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Hierarchical Structured Sparse Learning for Schizophrenia Identification

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Abstract

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (Nos. 2016YFC1306900, 2018YFC2001602), the National Natural Science Foundation of China (Nos. 81771444, 61876082, 61861130366, 61703301), the Royal Society-Academy of Medical Sciences Newton Advanced Fellowship (No. NAF∖R1∖180371), and the Fundamental Research Funds for the Central Universities (No. NP2018104).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China

    Mingliang Wang, Xiaoke Hao, Jiashuang Huang & Daoqiang Zhang

  2. The State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, Shaanxi, China

    Mingliang Wang

  3. Department of Psychology, Southwest University, Chongqing, China

    Kangcheng Wang

  4. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Li Shen

  5. Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China

    Xijia Xu

  6. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Mingxia Liu

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  1. Mingliang Wang
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  2. Xiaoke Hao
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Wang, M., Hao, X., Huang, J. et al. Hierarchical Structured Sparse Learning for Schizophrenia Identification. Neuroinform 18, 43–57 (2020). https://doi.org/10.1007/s12021-019-09423-0

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