Neuroinformatics

, Volume 15, Issue 3, pp 271–284 | Cite as

Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification

  • Xiaobo Chen
  • Han Zhang
  • Seong-Whan Lee
  • Dinggang Shen
  • the Alzheimer’s Disease Neuroimaging Initiative
Article

Abstract

Conventional Functional connectivity (FC) analysis focuses on characterizing the correlation between two brain regions, whereas the high-order FC can model the correlation between two brain region pairs. To reduce the number of brain region pairs, clustering is applied to group all the brain region pairs into a small number of clusters. Then, a high-order FC network can be constructed based on the clustering result. By varying the number of clusters, multiple high-order FC networks can be generated and the one with the best overall performance can be finally selected. However, the important information contained in other networks may be simply discarded. To address this issue, in this paper, we propose to make full use of the information contained in all high-order FC networks. First, an agglomerative hierarchical clustering technique is applied such that the clustering result in one layer always depends on the previous layer, thus making the high-order FC networks in the two consecutive layers highly correlated. As a result, the features extracted from high-order FC network in each layer can be decomposed into two parts (blocks), i.e., one is redundant while the other might be informative or complementary, with respect to its previous layer. Then, a selective feature fusion method, which combines sequential forward selection and sparse regression, is developed to select a feature set from those informative feature blocks for classification. Experimental results confirm that our novel method outperforms the best single high-order FC network in diagnosis of mild cognitive impairment (MCI) subjects.

Keywords

Functional connectivity High-order network Hierarchical clustering Brain disease diagnosis 

