Abstract
Brain functional network (BFN) analysis based on functional magnetic resonance imaging (fMRI) has proven to be a value method for revealing organization architectures in normal aging brains. However, a comprehensive comparison of different BFN methods for predicting brain age remains lacking. In this paper, we introduce a novel method to establish the BFN by using the Schatten-0 (\( S_0 \)) and \( \ell _0 \)-regularized low rank sparse representation (\({S_0}{{/}}{\ell _{{0}}}\) LSR) method. Moreover, the performance of different BFN methods in the brain age prediction with different feature extraction methods is evaluated. A support vector regression (SVR) is applied to the BFN data to predict brain age. Experimental results for resting state fMRI data sets show that compared with the Pearson correlation (PC), sparse representation (SR), low rank representation (LR), and low rank sparse representation (LSR) methods, the LSR method can achieve better modularity and predict brain age more accurately. The novel approach can enhance our understanding of the functional network of the aging brain.
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This work was supported in part by the National Nature Science Foundation of China under Grant 61773114 and the Key Research and Development Plan (Industry Foresight and Common Key Technology) of Jiangsu Province under Grant BE2017007-3.
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Han, H., Xiong, X., Yan, J., Wang, H., Wei, M. (2020). The Evaluation of Brain Age Prediction by Different Functional Brain Network Construction Methods. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_11
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