Fahimeh Nezhadmoghadam
ABSTRACT
Discovering and characterizing reproducible disease subtypes results is one of the most demanding and fundamental tasks in many fields, such as bioinformatics and health informatics. It could facilitate diagnosis and is a vital step toward more individualized therapy. This paper aims to analyze the ability of unsupervised learning methods to identify a small collection of reliable and stable subtypes of subjects with mild cognitive impairment (MCI) and to discover the primary prodromal Alzheimer's disease (AD) stages in subjects with MCI to AD conversion risk. We present a novel unsupervised learning methodology to identify the notable stable and reproducible subtypes. The proposed method takes advantage of the consensus clustering of unsupervised clustering methods. For this mean, we obtained the data from the Alzheimer's disease Neuroimaging Initiative study. 346 features, including demographic information and MRI-derived features, described 839 subjects with early MCI. We randomly split the data into discovery (70%) and validation (30%) sets. The discovery set was analyzed using five unsupervised clustering methods, and robust consensus clustering was used to determine the most stable and reliable subtypes. The results show that the proposed method identified four different MCI patient subtypes. After discovery, subtypes were predicted in the testing set and associated with MCI conversion. One subtype had a high-risk (OR = 2.99, 95%CI = 1.65 to 5.41) of converting to Alzheimer's disease.
- Arem, Hannah, and Erikka Loftfield. "Cancer epidemiology: a survey of modifiable risk factors for prevention and survivorship." American journal of lifestyle medicine 12.3 (2018): 200-210.Google Scholar
- Li, Xiaowan, and Fei Zhu. "On clustering algorithms for biological data." Engineering 5.549 (2013): 10-4236.Google Scholar
- Alashwal, Hany, "The application of unsupervised clustering methods to Alzheimer's disease." Frontiers in computational neuroscience 13 (2019): 31.Google Scholar
- Wiwie, Christian, Jan Baumbach, and Richard Röttger. "Comparing the performance of biomedical clustering methods." Nature methods 12.11 (2015): 1033-1038.Google Scholar
- Chen, Chien-Hsing. "A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection." Applied Soft Computing 20 (2014): 4-14.Google Scholar
- Nilashi, M., O. Ibrahim, and A. Ahani. "‘Accuracy improvement for predicting Parkinson's disease progression,’’Sci." (2016).Google Scholar
- Yilmaz, Nihat, Onur Inan, and Mustafa Serter Uzer. "A new data preparation method based on clustering algorithms for diagnosis systems of heart and diabetes diseases." J Med Syst 38 (2014): 48-59.Google Scholar
- Nezhadmoghadam, Fahimeh, "Robust Discovery of Mild Cognitive impairment subtypes and their Risk of Alzheimer's Disease conversion using unsupervised machine learning and Gaussian Mixture Modeling." Current Alzheimer Research 18.7 (2021): 595-606.Google Scholar
- Alashwal, Hany, "The application of unsupervised clustering methods to Alzheimer's disease." Frontiers in computational neuroscience 13 (2019): 31.Google Scholar
- Károly, Artúr István, Róbert Fullér, and Péter Galambos. "Unsupervised clustering for deep learning: A tutorial survey." Acta Polytechnica Hungarica 15.8 (2018): 29-53.Google Scholar
- Matthew D. Wilkerson, D. Neil Hayes, ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking, Bioinformatics 26 (12) (2010) 1572–1573.Google ScholarDigital Library
- Association, A.s., 2018 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 2018. 14(3): p. 367-429.Google Scholar
- Ward, A., , Rate of conversion from prodromal Alzheimer's disease to Alzheimer's dementia: a systematic review of the literature. Dementia and geriatric cognitive disorders extra, 2013. 3(1): p. 320-332.Google Scholar
- Marinescu, Razvan V., "The alzheimer's disease prediction of longitudinal evolution (TADPOLE) challenge: Results after 1 year follow-up." arXiv preprint arXiv:2002.03419 (2020).Google Scholar
- Edmonds, E.C., , Early versus late MCI: Improved MCI staging using a neuropsychological approach. Alzheimer's & Dementia, 2019. 15(5): p. 699-708.Google Scholar
- Aisen, P.S., , Clinical Core of the Alzheimer's Disease Neuroimaging Initiative: progress and plans. Alzheimer's & Dementia, 2010. 6(3): p. 239-246.Google Scholar
- McInnes, Leland, John Healy, and James Melville. "Umap: Uniform manifold approximation and projection for dimension reduction." arXiv preprint arXiv:1802.03426 (2018).Google Scholar
- Rogovschi, Nicoleta, "t-Distributed Stochastic Neighbor Embedding Spectral Clustering using higher order approximations." Aust. J. Intell. Inf. Process. Syst. 17.1 (2019): 78-86.Google Scholar
- Abdi, Hervé, and Lynne J. Williams. "Principal component analysis." Wiley interdisciplinary reviews: computational statistics 2.4 (2010): 433-459.Google Scholar
- Senbabaoğlu, Y., G. Michailidis, and J.Z. Li, Critical limitations of consensus clustering in class discovery. Scientific reports, 2014. 4(1): p. 1-13.Google Scholar
- Tang, Ming, "Evaluating single-cell cluster stability using the Jaccard similarity index." Bioinformatics 37.15 (2021): 2212-2214.Google Scholar
- Zhang, Shaohong, Hau-San Wong, and Ying Shen. "Generalized adjusted rand indices for cluster ensembles." Pattern Recognition 45.6 (2012): 2214-2226.Google Scholar
- Szumilas, M., Explaining odds ratios. Journal of the Canadian academy of child and adolescent psychiatry, 2010. 19(3): p. 227.Google Scholar
- Schober, Patrick, and Thomas R. Vetter. "Kaplan-meier curves, log-rank tests, and cox regression for time-to-event data." Anesthesia & Analgesia 132.4 (2021): 969-970.Google Scholar
- Kassambara, A., , Package ‘survminer’. Drawing Survival Curves using ‘ggplot2’.(R package version 0.3. 1.), 2017.Google Scholar
- Gamberger, D., , Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease. Scientific reports, 2017. 7(1): p. 1-12.Google Scholar
Index Terms
- Unsupervised discovery of Mild Cognitive Impairment subtypes of Alzheimer's disease using consensus clustering and unsupervised learning techniques
Recommendations
Spatio-temporal convolution for classification of alzheimer disease and mild cognitive impairment
Highlights- (2+1)D Convolutional Neural Network method was proposed to distinguish Mild Cognitive Impairment from Alzheimer Disease.
- Compared to 3D CNN methods (2+1)D provided better accuracy with an almost two times faster performance.
Abstract Background and objectiveDementia refers to the loss of memory and other cognitive abilities. Alzheimer's disease (AD), which patients eventually die from, is the most common cause of dementia. In USA, %60 to %80 of dementia cases, are caused by ...
Prospective classification of Alzheimer’s disease conversion from mild cognitive impairment
AbstractAlzheimer’s disease (AD) is emerging as a serious problem with the rapid aging of the population, but due to the unclear cause of the disease and the absence of therapy, appropriate preventive measures are the next best thing. For this ...
Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects
AbstractAlzheimer's Disease (AD) is the most common type of dementia. Predicting the conversion to Alzheimer's from the mild cognitive impairment (MCI) stage is a complex problem that has been studied extensively. This study centers on ...
Highlights- Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's Disease (AD).
Comments