References

  1. Anderson, A., & Cohen, M. S. (2013). Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: An fMRI classification tutorial. Frontiers in Human Neuroscience, 7, 520. doi:10.3389/fnhum.PubMedPubMedCentralGoogle Scholar
  2. Bain, L.J., Jedrziewski, K., Morrison-Bogorad, M., Albert, M., Cotman, C., Hendrie, H., Trojanowski, J.Q. (2008) Healthy brain aging: A meeting report from the sylvan M. Cohen annual retreat of the University of Pennsylvania Institute on aging. Alzheimer's & dementia: the journal of the Alzheimer's Association, 4:443.Google Scholar
  3. Brier, M. R., Thomas, J. B., Fagan, A. M., Hassenstab, J., Holtzman, D. M., Benzinger, T. L., Morris, J. C., & Ances, B. M. (2014). Functional connectivity and graph theory in preclinical Alzheimer's disease. Neurobiology of Aging, 35, 757–768.CrossRefPubMedGoogle Scholar
  4. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., & Arrighi, H. M. (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement, 3, 186–191.CrossRefPubMedGoogle Scholar
  5. Chang, C., Lin, C. (2001) LIBSVM: A library for support vector machines. Citeseer.Google Scholar
  6. Chen, X., Yang, J., Ye, Q., & Liang, J. (2011a). Recursive projection twin support vector machine via within-class variance minimization. Pattern Recognition, 44(10–11), 2643–2655.CrossRefGoogle Scholar
  7. Chen, X., Yang, J., & Liang, J. (2011b). Optimal Locality Regularized Least Squares Support Vector Machine via Alternating Optimization. Neural Processing Letters, 33, 301–315.CrossRefGoogle Scholar
  8. Chen, X., Xiao, Y., Cai, Y., & Chen, L. (2014). Structural max-margin discriminant analysis for feature extraction. Knowledge-Based Systems, 70, 154–166.CrossRefGoogle Scholar
  9. Chen, X., Zhang, H., Gao, Y., Wee, C.-Y., Li, G., & Shen, D. (2016). High-order resting-state functional connectivity network for MCI classification. Human Brain Mapping, 37(9), 3282–3296.CrossRefPubMedGoogle Scholar
  10. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.Google Scholar
  11. Fekete, T., Wilf, M., Rubin, D., Edelman, S., Malach, R., & Mujica-Parodi, L. R. (2013). Combining classification with fMRI-derived complex network measures for potential neurodiagnostics. PloS One, 8(5), e62867.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews. Neuroscience, 8, 700–711.CrossRefPubMedGoogle Scholar
  13. Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432–441.CrossRefPubMedGoogle Scholar
  14. Friston, K., Frith, C., Liddle, P., & Frackowiak, R. (1993). Functional connectivity: The principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow and Metabolism, 13, 5–5.CrossRefPubMedGoogle Scholar
  15. Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., Belleville, S., Brodaty, H., Bennett, D., & Chertkow, H. (2006). Mild cognitive impairment. Lancet, 367, 1262–1270.CrossRefPubMedGoogle Scholar
  16. Greicius, M. (2008). Resting-state functional connectivity in neuropsychiatric disorders. Current Opinion in Neurology, 21, 424–430.CrossRefPubMedGoogle Scholar
  17. Huang, S., Li, J., Sun, L., Ye, J., Fleisher, A., Wu, T., Chen, K., Reiman, E., & Initiative, A.s.D.N. (2010). Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. NeuroImage, 50, 935–949.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Jain, A., & Zongker, D. (1997). Feature selection: Evaluation, application, and small sample performance. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19, 153–158.CrossRefGoogle Scholar
  19. Jie, B., Shen, D., Zhang, D. (2014a) Brain connectivity hyper-network for MCI classification. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: Springer. P 724-732.Google Scholar
  20. Jie, B., Zhang, D., Gao, W., Wang, Q., Wee, C.-Y., & Shen, D. (2014b). Integration of network topological and connectivity properties for neuroimaging classification. Biomedical Engineering, IEEE Transactions on, 61, 576–589.CrossRefGoogle Scholar
  21. Johnson, S., Schmitz, T., Moritz, C., Meyerand, M., Rowley, H., Alexander, A., Hansen, K., Gleason, C., Carlsson, C., & Ries, M. (2006). Activation of brain regions vulnerable to Alzheimer's disease: The effect of mild cognitive impairment. Neurobiology of Aging, 27, 1604–1612.CrossRefPubMedGoogle Scholar
  22. Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97, 273–324.CrossRefGoogle Scholar
  23. Liu, J., Ji, S., & Ye, J. (2009). SLEP: Sparse learning with efficient projections. Arizona State University, 6, 491.Google Scholar
  24. Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., & Fulham, M. J. (2015). Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. Biomedical Engineering, IEEE Transactions on, 62, 1132–1140.CrossRefGoogle Scholar
  25. McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). 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, 34, 939–939.CrossRefPubMedGoogle Scholar
  26. Misra, C., Fan, Y., & Davatzikos, C. (2009). Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI. NeuroImage, 44, 1415–1422.CrossRefPubMedGoogle Scholar
  27. Mitchell, T.M. (1997) Machine learning. McGraw-Hill New York.Google Scholar
  28. Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001). Current concepts in mild cognitive impairment. Archives of Neurology, 58, 1985–1992.CrossRefPubMedGoogle Scholar
  29. Rombouts, S. A., Barkhof, F., Goekoop, R., Stam, C. J., & Scheltens, P. (2005). Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study. Human Brain Mapping, 26, 231–239.CrossRefPubMedGoogle Scholar
  30. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059–1069.CrossRefPubMedGoogle Scholar
