{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:34:34Z","timestamp":1743093274327,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304928"},{"type":"electronic","value":"9783030304935"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-30493-5_27","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"258-272","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Inter-region Synchronization Analysis Based on Heterogeneous Matrix Similarity Measurement"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3571-0005","authenticated-orcid":false,"given":"Hengjin","family":"Ke","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7055-141X","authenticated-orcid":false,"given":"Dan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"XinHua","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xianzeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1038\/npp.2016.186","volume":"42","author":"CG Abdallah","year":"2017","unstructured":"Abdallah, C.G., et al.: Ketamine treatment and global brain connectivity in major depression. Neuropsychopharmacology 42, 1210\u20131219 (2017). https:\/\/doi.org\/10.1038\/npp.2016.186","journal-title":"Neuropsychopharmacology"},{"key":"27_CR2","doi-asserted-by":"publisher","unstructured":"Baird, L., Moore, A.: Gradient descent for general reinforcement learning. In: Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II, pp. 968\u2013974. MIT Press, Cambridge (1999). https:\/\/doi.org\/10.1145\/1514274.1514279","DOI":"10.1145\/1514274.1514279"},{"key":"27_CR3","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/nn.4502","volume":"20","author":"DS Bassett","year":"2017","unstructured":"Bassett, D.S., Sporns, O.: Network neuroscience. Nat. Neurosci. 20, 353\u2013364 (2017). https:\/\/doi.org\/10.1038\/nn.4502","journal-title":"Nat. Neurosci."},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11571-013-9267-8","volume":"8","author":"JD Bonita","year":"2014","unstructured":"Bonita, J.D., et al.: Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures. Cogn. Neurodyn. 8, 1\u201315 (2014). https:\/\/doi.org\/10.1007\/s11571-013-9267-8","journal-title":"Cogn. Neurodyn."},{"key":"27_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"S Boyd","year":"2004","unstructured":"Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, London (2004)"},{"issue":"1","key":"27_CR6","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1109\/TNSRE.2013.2258939","volume":"22","author":"D Chen","year":"2014","unstructured":"Chen, D., Li, X., Cui, D., Wang, L., Lu, D.: Global synchronization measurement of multivariate neural signals with massively parallel nonlinear interdependence analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 22(1), 33\u201343 (2014). https:\/\/doi.org\/10.1109\/TNSRE.2013.2258939","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"27_CR7","unstructured":"Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems 29 (NIPS), pp. 658\u2013666. Curran Associates Inc., Barcelona (2016). http:\/\/papers.nips.cc\/paper\/6158-generating-images-with-perceptual-similarity-metrics-based-on-deep-networks.pdf"},{"key":"27_CR8","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1038\/nm.4246","volume":"23","author":"AT Drysdale","year":"2017","unstructured":"Drysdale, A.T., et al.: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23, 28\u201338 (2017). https:\/\/doi.org\/10.1038\/nm.4246","journal-title":"Nat. Med."},{"issue":"1","key":"27_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neuroimage.2011.01.046","volume":"56","author":"AS Ghuman","year":"2011","unstructured":"Ghuman, A.S., McDaniel, J.R., Martin, A.: A wavelet-based method for measuring the oscillatory dynamics of resting-state functional connectivity in MEG. NeuroImage 56(1), 69\u201377 (2011). https:\/\/doi.org\/10.1016\/j.neuroimage.2011.01.046","journal-title":"NeuroImage"},{"key":"27_CR10","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision (ICCV 2015), vol. 1502, pp. 1026\u20131034, February 2015. https:\/\/doi.org\/10.1109\/ICCV.2015.123","DOI":"10.1109\/ICCV.2015.123"},{"key":"27_CR11","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, vol. abs\/1512.03385, Las Vegas, NV, USA, June 2016. