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


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.


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



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)
(PDF 212 KB)


  1. Arribas, J.I., Calhoun, V.D., Adalı, T. (2010). Automatic bayesian classification of healthy controls, bipolar disorder and schizophrenia using intrinsic connectivity maps from fMRI data. IEEE Transactions on Biomedical Engineering, 57(12), 2850–2860.PubMedCrossRefGoogle Scholar
  2. Bassett, D.S., Nelson, B.G., Mueller, B.A., Camchong, J., Lim, K.O. (2012). Altered resting state complexity in schizophrenia. NeuroImage, 59(3), 2196–2207.PubMedCrossRefGoogle Scholar
  3. Besga, A., Termenon, M., Graña, M., Echeveste, J., Pérez, J.M., Gonzalezpinto, A. (2012). Discovering Alzheimer’s disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neuroscience Letters, 520(1), 71–76.PubMedCrossRefGoogle Scholar
  4. Bhugra, D. (2005). The global prevalence of schizophrenia. Plos Medicine, 2(5), 372–373.CrossRefGoogle Scholar
  5. Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537–541.PubMedCrossRefGoogle Scholar
  6. Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304(5679), 1926–1929.PubMedCrossRefGoogle Scholar
  7. Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.CrossRefGoogle Scholar
  8. Chang, C.C., & Lin, C.J. (2011). LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol., 2(3), 389–396.CrossRefGoogle Scholar
  9. Chen, X., Lin, Q., Kim, S., Carbonell, J.G., Xing, E.P. (2011). Smoothing proximal gradient method for general structured sparse learning. In Twenty-seventh conference on uncertainty in artificial intelligence (pp. 105–114): ACM.Google Scholar
  10. Cheng, H., Newman, S., Goñi, J., Kent, J.S., Howell, J., Bolbecker, A., Puce, A., O’Donnell, B.F., Hetrick, W.P. (2015). Nodal centrality of functional network in the differentiation of schizophrenia. Schizophrenia Research, 168(2), 345–352.PubMedPubMedCentralCrossRefGoogle Scholar
  11. Cherkassky, V. (1997). The nature of statistical learning theory. IEEE Transactions on Neural Networks, 8 (6), 1564–1564.PubMedCrossRefGoogle Scholar
  12. Chyzhyk, D., Savio, A., Graña, M. (2015). Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Networks, 68, 23–33.PubMedCrossRefGoogle Scholar
  13. Cole, M.W., & Schneider, W. (2007). The cognitive control network: integrated cortical regions with dissociable functions. NeuroImage, 37(1), 343–360.PubMedCrossRefGoogle Scholar
  14. Demirci, O., & Calhoun, V.D. (2009). Functional magnetic resonance imaging-implications for detection of schizophrenia. European Neurological Review, 4(2), 103–106.PubMedPubMedCentralCrossRefGoogle Scholar
  15. Demirci, O., Clark, V.P., Magnotta, V.A., Andreasen, N.C., Lauriello, J., Kiehl, K.A., Pearlson, G.D., Calhoun, V.D. (2008). A review of challenges in the use of fMRI for disease classification/characterization and a projection pursuit application from multi-site fMRI schizophrenia study. Brain Imaging and Behavior, 2(3), 207–226.CrossRefGoogle Scholar
  16. Dietterich, T.G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895–1923.PubMedCrossRefGoogle Scholar
  17. Du, Y., Pearlson, G.D., Yu, Q., He, H., Lin, D., Jing, S., Wu, L., Calhoun, V.D. (2016). Interaction among subsystems within default mode network diminished in schizophrenia patients: a dynamic connectivity approach. Schizophrenia Research, 170(1), 55–65.PubMedCrossRefGoogle Scholar
