Abstract
Functional connectivity network, which as a simplified representation of functional interactions, it has been widely used for diseases diagnosis and classification, especially for Alzheimer’s disease (AD). Although, many methods for functional connectivity network construction have been developed, these methods rarely adopt anatomical prior knowledge while constructing functional brain networks. However, in the neuroscience field, it is widely believed that brain anatomy structure determining brain function. Thus, integrating anatomical structure information into functional brain network representation is significant for disease diagnosis. Furthermore, ignoring the prior knowledge may lose some useful neuroscience information that is important to interpret the data, and lose information could be important for disease diagnosis. In this paper, we propose a novel framework for constructing the functional connectivity network for AD classification and functional connectivity analysis. The experimental results demonstrate the proposed method not only improves the classification performance, but also found alteration functional connectivity.
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References
Mueller, S.G., et al.: Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s Dement. 1, 55–66 (2005)
Pievani, M., et al.: Functional networks connectivity in patients with Alzheimer’s disease and mild cognitive impairment. J. Neurol. 258, S170 (2011)
Wang, J., He, Y., et al.: Disrupted functional brain connectome in individuals at risk for Alzheimer’s disease. Biol. Psychiatry 73, 472–481 (2013)
Wee, C.-Y., Yap, P.T., Shen, D., et al.: Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification. Brain Struct. Funct. 219, 641 (2014)
Huang, S., Li, J., Ye, J., et al.: Learning brain connectivity of Alzheimer’s disease from neuroimaging data. In: Advances in Neural Information Processing Systems, pp. 808–816 (2009)
Lee, H., Lee, D.S., Chung, M.K., et al.: Sparse brain network recovery under compressed sensing. IEEE Trans. Med. Imaging 30, 1154–1165 (2011)
Smith, S.M., et al.: Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013)
Jenkinson, M., Smith, S.M., et al.: Fsl. Neuroimage 62, 782–790 (2012)
Liu, J., Ye, J.: Efficient L1/Lq norm regularization. ar**v preprint ar**v:1009.4766 (2010)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011)
Peng, J., Wang, P., Zhou, N., Zhu, J.: Partial correlation estimation by joint sparse regression models. J. Am. Stat. Assoc. 104, 735–746 (2009)
Tzourio-Mazoyer, N., Landeau, B., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2008)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2012)
Huang, S., Li, J., Sun, L., Ye, J., et al.: Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation. Neuroimage 50, 935–949 (2010)
Qiao, L., Shen, D., et al.: Estimating functional brain networks by incorporating a modularity prior. NeuroImage 141, 399–407 (2016)
Acknowledgments
This work is partly supported by the 111 Project (No. B13022). J Lu was supported by the 111 Project (No. B13022) and the Natural Science Foundation of Jiangsu Province of China under Grant (No. 20131351).
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Zhao, Q., Ali, Z., Lu, J., Metmer, H. (2019). Structure Feature Learning: Constructing Functional Connectivity Network for Alzheimer’s Disease Identification and Analysis. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_12
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DOI: https://doi.org/10.1007/978-3-030-31456-9_12
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