates, S. DebRoy, D. Sarkar, and R Core Team. nlme: Linear and
Nonlinear Mixed Effects Models, 2013. R package version 3.1153.
[126] J. R. Quinlan. Induction of decision trees. Machine Learning, 1(1):81–106, 1986.
[127] J. R. Quinlan. C4.5: Programs for machine learning. Elsevier, 2014.
[128] G. D. Rabinovici. Controversy and progress in Alzheimer’s disease —FDA approval
of Aducanumab. New England Journal of Medicine, 385(9):771–774, 2021.
[129] J. Ramsay, G. Hooker, and S. Graves. Functional Data Analysis with R and MATLAB.
Springer, New York, 2009.
[130] J. Ramsay and B. W. Silverman. Functional Data Analysis (2 ed.). Springer, New
York, 2005.
[131] M. Ravanelli, P. Brakel, M. Omologo, and Y. Bengio. Light gated recurrent units
for speech recognition. IEEE Transactions on Emerging Topics in Computational
Intelligence, 2(2):92–102, 2018.
[132] C. Reitz. Alzheimer’s disease and the amyloid cascade hypothesis: A critical review.
International journal of Alzheimer’s disease, 2012:Article ID 369808, 11
pages, 2012.
[133] K. E. Roach, V. Pedoia, J. J. Lee, T. Popovic, T. M. Link, S. Majumdar, and R. B.
Souza. Multivariate functional principal component analysis identifies waveform
features of gait biomechanics related to earlytomoderate
hip osteoarthritis. Journal
of Orthopaedic Research®, 39(8):1722–1731, 2021.
[134] F. Rossi and N. Villa. Support vector machine for functional data classification.
Neurocomputing, 69(79):
730–742, 2006.
[135] I. Saied, T. Arslan, and S. Chandran. Classification of Alzheimer’s disease using
RF signals and machine learning. IEEE Journal of Electromagnetics, RF and
Microwaves in Medicine and Biology, 6(1), 2022.
[136] A. Sarica, R. Vasta, F. Novellino, M. G. Vaccaro, A. Cerasa, A. Quattrone, A. D. N.
Initiative, et al. MRI asymmetry index of hippocampal subfields increases through
the continuum from the mild cognitive impairment to the Alzheimer’s disease.
Frontiers in Neuroscience, page 576, 2018.
[137] S. W. Scheff, D. A. Price, F. A. Schmitt, M. A. Scheff, and E. J. Mufson. Synaptic
loss in the inferior temporal gyrus in mild cognitive impairment and alzheimer’s
disease. Journal of Alzheimer’s Disease, 24(3):547–557, 2011.
[138] P. Scheltens, D. Leys, F. Barkhof, D. Huglo, H. Weinstein, P. Vermersch, M. Kuiper,
M. Steinling, E. C. Wolters, and J. Valk. Atrophy of medial temporal lobes on
MRI in ” probable” Alzheimer’s disease and normal ageing: Diagnostic value and
neuropsychological correlates. Journal of Neurology, Neurosurgery & Psychiatry,
55(10):967–972, 1992.
[139] S. A. Sikkes, E. S. de Langede
Klerk, Y. A. Pijnenburg, F. Gillissen, R. Romkes,
D. L. Knol, B. M. Uitdehaag, and P. Scheltens. A new informantbased
questionnaire for instrumental activities of daily living in dementia. Alzheimer’s & Dementia,
8(6):536–543, 2012.
[140] A. Singleton, M. Farrer, J. Johnson, A. Singleton, S. Hague, J. Kachergus, M. Hulihan,
T. Peuralinna, A. N. Dutra, S. Lincoln, et al. αsynuclein
locus triplication
causes Parkinson’s disease. Science, 302(5646):841–842, 2003.
[141] R. Smith, T. Mukerji, and T. Lupo. Correlating geologic and seismic data with
unconventional resource production curves using machine learning. Geophysics,
84(2):O39–O47, 2019.
[142] T. A. Snijders and R. J. Bosker. Multilevel analysis: An introduction to basic and
advanced multilevel modeling (2 ed.). Sage Publications, London, 2011.
[143] H. Sørensen, J. Goldsmith, and L. M. Sangalli. An introduction with medical applications
to functional data analysis. Statistics in Medicine, 32(30):5222–5240,
2013.
