Mild cognitive impairment (MCI) as the potential sign of serious cognitive decline could be divided into two stages, i.e., late MCI (LMCI) and early MCI (EMCI). Although the different cognitive states in the MCI progression have been clinically defined, effective and accurate identification of differences in neuroimaging data between these stages still needs to be further studied. In this paper, a new method of clustering-evolutionary weighted support vector machine ensemble (CEWSVME) is presented to investigate the alterations from cognitively normal (CN) to EMCI to LMCI. The CEWSVME mainly includes two steps. The first step is to build multiple SVM classifiers by randomly selecting samples and features. The second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs, thereby improving the final classification performances. Additionally, we extracted the optimal features to detect the differential brain regions in MCI progression, and confirmed that these differential brain regions changed dynamically with the development of MCI. More exactly, this study found that some brain regions only have durative effects on MCI progression, such as parahippocampal gyrus, posterior cingulate gyrus and amygdala, while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the progression. Our work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.
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Sherman D S, Mauser J, Nuno M, Sherzai D. The efficacy of cognitive intervention in mild cognitive impairment (MCI): a meta-analysis of outcomes on neuropsychological measures. Neuropsychology Review, 2017, 27(4): 440–484
Li J Q, Tan L, Wang H F, Tan M S, Tan L, Xu W, Zhao Q F, Wang J, Jiang T, Yu J T. Risk factors for predicting progression from mild cognitive impairment to alzheimer’s disease: a systematic review and meta-analysis of cohort studies. Journal of Neurology, Neurosurgery & Psychiatry, 2016, 87(5): 476–484
Yi H A, Möller C, Dieleman N, Bouwman F H, Barkhof F, Scheltens P, van der Flier W M, Vrenken H. Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to alzheimer’s disease. Journal of Neurology, Neurosurgery & Psychiatry, 2016, 87(4): 425–432
Ramírez J, Górriz J M, Ortiz A, Martínez-Murcia F J, Segovia F, Salas-Gonzalez D, Castillo-Barnes D, Illán I A, Puntonet C G. Ensemble of random forests one vs. rest classifiers for MCI and ad prediction using anova cortical and subcortical feature selection and partial least squares. Journal of Neuroscience Methods, 2018, 302: 47–57
ten Brinke L F, Bolandzadeh N, Nagamatsu L S, Hsu C L, Davis J C, Miran-Khan K, Liu-Ambrose T. Aerobic exercise increases hippocampal volume in older women with probable mild cognitive impairment: a 6-month randomised controlled trial. British Journal of Sports Medicine, 2015, 49(4): 248–254
Spulber G, Simmons A, Muehlboeck J S, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Spenger C, Lovestone S, Wahlund L O, Westman E, et al. An MRI-based index to measure the severity of alzheimer’s disease-like structural pattern in subjects with mild cognitive impairment. Journal of Internal Medicine, 2013, 273(4): 396–409
Mecca A P, Michalak H R, McDonald J W, Kemp E C, Pugh E A, Becker M L, Mecca M C, van Dyck C H. Sleep disturbance and the risk of cognitive decline or clinical conversion in the adni cohort. Dementia and Geriatric Cognitive Disorders, 2018, 45(3–4): 232–242
Jagust W J, Landau S M, Koeppe R A, Reiman E M, Chen K, Mathis C A, Price J C, Foster N L, Wang A Y. The alzheimer’s disease neuroimaging initiative 2 pet core: 2015. Alzheimer’s & Dementia, 2015, 11(7): 757–771
Lee E S, Yoo K, Lee Y B, Chung J, Lim J E, Yoon B, Jeong Y. Default mode network functional connectivity in early and late mild cognitive impairment. Alzheimer Disease & Associated Disorders, 2016, 30(4): 289–296
Cai S, Chong T, Peng Y, Shen W, Li J, von Deneen K M, Huang L. Altered functional brain networks in amnestic mild cognitive impairment: a resting-state fMRI study. Brain Imaging and Behavior, 2017, 11(3): 619–631
Fei F, Jie B, Zhang D. Frequent and discriminative subnetwork mining for mild cognitive impairment classification. Brain Connectivity, 2014, 4(5): 347–360
Bi X A, Xu Q, Luo X, Sun Q, Wang Z. Weighted random support vector machine clusters analysis of resting-state fMRI in mild cognitive impairment. Frontiers in Psychiatry, 2018, 9: 340
