Hierarchical Structured Sparse Learning for Schizophrenia Identification
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.
KeywordsSchizophrenia 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.
- 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
- 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
- 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
- Duda, R.O., Hart, P.E., Stork, D.G. (2001). Pattern classification. USA: Wiley.Google Scholar
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- Liu, J., Ji, S., Ye, J. (2009). SLEP: sparse learning with efficient projections. Arizona State University.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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