[144] R. A. Sperling, P. S. Aisen, L. A. Beckett, D. A. Bennett, S. Craft, A. M. Fagan,
T. Iwatsubo, C. R. Jack Jr, J. Kaye, T. J. Montine, et al. Toward defining
the preclinical stages of Alzheimer’s disease: Recommendations from the National
Institute on AgingAlzheimer’s
Association workgroups on diagnostic guidelines
for Alzheimer’s disease. Alzheimer’s & dementia, 7(3):280–292, 2011.
[145] S. Srivastava, R. Ahmad, and S. K. Khare. Alzheimer’s disease and its treatment
by different approaches: A review. European Journal of Medicinal Chemistry,
216:113320, 2021.
[146] J. E. Storey, J. T. Rowland, D. A. Conforti, and H. G. Dickson. The Rowland universal
dementia assessment scale (RUDAS): A multicultural cognitive assessment
scale. International Psychogeriatrics, 16(1):13–31, 2004.
[147] Y. Su and C.C.
J. Kuo. On extended long shortterm
memory and dependent bidirectional
recurrent neural network. Neurocomputing, 356:151–161, 2019.
[148] Taiwan Alzheimer Disease Association. 認識失智症. http://www.tada2002.org.
tw/About/IsntDementia, 04 2021. Accessed: 20210928.
[149] M. Tanveer, B. Richhariya, R. Khan, A. Rashid, P. Khanna, M. Prasad, and C. Lin.
Machine learning techniques for the diagnosis of Alzheimer’s disease: A review.
ACM Transactions on Multimedia Computing, Communications, and Applications
(TOMM), 16(1s):1–35, 2020.
[150] S. J. Teipel, W. Bayer, G. E. Alexander, Y. Zebuhr, D. Teichberg, L. Kulic, M. B.
Schapiro, H.J.
Möller, S. I. Rapoport, and H. Hampel. Progression of Corpus
Callosum Atrophy in Alzheimer Disease. Archives of Neurology, 59(2):243–248,
02 2002.
[151] C. G. Thomas, R. A. Harshman, and R. S. Menon. Noise reduction in BOLDbased
fMRI using component analysis. Neuroimage, 17(3):1521–1537, 2002.
[152] M. Torso, M. Bozzali, G. Zamboni, M. Jenkinson, S. A. Chance, and A. D. N.
Initiative. Detection of Alzheimer’s disease using cortical diffusion tensor imaging.
Human Brain Mapping, 42(4):967–977, 2021.
[153] D. Tosun, Z. Demir, D. P. Veitch, D. Weintraub, P. Aisen, C. R. Jack Jr,
W. J. Jagust, R. C. Petersen, A. J. Saykin, L. M. Shaw, et al. Contribution of
Alzheimer’s biomarkers and risk factors to cognitive impairment and decline across
the Alzheimer’s disease continuum. Alzheimer’s & Dementia, 2021.
[154] G. Van Rossum and F. L. Drake Jr. Python reference manual. Centrum voor
Wiskunde en Informatica Amsterdam, 1995.
[155] M. Vernooij, F. Pizzini, R. Schmidt, M. Smits, T. Yousry, N. Bargallo, G. Frisoni,
S. Haller, and F. Barkhof. Dementia imaging in clinical practice: A europeanwide
survey of 193 centres and conclusions by the ESNR working group. Neuroradiology,
61(6):633–642, 2019.
[156] R. Viviani, G. Grön, and M. Spitzer. Functional principal component analysis of
fMRI data. Human brain mapping, 24(2):109–129, 2005.
[157] M. Walterfang, E. Luders, J. C. Looi, P. Rajagopalan, D. Velakoulis, P. M. Thompson,
O. Lindberg, P. Östberg, L. E. Nordin, L. Svensson, et al. Shape analysis of
the corpus callosum in Alzheimer’s disease and frontotemporal lobar degeneration
subtypes. Journal of Alzheimer’s Disease, 40(4):897–906, 2014.
[158] J.L.
Wang, J.M.
Chiou, and H.G.
Müller. Functional data analysis. Annual Review
of Statistics and Its Application, 3:257–295, 2016.
[159] L. Wang, Y. Zang, Y. He, M. Liang, X. Zhang, L. Tian, T. Wu, T. Jiang, and K. Li.