  18. Duda, R.O., Hart, P.E., Stork, D.G. (2001). Pattern classification. USA: Wiley.Google Scholar
  19. Guo, W., Su, Q., Yao, D., Jiang, J., Zhang, J., Zhang, Z., Yu, L., Zhai, J., Xiao, C. (2014). Decreased regional activity of default-mode network in unaffected siblings of schizophrenia patients at rest. European Neuropsychopharmacology, 24(4), 545–552.PubMedCrossRefGoogle Scholar
  20. Guyon, I., Weston, J., Barnhill, S., Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1), 389–422.CrossRefGoogle Scholar
  21. Han, L., & Zhang, Y. (2015). Learning multi-level task groups in multi-task learning. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 2638–2644): AAAI.Google Scholar
  22. Hao, X., Yao, X., Yan, J., Risacher, S.L., Saykin, A.J., Zhang, D., Shen, L. (2016). Identifying multimodal intermediate phenotypes between genetic risk factors and disease status in Alzheimer’s disease. Neuroinformatics, 14(4), 439–452.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Hastie, T., Tibshirani, R., Friedman, J. (2001). The elements of statistical learning. New York: Springer.CrossRefGoogle Scholar
  24. Hoptman, M.J., Zuo, X.N., Butler, P.D., Javitt, D.C., D’Angelo, D., Mauro, C.J., Milham, M.P. (2010). Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study. Schizophrenia Research, 117(1), 13–20.PubMedCrossRefGoogle Scholar
  25. Huang, X.Q., Lui, S., Deng, W., Chan, R.C.K., Wu, Q.Z., Jiang, L.J., Zhang, J.R., Jia, Z.Y., Li, X.L., Li, F. (2010). Localization of cerebral functional deficits in treatment-naive, first-episode schizophrenia using resting-state fMRI. NeuroImage, 49(4), 2901–2906.PubMedCrossRefGoogle Scholar
  26. Jafri, M.J., & Calhoun, V.D. (2006). Functional classification of schizophrenia using feed forward neural networks. In 2006 International conference of the IEEE engineering in medicine and biology society (pp. 6631–6634).Google Scholar
  27. Jalali, A., Sanghavi, S., Ruan, C., Ravikumar, P.K. (2010). A dirty model for multi-task learning. In Advances in neural information processing systems 23. Curran Associates, Inc. (pp. 964–972).Google Scholar
  28. Jie, B., Liu, M., Liu, J., Zhang, D., Shen, D. (2017). Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer’s disease. IEEE Transactions on Biomedical Engineering, 64(1), 238–249.PubMedCrossRefGoogle Scholar
  29. Jie, B., Liu, M., Zhang, D., Shen, D. (2018). Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis. IEEE Transactions on Image Processing, 27(5), 2340–2353.PubMedPubMedCentralCrossRefGoogle Scholar
  30. Kaufmann, T., Skåtun, K.C., Alnæs, D., Doan, N.T., Duff, E.P., Tønnesen, S., Roussos, E., Ueland, T., Aminoff, S.R., Lagerberg, T.V. (2015). Disintegration of sensorimotor brain networks in schizophrenia. Schizophrenia Bulletin, 41(6), 1326–1335.PubMedPubMedCentralCrossRefGoogle Scholar
  31. Kim, J., Calhoun, V.D., Shim, E., Lee, J.H. (2015). Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. NeuroImage, 124, 1326–1335.Google Scholar
  32. Knyazev, G.G. (2007). Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neuroscience and Biobehavioral Reviews, 31(3), 377–95.PubMedCrossRefGoogle Scholar
  33. Lachowicz, M., & wrzosek, D. (2001). Nonlocal bilinear equations: equilibrium solutions and diffusive limit. Mathematical Models and Methods in Applied Sciences, 11(08), 1393–1409.CrossRefGoogle Scholar
