Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

Abstract

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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References

  1. 1.

    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

    Article  Google Scholar 

  2. 2.

    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

    Article  Google Scholar 

  3. 3.

    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

    Article  Google Scholar 

  4. 4.

    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

    Article  Google Scholar 

  5. 5.

    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

    Article  Google Scholar 

  6. 6.

    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

    Article  Google Scholar 

  7. 7.

    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

    Article  Google Scholar 

  8. 8.

    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

    Article  Google Scholar 

  9. 9.

    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

    Article  Google Scholar 

  10. 10.

    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

    Article  Google Scholar 

  11. 11.

    Fei F, Jie B, Zhang D. Frequent and discriminative subnetwork mining for mild cognitive impairment classification. Brain Connectivity, 2014, 4(5): 347–360

    Article  Google Scholar 

  12. 12.

    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

    Article  Google Scholar 

  13. 13.

    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

    Article  Google Scholar 

  14. 14.

    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

    Article  Google Scholar 

  15. 15.

    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

    Article  Google Scholar 

  16. 16.

    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

    Article  Google Scholar 

  17. 17.

    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

    Article  Google Scholar 

  18. 18.

    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

    MathSciNet  Article  Google Scholar 

  19. 19.

    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

    Article  Google Scholar 

  20. 20.

    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

    Article  Google Scholar 

  21. 21.

    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

    Article  Google Scholar 

  22. 22.

    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

    Article  Google Scholar 

  23. 23.

    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

    Article  Google Scholar 

  24. 24.

    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

    Article  Google Scholar 

  25. 25.

    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

    Article  Google Scholar 

  26. 26.

    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

    Article  Google Scholar 

  27. 27.

    Chen X, Zhu C C, Yin J. Ensemble of decision tree reveals potential mirna-disease associations. PLoS Computational Biology, 2019, 15(7): e1007209

    Article  Google Scholar 

  28. 28.

    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

    Article  Google Scholar 

  29. 29.

    Cui H, Zhang X. Alignment-free supervised classification of metagenomes by recursive SVM. BMC Genomics, 2013, 14: 641

    Article  Google Scholar 

  30. 30.

    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

    Article  Google Scholar 

  31. 31.

    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

    Article  Google Scholar 

  32. 32.

    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

    Article  Google Scholar 

  33. 33.

    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

    Article  Google Scholar 

  34. 34.

    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

    Article  Google Scholar 

  35. 35.

    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

    Article  Google Scholar 

  36. 36.

    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

    Article  Google Scholar 

  37. 37.

    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

    Article  Google Scholar 

  38. 38.

    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

    Article  Google Scholar 

  39. 39.

    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

    Article  Google Scholar 

  40. 40.

    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

    Article  Google Scholar 

  41. 41.

    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

    Article  Google Scholar 

  42. 42.

    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

    Article  Google Scholar 

  43. 43.

    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

    Google Scholar 

  44. 44.

    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

    Article  Google Scholar 

  45. 45.

    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

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Hunan Provincial Science and Technology Project Foundation (2018TP1018), the National Science Foundation of China (61502167).

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Correspondence to Xia-an Bi or Luyun Xu.

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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

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Keywords

  • machine learning
  • MCI progression
  • optimal feature extraction
  • differential brain regions
  • functional magnetic resonance imaging