Changes in hippocampal connectivity in the early stages of Alzheimer’s disease:
Evidence from resting state fMRI. Neuroimage, 31(2):496–504, 2006.
[160] Y. Wei, G. Xiao, H. Deng, H. Chen, M. Tong, G. Zhao, and Q. Liu. Hyperspectral
image classification using FPCAbased
kernel extreme learning machine. Optik,
126(23):3942–3948, 2015.
[161] R. K. Wong, Y. Li, and Z. Zhu. Partially linear functional additive models for
multivariate functional data. Journal of the American Statistical Association,
114(525):406–418, 2019.
[162] World Health Organization. Dementia. https://www.who.int/news-room/
fact-sheets/detail/dementia. Accessed: 2021-09-28.
[163] World Health Organization. Dementia: a public health priority. https://www.who.
int/publications/i/item/dementia-a-public-health-priority. Accessed: 2021-09-28.
[164] World Health Organization. The top 10 causes of death. https://www.who.int/
news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed: 2021-09-28.
[165] Y. Wu and Y. Liu. Functional robust support vector machines for sparse and
irregular longitudinal data. Journal of computational and Graphical Statistics,
22(2):379–395, 2013.
[166] S. Xie. Wavelet power spectral domain functional principal component analysis for
feature extraction of epileptic EEGs. Computation, 9(7):78, 2021.
[167] F. Xue, F. Tan, Z. Ye, J. Chen, and Y. Wei. Spectralspatial
classification of hyperspectral
image using improved functional principal component analysis. IEEE
Geoscience and Remote Sensing Letters, 19:1–5, 2021.
[168] B. Yang, H. Yu, M. Xing, R. He, R. Liang, and L. Zhou. The relationship between
cognition and depressive symptoms, and factors modifying this association,
in Alzheimer’s disease: A multivariate multilevel model. Archives of Gerontology
and Geriatrics, 72:25–31, 2017.
[169] L. Yang, J. Yan, X. Jin, Y. Jin, W. Yu, S. Xu, and H. Wu. Screening for dementia
in older adults: Comparison of MiniMental
State Examination, MiniCog,
Clock
Drawing Test and AD8. PLOS ONE, 11(12):1–9, 12 2016.
[170] F. Yao, E. Lei, and Y. Wu. Effective dimension reduction for sparse functional data.
Biometrika, 102(2):421–437, 2015.
[171] F. Yao, H.G.
Müller, and J.L.
Wang. Functional data analysis for sparse longitudinal
data. Journal of the American statistical association, 100(470):577–590,
2005.
[172] F. Yao, Y. Wu, and J. Zou. Probabilityenhanced
effective dimension reduction for
classifying sparse functional data. Test, 25(1):1–22, 2016.
[173] L. Zhang, M. Wang, M. Liu, and D. Zhang. A survey on deep learning for
neuroimagingbased
brain disorder analysis. Frontiers in Neuroscience, page 779,
2020.
[174] 台灣神經學學會Taiwan Neurological Society. 台灣神經學學會會訊2020 年
01 月第80 期. http://www.neuro.org.tw/files/newsletter/080.pdf. Accessed:
2021-09-28.
[175] 衛生福利部Ministry of Health and Welfare. 失智症防治照護政策綱
領暨行動方案2.0(含工作項目)(2021 年版). https://1966.gov.tw/LTC/
cp-4020-42469-201.html. Accessed: 2021-09-28.
[176] 衛生福利部中央健康保險署National Health Insurance Administration, Ministry
of Health and Welfare. 最新版藥品給付規定內容
第1 節神經系統藥
物drugs acting on the nervous system. https://www.nhi.gov.tw/Content_List.
aspx?n=E70D4F1BD029DC37&topn=5FE8C9FEAE863B46. Update: 20220224,
Accessed: 2022-03-02.
[177] 衛生福利部統計處Department of Statistics, Ministry of Health and Welfare.
國際失智症日衛生福利統計通報. https://www.mohw.gov.tw/
dl-71799-1d824fee-a486-4504-9c7d-5d819c6848b2.html. Accessed: 2021-09-28. |
| Description: | 博士
國立政治大學
統計學系
99354501 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0099354501 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201025 |
| Appears in Collections: | [統計學系] 學位論文
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