McKenna F, Koo B B, Killiany R, et al. Comparison of apoe-related brain connectivity differences in early MCI and normal aging populations: an fMRI study. Brain Imaging and Behavior, 2016, 10(4): 970–983
Wee C Y, Yang S, Yap P T, Shen D, et al. Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging and Behavior, 2016, 10(2): 342–356
Jie B, Liu M, Shen D. Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease. Medical Image Analysis, 2018, 47: 81–94
Grajski K A, Bressler S L. Differential medial temporal lobe and default-mode network functional connectivity and morphometric changes in alzheimer’s disease. NeuroImage: Clinical, 2019, 23: 101860
Daianu M, Jahanshad N, Nir T M, Jack Jr C R, Weiner M W, Bernstein M A, Thompson P M, et al. Rich club analysis in the alzheimer’s disease connectome reveals a relatively undisturbed structural core network. Human Brain Mapping, 2015, 36(8): 3087–3103
Jie B, Liu M, Zhang D, Shen D. Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis. IEEE Transactions on Image Processing, 2018, 27(5): 2340–2353
Ding X, Charnigo R J, Schmitt F A, Kryscio R J, Abner E L, et al. Evaluating trajectories of episodic memory in normal cognition and mild cognitive impairment: results from adni. PLoS ONE, 2019, 14(2): e0212435
Schetinin V, Jakaite L, Nyah N, Novakovic D, Krzanowski W. Feature extraction with gmdh-type neural networks for eeg-based person identification. International Journal of Neural Systems, 2017, 28(6): 1750064
Du L, Liu K, Zhu L, Yao X, Risacher S L, Guo L, Saykin A J, Shen L, et al. Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the adni cohort. Bioinformatics (Oxford, England), 2019, 35(14): i474–i483
Yan K, Xu Y, Fang X, Zheng C, Liu B. Protein fold recognition based on sparse representation based classification. Artificial Intelligence in Medicine, 2017, 79: 1–8
Wu D, Zheng S J, Zhang X P, Yuan C A, Cheng F, Zhao Y, Lin Y J, Zhao Z Q, Jiang Y L, Huang D S. Deep learning-based methods for person re-identification: a comprehensive review. Neurocomputing, 2019, 337: 354–371
Jin Q, Meng Z, Pham T D, Chen Q, Wei L, Su R. Dunet: a deformable network for retinal vessel segmentation. Knowledge-Based Systems, 2019, 178: 149–162
Su R, Liu X, Wei L, Zou Q. Deep-resp-forest: a deep forest model to predict anti-cancer drug response. Methods, 2019, 166: 91–102
Zeng X, Yuan S, Huang X, Zou Q. Identification of cytokine via an improved genetic algorithm. Frontiers of Computer Science, 2015, 9(4): 643–651
Chen X, Zhu C C, Yin J. Ensemble of decision tree reveals potential mirna-disease associations. PLoS Computational Biology, 2019, 15(7): e1007209
Peng J, Hui W, Li Q, Chen B, Hao J, Jiang Q, Shang X, Wei Z. A learning-based framework for mirna-disease association identification using neural networks. Bioinformatics (Oxford, England), 2019, 35(21): 4364–4371
Cui H, Zhang X. Alignment-free supervised classification of metagenomes by recursive SVM. BMC Genomics, 2013, 14: 641
Prasad G, Joshi S H, Nir T M, Toga A W, Thompson P M. Brain connectivity and novel network measures for alzheimer’s disease classification. Neurobiology of Aging, 2015, 36: S121–S131
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and alzheimer’s disease. Brain Imaging and Behavior, 2016, 10(3): 799–817
Echávarri C, Aalten P, Uylings H B M, Jacobs H I L, Visser P J, Gronenschild E H B M, Verhey F R J, Burgmans S. Atrophy in the parahippocampal gyrus as an early biomarker of alzheimer’s disease. Brain Structure and Function, 2011, 215(3): 265–271
Chao L L, Mueller S G, Buckley S T, Peek K, Raptentsetseng S, Elman J, Yaffe K, Miller B L, Kramer J H, Madison C, Mungas D, Schuff N, Weiner M W. Evidence of neurodegeneration in brains of older adults who do not yet fulfill MCI criteria. Neurobiology of Aging, 2010, 31(3): 368–377
Kim S M, Kim M J, Rhee H Y, Ryu C W, Kim E J, Petersen E T, Jahng G H. Regional cerebral perfusion in patients with alzheimer’s disease and mild cognitive impairment: effect of apoe epsilon4 allele. Neuroradiology, 2013, 55(1): 25–34
Ward A M, Schultz A P, Huijbers W, Van Dijk K R A, Hedden T, Sperling R A. The parahippocampal gyrus links the default-mode cortical network with the medial temporal lobe memory system. Human Brain Mapping, 2014, 35(3): 1061–1073
Luck D, Danion J M, Marrer C, Pham B T, Gounot D, Foucher J. The right parahippocampal gyrus contributes to the formation and maintenance of bound information in working memory. Brain and Cognition, 2010, 72(2): 255–263