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"27_CR12","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR, Lille, France, 07\u201309 July 2015, vol. 37, pp. 448\u2013456 (2015). http:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"27_CR13","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/nrn3578","volume":"14","author":"ER Kandel","year":"2013","unstructured":"Kandel, E.R., Markram, H., Matthews, P.M., Yuste, R., Koch, C.: Neuroscience thinks big (and collaboratively). Nat. Rev. Neurosci. 14, 659\u2013664 (2013). https:\/\/doi.org\/10.1038\/nrn3578","journal-title":"Nat. Rev. Neurosci."},{"key":"27_CR14","doi-asserted-by":"publisher","first-page":"14722","DOI":"10.1109\/ACCESS.2018.2810882","volume":"6","author":"H Ke","year":"2018","unstructured":"Ke, H., Chen, D., Li, X., Tang, Y., Shah, T., Ranjan, R.: Towards brain big data classification: Epileptic EEG identification with a lightweight VGGNet on global MIC. IEEE Access 6, 14722\u201314733 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2810882","journal-title":"IEEE Access"},{"issue":"Suppl. C","key":"27_CR15","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.nicl.2013.03.007","volume":"2","author":"DJ Kim","year":"2013","unstructured":"Kim, D.J., et al.: Disturbed resting state EEG synchronization in bipolar disorder: a graph-theoretic analysis. NeuroImage: Clin. 2(Suppl. C), 414\u2013423 (2013). https:\/\/doi.org\/10.1016\/j.nicl.2013.03.007","journal-title":"NeuroImage: Clin."},{"issue":"2","key":"27_CR16","doi-asserted-by":"publisher","first-page":"2012","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(2), 2012 (2012). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"issue":"2","key":"27_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0032508","volume":"7","author":"AF Leuchter","year":"2012","unstructured":"Leuchter, A.F., Cook, I.A., Hunter, A.M., Cai, C., Horvath, S.: Resting-state quantitative electroencephalography reveals increased neurophysiologic connectivity in depression. PLOS One 7(2), 1\u201313 (2012). https:\/\/doi.org\/10.1371\/journal.pone.0032508","journal-title":"PLOS One"},{"issue":"2","key":"27_CR18","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.neuroimage.2010.05.003","volume":"52","author":"X Li","year":"2010","unstructured":"Li, X., Ouyang, G.: Estimating coupling direction between neuronal populations with permutation conditional mutual information. NeuroImage 52(2), 497\u2013507 (2010). https:\/\/doi.org\/10.1016\/j.neuroimage.2010.05.003","journal-title":"NeuroImage"},{"issue":"2","key":"27_CR19","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s11517-017-1685-z","volume":"56","author":"W Mumtaz","year":"2018","unstructured":"Mumtaz, W., Ali, S.S.A., Yasin, M.A.M., Malik, A.S.: A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD). Med. Biol. Eng. Comput. 56(2), 233\u2013246 (2018). https:\/\/doi.org\/10.1007\/s11517-017-1685-z","journal-title":"Med. Biol. Eng. Comput."},{"issue":"6062","key":"27_CR20","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1126\/science.1205438","volume":"334","author":"DN Reshef","year":"2011","unstructured":"Reshef, D.N., et al.: Detecting novel associations in large datasets. Science 334(6062), 1518\u20131524 (2011). https:\/\/doi.org\/10.1126\/science.1205438","journal-title":"Science"},{"issue":"3","key":"27_CR21","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059\u20131069 (2010). https:\/\/doi.org\/10.1016\/j.neuroimage.2009.10.003","journal-title":"NeuroImage"},{"key":"27_CR22","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems 30 (NIPS), pp. 3859\u20133869 (2017). http:\/\/papers.nips.cc\/paper\/6975-dynamic-routing-between-capsules.pdf"},{"key":"27_CR23","volume-title":"Multi-way Analysis","author":"A Smilde","year":"2004","unstructured":"Smilde, A., Bro, R., Geladi, P.: Multi-way Analysis. Wiley, West Sussex (2004)"},{"issue":"1","key":"27_CR24","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.neulet.2003.10.063","volume":"355","author":"C Stam","year":"2004","unstructured":"Stam, C.: Functional connectivity patterns of human magnetoencephalographic recordings: a \u2018small-world\u2019 network? Neurosci. Lett. 355(1), 25\u201328 (2004). https:\/\/doi.org\/10.1016\/j.neulet.2003.10.063","journal-title":"Neurosci. Lett."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:50:18Z","timestamp":1710348618000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}