  34. Lam, L., & Suen, S.Y. (1997). Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transactions on Systems Man and Cybernetics Part A Systems and Humans, 27(5), 553–568.CrossRefGoogle Scholar
  35. Lian, C., Liu, M., Zhang, J., Shen, D. (2019). Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  36. Liang, X., Wang, J., Yan, C., Shu, N., Xu, K., Gong, G., He, Y. (2012). Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study. Plos One, e32(3), 766.Google Scholar
  37. Liu, F., Wee, C.Y., Chen, H., Shen, D. (2014). Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s disease and mild cognitive impairment identification. NeuroImage, 84, 466–475.PubMedCrossRefGoogle Scholar
  38. Liu, J., Ji, S., Ye, J. (2009). Multi-task feature learning via efficient l2,1-norm minimization. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (pp. 339–348): AUAI Press.Google Scholar
  39. Liu, J., Ji, S., Ye, J. (2009). SLEP: sparse learning with efficient projections. Arizona State University.Google Scholar
  40. Liu, M., & Zhang, D. (2014). Sparsity score: a novel graph-preserving feature selection method. International Journal of Pattern Recognition and Artificial Intelligence, 1450(04), 009.Google Scholar
  41. Liu, M., & Zhang, D. (2016). Feature selection with effective distance. Neurocomputing, 215, 100–109.CrossRefGoogle Scholar
  42. Liu, M., Zhang, D., Adeli, E., Shen, D. (2016). Inherent structure based multi-view learning with multi-template feature representation for Alzheimer’s disease diagnosis. IEEE Transactions on Biomedical Engineering, 63(7), 1473–1482.PubMedCrossRefGoogle Scholar
  43. Liu, M., Zhang, D., Chen, S., Xue, H. (2016). Joint binary classifier learning for ECOC-based multi-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11), 2335–2341.CrossRefGoogle Scholar
  44. Liu, M., Zhang, D., Shen, D. (2016). Relationship induced multi-template learning for diagnosis of Alzheimer’s disease and mild cognitive impairment. IEEE Transactions on Medical Imaging, 35(6), 1463–1474.PubMedPubMedCentralCrossRefGoogle Scholar
  45. Liu, M., Zhang, J., Adeli, E., Shen, D. (2017). Deep multi-task multi-channel learning for joint classification and regression of brain status. In International conference on medical image computing and computer-assisted intervention (pp. 3–11): Springer.Google Scholar
  46. Marsman, A., Mandl, R.C., Mp, V.D.H., Boer, V.O., Wijnen, J.P., Klomp, D.W., Luijten, P.R., Hilleke, E. (2013). H.P.: Glutamate changes in healthy young adulthood. European Neuropsychopharmacology, 23 (11), 1484–1490.PubMedCrossRefGoogle Scholar
  47. Morgan, A.R., Touchard, S., O’ Hagan, C., Sims, R., Majounie, E., Escott-Price, V., Jones, L., Williams, J., Morgan, B.P. (2017). The correlation between inflammatory biomarkers and polygenic risk score in Alzheimer’s disease. Journal of Alzheimer’s Disease, 56(1), 25–36.PubMedCrossRefGoogle Scholar
  48. Neuhaus, A.H., Popescu, F.C., Grozea, C., Hahn, E., Hahn, C., Opgenrhein, C., Urbanek, C., Dettling, M. (2011). Single-subject classification of schizophrenia by event-related potentials during selective attention. NeuroImage, 55(2), 514–521.PubMedCrossRefGoogle Scholar
  49. Noriaki, Y., Jun, M., Ryuichiro, H., Giuseppe, L., Kazuhisa, S., Yuki, K., Hitoshi, K., Miho, K., Takashi, Y., Fukuda, M. (2016). A small number of abnormal brain connections predicts adult autism spectrum disorder. Nature Communications, 7(11254), 1–12.Google Scholar