Browndyke J N, Giovanello K, Petrella J, Hayden K, Chiba-Falek O, Tucker K A, Burke J R, Welsh-Bohmer K A. Phenotypic regional functional imaging patterns during memory encoding in mild cognitive impairment and alzheimer’s disease. Alzheimer’s & Dementia, 2013, 9(3): 284–294
Kantarci K, Jack C R, Xu Y C, Campeau N G, O’Brien P C, Smith G E, Ivnik R J, Boeve B F, Kokmen E, Tangalos E G, Petersen R C. Regional metabolic patterns in mild cognitive impairment and alzheimer’s disease: a 1h mrs study. Neurology, 2000, 55(2): 210–217
Camus V, Payoux P, Barré L, Desgranges B, Voisin T, Tauber C, La Joie R, Tafani M, Hommet C, Chételat G, Mondon K, de La Sayette V, Cottier J P, Beaufils E, Ribeiro M J, Gissot V, Vierron E, Vercouillie J, Vellas B, Eustache F, Guilloteau D. Using pet with 18f-av-45 (florbetapir) to quantify brain amyloid load in a clinical environment. European Journal of Nuclear Medicine and Molecular Imaging, 2012, 39(4): 621–631
Bailly M, Destrieux C, Hommet C, Mondon K, Cottier J P, Beaufils E, Vierron E, Vercouillie J, Ibazizene M, Voisin T, Payoux P, Barré L, Camus V, Guilloteau D, Ribeiro M J. Precuneus and cingulate cortex atrophy and hypometabolism in patients with alzheimer&’s disease and mild cognitive impairment: MRI and 18f-fdg pet quantitative analysis using freesurfer. BioMed Research International, 2015, 2015: 583931
Cai S, Huang L, Zou J, Jing L, Zhai B, Ji G, von Deneen K M, Ren J, Ren A, et al. Changes in thalamic connectivity in the early and late stages of amnestic mild cognitive impairment: a resting-state functional magnetic resonance study from ADNI. PLoS ONE, 2015, 10(2): e0115573
Li H, Fang S, Contreras J A, West J D, Risacher S L, Wang Y, Sporns O, Saykin A J, Goñi J, Shen L, et al. Brain explorer for connectomic analysis. Brain Informatics, 2017, 4(4): 253–269
Xiang J, Guo H, Cao R, Liang H, Chen J. An abnormal resting-state functional brain network indicates progression towards alzheimer’s disease. Neural Regeneration Research, 2013, 8(30): 2789–2799
Wei H, Kong M, Zhang C, Guan L, Ba M, et al. The structural MRI markers and cognitive decline in prodromal alzheimer’s disease: a 2-year longitudinal study. Quantitative Imaging in Medicine and Surgery, 2018, 8(10): 1004–1019
Ribeiro A S, Lacerda L M, Silva N A D, Ferreira H A. Multimodal imaging of brain connectivity using the mibca toolbox: preliminary application to alzheimer’s disease. IEEE Transactions on Nuclear Science, 2015, 62(3): 604–611
This work was supported by the Hunan Provincial Science and Technology Project Foundation (2018TP1018), the National Science Foundation of China (61502167).
Xia-an Bi is currently an associate professor in the College of Information Science and Engineering in Hunan Normal University, China. He received the PhD degree in computer science and technology from the College of Information Science and Engineering in Hunan University, China in 2012. His current research interests include machine learning, brain science and artificial intelligence.
Yiming Xie, received the BE degree in Network Engineering from Chengdu Technological University, China in 2019. He is currently pursuing the MS degree in College of Information Science and Engineering, Hunan Normal University, China. The major of him is computer science and technology. His main research fields include data mining, brain science and artificial intelligence.
Hao Wu, received the BE degree in Computer Science and Technology from Jinggangshan University, China in 2019. He is currently pursuing the MS degree in College of Information Science and Engineering, Hunan Normal University, China. The major of him is computer technology. His main research fields include data mining, brain science and artificial intelligence.
Luyun Xu, received the PhD degree in business administration from Hunan University, China in 2018. She is currently an assistant professor in Business School in Hunan Normal University, China. Her research interests focus on knowledge management, data mining and machine learning. She has published in the Journal of Technology Transfer, Technological Analysis & Strategic Management, Computational and Mathematical Organization Theory.
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Bi, Xa., Xie, Y., Wu, H. et al. Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm. Front. Comput. Sci. 15, 156903 (2021). https://doi.org/10.1007/s11704-020-9520-3
- machine learning
- MCI progression
- optimal feature extraction
- differential brain regions
- functional magnetic resonance imaging