Hierarchical Structured Sparse Learning for Schizophrenia Identification

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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References

  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.

    PubMed  Google 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.

    PubMed  Google 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.

    CAS  PubMed  Google Scholar 

  4. Bhugra, D. (2005). The global prevalence of schizophrenia. Plos Medicine, 2(5), 372–373.

    Google 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.

    CAS  PubMed  Google Scholar 

  6. Buzsáki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304(5679), 1926–1929.

    PubMed  Google Scholar 

  7. Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.

    Google 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.

    Google 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.

  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.

    PubMed  PubMed Central  Google Scholar 

  11. Cherkassky, V. (1997). The nature of statistical learning theory. IEEE Transactions on Neural Networks, 8 (6), 1564–1564.

    CAS  PubMed  Google 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.

    PubMed  Google Scholar 

  13. Cole, M.W., & Schneider, W. (2007). The cognitive control network: integrated cortical regions with dissociable functions. NeuroImage, 37(1), 343–360.

    PubMed  Google Scholar 

  14. Demirci, O., & Calhoun, V.D. (2009). Functional magnetic resonance imaging-implications for detection of schizophrenia. European Neurological Review, 4(2), 103–106.

    PubMed  PubMed Central  Google 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.

    Google Scholar 

  16. Dietterich, T.G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895–1923.

    CAS  PubMed  Google 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.

    PubMed  Google 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.

    CAS  PubMed  Google 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.

    Google 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.

  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.

    PubMed  PubMed Central  Google Scholar 

  23. Hastie, T., Tibshirani, R., Friedman, J. (2001). The elements of statistical learning. New York: Springer.

    Google 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.

    PubMed  Google 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.

    PubMed  Google 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).

  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).

  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.

    PubMed  Google 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.

    PubMed  PubMed Central  Google 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.

    PubMed  PubMed Central  Google 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.

    PubMed  Google 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.

    Google 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.

    Google 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. https://doi.org/10.1109/TPAMI.2018.2889096.

  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.

    PubMed  Google 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.

  39. Liu, J., Ji, S., Ye, J. (2009). SLEP: sparse learning with efficient projections. Arizona State University.

  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.

    Google 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.

    PubMed  Google 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.

    Google 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.

    PubMed  PubMed Central  Google 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.

  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.

    CAS  PubMed  Google 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.

    CAS  PubMed  Google 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.

    PubMed  Google 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.

    PubMed  PubMed Central  Google 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.

    PubMed  Google 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.

    Google 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.

    PubMed  PubMed Central  Google 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.

    CAS  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.

  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.

    PubMed  Google 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.

    PubMed  Google 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.

    PubMed  Google 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.

    PubMed  PubMed Central  Google 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.

    Google 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.

    PubMed  Google Scholar 

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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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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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Keywords

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