  31. dos Santos Siqueira, A., Biazoli Junior, C.E., Comfort, W.E., Rohde, L.A., & Sato, J.R. (2014). Abnormal functional resting-state networks in ADHD: Graph theory and pattern recognition analysis of fMRI data. BioMed Research International.Google Scholar
  32. Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006) Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. AI 2006: Advances in Artificial Intelligence. Springer p 1015–1021.Google Scholar
  33. Sorg, C., Riedl, V., Mühlau, M., Calhoun, V. D., Eichele, T., Läer, L., Drzezga, A., Förstl, H., Kurz, A., & Zimmer, C. (2007). Selective changes of resting-state networks in individuals at risk for Alzheimer's disease. Proceedings of the National Academy of Sciences, 104, 18760–18765.CrossRefGoogle Scholar
  34. Stam, C., Jones, B., Nolte, G., Breakspear, M., & Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer's disease. Cerebral Cortex, 17, 92–99.CrossRefPubMedGoogle Scholar
  35. Stam, C., De Haan, W., Daffertshofer, A., Jones, B., Manshanden, I., Van Walsum, A. V. C., Montez, T., Verbunt, J., De Munck, J., & Van Dijk, B. (2009). Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain, 132, 213–224.CrossRefPubMedGoogle Scholar
  36. Suk, H.-I., Lee, S.-W., Shen, D., & Initiative, A.s.D.N. (2013). Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Structure & Function, 220, 841–859.CrossRefGoogle Scholar
  37. Suk, H.-I., Lee, S.-W., Shen, D., & Initiative, A.D.N. (2014a) Subclass-based multi-task learning for Alzheimer's disease diagnosis Frontiers in Aging Neuroscience, 6.Google Scholar
  38. Suk, H.-I., Lee, S.-W., Shen, D., & Initiative, A.s.D.N. (2014b). Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage, 101, 569–582.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Suk, H.-I., Wee, C.-Y., Lee, S.-W., & Shen, D. (2014c) Supervised discriminative group sparse representation for mild cognitive impairment diagnosis. Neuroinformatics, 9349, 1-19.Google Scholar
  40. Suk, H.-I., Lee, S.-W., & Shen, D. (2015) A hybrid of deep network and hidden Markov model for MCI identification with resting-state fMRI. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015. Springer. p 573-580.Google Scholar
  41. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B: Methodological, 58, 267–288.Google Scholar
  42. Toussaint, P.-J., Maiz, S., Coynel, D., Doyon, J., Messé, A., de Souza, L. C., Sarazin, M., Perlbarg, V., Habert, M.-O., & Benali, H. (2014). Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements. NeuroImage, 101, 778–786.CrossRefPubMedGoogle Scholar
  43. Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007). Altered functional connectivity in early Alzheimer's disease: A resting-state fMRI study. Human Brain Mapping, 28, 967–978.CrossRefPubMedGoogle Scholar
  44. Ward Jr., J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.CrossRefGoogle Scholar
  45. Watts, D.J., Strogatz, S.H. (1998) Collective dynamics of ‘small-world’networks. nature, 393:440-442.Google Scholar
  46. Wee, C.-Y., Yap, P.-T., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., Wang, L., & Shen, D. (2012). Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PloS One, 7, e37828.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Wee, C.-Y., Yang, S., Yap, P.-T., & Shen, D. (2013) Temporally dynamic resting-state functional connectivity networks for early MCI identification. Machine Learning in Medical Imaging: Springer p 139–146.Google Scholar
  48. Wee, C.-Y., Yap, P.-T., Zhang, D., Wang, L., & Shen, D. (2014). Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Structure & Function, 219(2), 641–656.CrossRefGoogle Scholar
  49. Wee, C.-Y., Yang, S., Yap, P.-T., Shen, D., & Initiative, A.s.D.N. (2015). Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging and Behavior, 10(2), 342–356.Google Scholar
  50. Whitwell, J. L., Przybelski, S. A., Weigand, S. D., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2007). 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease. Brain, 130, 1777–1786.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009). Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31, 210–227.CrossRefGoogle Scholar
  52. Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., Shen, D. (2016). Correlation-weighted sparse group representation for brain network construction in MCI classification. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016. Springer. P 37-45.Google Scholar
  53. Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D., & Initiative, A.s.D.N. (2011). Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55, 856–867.CrossRefPubMedPubMedCentralGoogle Scholar
  54. Zhang, H., Chen X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., Haller, S., Shen, D. (2016a). Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment. Journal of Alzheimers Disease, 54, 1095–1112.Google Scholar
  55. Zhang, Y., Zhou, G., Jin, J., Zhao, Q., Wang, X., & Cichocki, A. (2016b). Sparse Bayesian classification of EEG for brain-Computer Interface. IEEE Transactions on Neural Networks and Learning Systems, 27(11), 2256–2267.Google Scholar
  56. Zhang, Y., Wang, Y., Jin, J., & Wang, X. (2017). Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. International Journal of Neural Systems, 27, 1650032.CrossRefPubMedGoogle Scholar
  57. Zhou, L., Wang, Y., Li, Y., Yap, P., & Shen, D. (2011). Hierarchical anatomical brain networks for MCI prediction: Revisiting volumetric measures. PloS One, 6(7), e21935.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

Personalised recommendations