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Hierarchical Structured Sparse Learning for Schizophrenia Identification

  • Mingliang Wang
  • Xiaoke Hao
  • Jiashuang Huang
  • Kangcheng Wang
  • Li Shen
  • Xijia XuEmail author
  • Daoqiang ZhangEmail author
  • Mingxia Liu
Original Article
  • 126 Downloads

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.

Keywords

Schizophrenia Fractional amplitude of low-frequency fluctuations (fALFF) Resting-state functional magnetic resonance imaging (rs-fMRI) Hierarchical feature selection 

Notes

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).

Compliance with Ethical Standards

Conflict of interests

The authors declare no conflict of interest.

Supplementary material

12021_2019_9423_MOESM1_ESM.pdf (212 kb)
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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjingChina
  2. 2.The State Key Laboratory of Integrated Services NetworksXidian UniversityXi’anChina
  3. 3.Department of PsychologySouthwest UniversityChongqingChina
  4. 4.Department of Biostatistics, Epidemiology and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUSA
  5. 5.Department of PsychiatryAffiliated Nanjing Brain Hospital, Nanjing Medical UniversityNanjingChina
  6. 6.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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