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21. Li, Y., Zheng, G., Qi, X., Yu, Y., & Ieee (2018). Critical brain dynamics in the EEG resting state. 2018 Ieee 23rd International Conference on Digital Signal Processing.


22. Liu, Z., Zhang, J., Xie, X., Rolls, E., Sun, J., Zhang, K., Jiao, Z., Chen, Q., Zhang, J., Qiu, J., & Feng, J. (2018). Neural and genetic determinants of creativity. Neuroimage, 174, 164-176. Neural and genetic determinants of creativity.pdf


23. Liu, Z., Zhang, J., Zhang, K., Zhang, J., Li, X., Cheng, W., Li, M., Zhao, L., Deng, W., Guo, W., Ma, X., Wang, Q., Matthews, P., Feng, J., & Li, T. (2018). Distinguishable brain networks relate disease susceptibility to symptom expression in schizophrenia. Human Brain Mapping, 39(9), 3503-3515. Distinguishable brain networks relate disease susceptibility to symptom expression in schizophrenia.pdf


24. Lu, W., & Atay, F. (2018). Stability of Phase Difference Trajectories of Networks of Kuramoto Oscillators with Time-Varying Couplings and Intrinsic FrequenciesSiam. Journal on Applied Dynamical Systems, 17(1), 457-483.


25. Luo, H., Huang, Y., Du, X., Zhang, Y., Green, A., Aziz, T., & Wang, S. (2018). Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain. Frontiers in Neuroscience, 12. Dynamic neural state identification in deep brain local field potentials of neuropathic pain.pdf


26. Ma, H., Leng, S., Aihara, K., Lin, W., & Chen, L. (2018). Randomly distributed embedding making short-term high-dimensional data predictable. Proceedings of the National Academy of Sciences of the United States of America, 115(43), E9994-E10002. Randomly distributed embedding making short-term.pdf


27. Macare, C., Ducci, F., Zhang, Y., Ruggeri, B., Jia, T., Kaakinen, M., Kalsi, G., Charoen, P., Casoni, F., Peters, J., Bromberg, U., Hil, M., Buxton, J., Blakemore, A., Veijola, J., Buchel, C., Banaschewski, T., Bokde, A., Conrod, P., Flor, H., Frouin, V., Gallinat, J., Garavan, H., Gowland, P., Heinz, A., Itternnann, B., Lathrop, M., Martinot, J.L., Paus, T., Desrivieres, S., Munafo, M., Jarvelin, M.R., Schumanna, G., & Consortium, I. (2018). A neurobiological pathway to smoking in adolescence: TTC12-ANKK1-DRD2 variants and reward response. European Neuropsychopharmacology, 28(10), 1103-1114.


28. Peng, W., Mao, L., Yin, D., Sun, W., Wang, H., Zhang, Q., Wang, J., Chen, C., Zeng, M., Ding, J., & Wang, X. (2018). Functional network changes in the hippocampus contribute to depressive symptoms in epilepsy. Seizure-European Journal of Epilepsy, 60, 16-22. Functional network changes in the hippocampus contribute to depressive symptoms in epilepsy.pdf


29. Peron, T., Ji, P., Kurths, J., & Rodrigues, F. (2018). Spectra of random networks in the weak clustering regime. Epl, 121(6).


30. Protachevicz, P., Borges, R., Reis, A., Borges, F., Iarosz, K., Caldas, I., Lameu, E., Macau, E., Viana, R., Sokolov, I., Ferrari, F., Kurths, I., Batista, A., Lo, C., He, Y., & Lin, C. (2018). Synchronous behaviour in network model based on human cortico-cortical connections. Physiological Measurement, 39(7). Synchronous behaviour in network model based on human corticocortical.pdf


31. Rolls, E., Cheng, W., Gilson, M., Qiu, J., Hu, Z., Ruan, H., Li, Y., Huang, C.C., Yang, A., Tsai, S.J., Zhang, X., Zhuang, K., Lin, C.P., Deco, G., Xie, P., & Feng, J. (2018). Effective Connectivity in Depression. Biological Psychiatry-Cognitive Neuroscience and Neuroimaging, 3(2), 187-197. Effective Connectivity in Depression.pdf


32. Zhang, N., Xia, M., Qiu, T., Wang, X., Lin, C.p., Guo, Q., Lu, J., Wu, Q., Zhuang, D., Yu, Z., Gong, F., Hameed, N., He, Y., Wu, J., & Zhou, L. (2018). Reorganization of cerebro-cerebellar circuit in patients with left hemispheric gliomas involving language network: A combined structural and resting-state functional MRI study. Human Brain Mapping, 39(12), 4802-4819.


33. Zhou, S., & Yu, Y. (2018). Synaptic Excitatory-Inhibitory Balance Underlying Efficient Neural Coding. Systems Neuroscience, 21, 85-100.


34. Zhou, Y., Tao, C., Lu, W., & Feng, J. (2018). An asymptotic theory for cross-correlation between auto-correlated sequences and its application on neuroimaging data. Journal of Neuroscience Methods, 304, 52-65. An asymptotic theory for cross-correlation between auto-correlated sequences and its application on neuroimaging data.pdf


35. Zhu, G., Geng, X., Tan, Z., Chen, Y., Zhang, R., Wang, X., Aziz, T., Wang, S., & Zhang, J. (2018). Characteristics of Globus Pallidus Internus Local Field Potentials in Hyperkinetic Disease. Frontiers in Neurology, 9. Characteristics of globus pallidus internus local field potentials in generalized dystonia patients with TWNK mutation.pdf


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