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Data Science, Quality & Reliability

Discriminant subgraph learning from functional brain sensory data

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Pages 1084-1097 | Received 15 Jul 2020, Accepted 03 Sep 2021, Published online: 16 Dec 2021

References

  • Bogdanov, P., Dereli, N., Dang, X.-H., Bassett, D.S., Wymbs, N.F., Grafton, S.T. and Singh, A.K. (2017) Learning about learning: Mining human brain sub-network biomarkers from fMRI data. PloS One, 12(10), e0184344.
  • Chong, C.D., Gaw, N., Fu, Y., Li, J., Wu, T. and Schwedt, T.J. (2017) Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia, 37(9), 828–844.
  • Chong, C.D., Wang, L., Wang, K., Traub, S. and Li, J. (2019) Homotopic region connectivity during concussion recovery: A longitudinal fMRI study. PloS One, 14(10), e0221892.
  • Dawid, A.P. (1981) Some matrix-variate distribution theory: Notational considerations and a Bayesian application. Biometrika, 68(1), 265–274.
  • Faragó, P., Szabó, N., Tóth, E., Tuka, B., Király, A., Csete, G., Párdutz, Á., Szok, D., Tajti, J. and Ertsey, C. (2017) Ipsilateral alteration of resting state activity suggests that cortical dysfunction contributes to the pathogenesis of cluster headache. Brain Topography, 30(2), 281–289.
  • Friedman, J., Hastie, T., Höfling, H. and Tibshirani, R. (2007) Pathwise coordinate optimization. The Annals of Applied Statistics, 1(2), 302–332.
  • Friedman, J., Hastie, T. and Tibshirani, R. (2001) The Elements of Statistical Learning, Springer, New York, NY.
  • Gärtner, T., Flach, P. and Wrobel, S. (2003) On graph kernels: Hardness results and efficient alternatives in Handbook of Learning Theory and Kernel Machines, Springer, Berlin, Heidelberg, pp. 129–143.
  • Hadjikhani, N., Ward, N., Boshyan, J., Napadow, V., Maeda, Y., Truini, A., Caramia, F., Tinelli, E. and Mainero, C. (2013) The missing link: Enhanced functional connectivity between amygdala and visceroceptive cortex in migraine. Cephalalgia, 33(15), 1264–1268.
  • Hirose, K., Fujisawa, H. and Sese, J. (2017) Robust sparse Gaussian graphical modeling. Journal of Multivariate Analysis, 161, 172–190.
  • Huang, S., Li, J., Chen, K., Wu, T., Ye, J., Wu, X. and Yao, L. (2012) A transfer learning approach for network modeling. IIE Transactions, 44(11), 915–931.
  • Jordan, M.I. and Weiss, Y. (2002) Graphical models: Probabilistic inference in Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, MA, pp. 490–496.
  • Kuismin, M. and Sillanpää, M.J. (2016) Use of Wishart prior and simple extensions for sparse precision matrix estimation. PloS One, 11(2), e0148171.
  • Li, Q., Zhu, Z. and Tang, G. (2019) Alternating minimizations converge to second-order optimal solutions, in Proceedings of International Conference on Machine Learning, PMLR, Long Beach, CA, pp. 3935–3943.
  • Pan, S., Wu, J., Zhu, X., Zhang, C. and Philip, S.Y. (2015) Joint structure feature exploration and regularization for multi-task graph classification. IEEE Transactions on Knowledge and Data Engineering, 28(3), 715–728.
  • Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A. and Shulman, G.L. (2001) A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676–682.
  • Riesen, K. and Bunke, H. (2009) Graph classification by means of Lipschitz embedding. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(6), 1472–1483.
  • Russo, A., Tessitore, A., Esposito, F., Marcuccio, L., Giordano, A., Conforti, R., Truini, A., Paccone, A., d’Onofrio, F. and Tedeschi, G. (2012) Pain processing in patients with migraine: An event-related fMRI study during trigeminal nociceptive stimulation. Journal of Neurology, 259(9), 1903–1912.
  • Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T. and Tsuda, K. (2009) gBoost: A mathematical programming approach to graph classification and regression. Machine Learning, 75(1), 69–89.
  • Schölkopf, B., Tsuda, K. and Vert, J. (2003) Kernel Methods in Computational Biology. MIT Press, Cambridge, MA.
  • Schwedt, T.J., Chong, C.D., Wu, T., Gaw, N., Fu, Y. and Li, J. (2015) Accurate classification of chronic migraine via brain magnetic resonance imaging. Headache: The Journal of Head and Face Pain, 55(6), 762–777.
  • Shervashidze, N., Schweitzer, P., van Leeuwen, E.J., Mehlhorn, K. and Borgwardt, K.M. (2011) Weisfeiler-Lehman graph kernels. Journal of Machine Learning Research, 12(9), 2539–2561.
  • Shervashidze, N., Vishwanathan, S.V.N., Petri, T., Mehlhorn, K. and Borgwardt, K. (2009) Efficient graphlet kernels for large graph comparison, in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, PMLR, Clearwater Beach, FL, pp. 488–495.
  • Silva, A.R., Magalhães, R., Arantes, C., Moreira, P.S., Rodrigues, M., Marques, P., Marques, J., Sousa, N. and Pereira, V.H. (2019) Brain functional connectivity is altered in patients with Takotsubo Syndrome. Scientific Reports, 9(1), 1–11.
  • Sugiyama, M. and Borgwardt, K. (2015) Halting in random walk kernels. Advances in Neural Information Processing Systems, 28, 1639–1647.
  • Tessitore, A., Russo, A., Giordano, A., Conte, F., Corbo, D., De Stefano, M., Cirillo, S., Cirillo, M., Esposito, F. and Tedeschi, G. (2013) Disrupted default mode network connectivity in migraine without aura. The Journal of Headache and Pain, 14(1), 1–7.
  • Thanikaivelan, S. and Gandhi, K.R. (2017) Efficient subgraph selection using principal component analysis with pruning methods in multitask graph classification. International Journal of Control Theory and Applications, 10(19), 195–210.
  • Tseng, P. (2001) Convergence of a block coordinate descent method for nondifferentiable minimization. Journal of Optimization Theory and Applications, 109(3), 475–494.
  • Van Den Heuvel, M.P. and Pol, H.E.H. (2010) Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519–534.
  • Vogelstein, J.T., Roncal, W.G., Vogelstein, R.J. and Priebe, C.E. (2012) Graph classification using signal-subgraphs: Applications in statistical connectomics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1539–1551.
  • Wu, C.F.J. (1983) On the convergence properties of the EM algorithm. The Annals of Statistics, 11(1), 95–103.
  • Yan, X., Cheng, H., Han, J. and Yu, P.S. (2008) Mining significant graph patterns by leap search, in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, ACM Press, Vancouver, Canada, pp. 433–444.
  • Yan, X. and Han, J. (2002) gSpan: Graph-based substructure pattern mining, in Proceedings of 2002 IEEE International Conference on Data Mining, IEEE Press, Maebashi City, Japan, pp. 721–724.
  • Yu, D., Yuan, K., Zhao, L., Zhao, L., Dong, M., Liu, P., Wang, G., Liu, J., Sun, J. and Zhou, G. (2012) Regional homogeneity abnormalities in patients with interictal migraine without aura: A resting-state study. NMR in Biomedicine, 25(5), 806–812.
  • Yuan, G. and Ghanem, B. (2017) An exact penalty method for binary optimization based on MPEC formulation, in Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, San Francisco, CA, pp. 2867–2875.

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