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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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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