  50. Rosario, B.L., Ziolko, S.K., Weissfeld, L.A., Price, J.C. (2008). Assessment of parameter settings for SPM5 spatial normalization of structural MRI data: application to type 2 diabetes. NeuroImage, 41(2), 363–370.PubMedPubMedCentralCrossRefGoogle Scholar
  51. Shen, H., Wang, L., Liu, Y., Hu, D. (2010). Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage, 49(4), 3110–3121.PubMedCrossRefGoogle Scholar
  52. Song, X.W., Dong, Z.Y., Long, X.Y., Li, S.F., Zuo, X.N., Zhu, C.Z., He, Y., Yan, C.G., Zang, Y.F. (2011). REST: a toolkit for resting-state functional magnetic resonance imaging data processing. Plos One, 6(9), 1–12.Google Scholar
  53. Su, L., Wang, L., Shen, H., Feng, G., Hu, D. (2013). Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI study. Frontiers in Human Neuroscience, 7(702), 1–12.Google Scholar
  54. Takayanagi, Y., Takahashi, T., Orikabe, L., Mozue, Y., Kawasaki, Y., Nakamura, K., Sato, Y., Itokawa, M., Yamasue, H., Kasai, K., Kurachi, M., Okazaki, Y., Suzuki, M. (2011). Classification of first-episode schizophrenia patients and healthy subjects by automated mri measures of regional brain volume and cortical thickness. PLOS ONE, 6(6), 1–10.CrossRefGoogle Scholar
  55. Wang, J., Wang, Q., Peng, J., Nie, D., Zhao, F., Kim, M., Zhang, H., Wee, C.Y., Wang, S., Shen, D. (2017). Multi-task diagnosis for autism spectrum disorders using multi-modality features: a multi-center study. Human Brain Mapping, 38(6), 3081–3097.PubMedPubMedCentralCrossRefGoogle Scholar
  56. Wang, J., Zuo, X., He, Y. (2010). Graph-based network analysis of resting-state functional MRI. Frontiers in Systems Neuroscience, 4(16), 1–14.Google Scholar
  57. Wang, M., Hao, X., Huang, J., Wang, K., Xu, X., Zhang, D. (2017). Multi-level multi-task structured sparse learning for diagnosis of schizophrenia disease. In International conference on medical image computing and computer-assisted intervention (pp. 46–54): Springer.Google Scholar
  58. Wang, Z., Zhang, Z., Liao, W., Xu, Q., Zhang, J., Lu, W., Jiao, Q., Chen, G., Feng, J., Lu, G. (2014). Frequency-dependent amplitude alterations of resting-state spontaneous fluctuations in idiopathic generalized epilepsy. Epilepsy Research, 108(5), 853–860.PubMedCrossRefGoogle Scholar
  59. Yu, R., Chien, Y.L., Wang, H.L.S., Liu, C.M., Liu, C.C., Hwang, T.J., Ming, H.H., Hwu, H.G., Tseng, W.Y.I. (2014). Frequency-specific alternations in the amplitude of low-frequency fluctuations in schizophrenia. Human Brain Mapping, 35(2), 627–637.PubMedCrossRefGoogle Scholar
  60. Zhang, D., Huang, J., Jie, B., Du, J., Tu, L., Liu, M. (2018). Ordinal pattern: a new descriptor for brain connectivity networks. IEEE Transactions on Medical Imaging, 37(7), 1711–1722.PubMedCrossRefGoogle Scholar
  61. Zhou, Z., Wang, J.B., Zang, Y.F., Pan, G. (2018). PAIR comparison between two within-group conditions of resting-state fMRI improves classification accuracy. Frontiers in Neuroscience, 11, 740.PubMedPubMedCentralCrossRefGoogle Scholar
  62. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 67(2), 301–320.CrossRefGoogle Scholar
  63. Zuo, X.N., Di, M.A., Kelly, C., Shehzad, Z.E., Gee, D.G., Klein, D.F., Castellanos, F.X., Biswal, B.B., Milham, M.P. (2010). The oscillating brain: complex and reliable. NeuroImage, 49(2), 1432–1445.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© 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

Personalised recommendations