{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T05:08:03Z","timestamp":1775624883386,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164514","type":"print"},{"value":"9783031164521","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_35","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"365-374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Neuro-RDM: An Explainable Neural Network Landscape of Reaction-Diffusion Model for Cognitive Task Recognition"],"prefix":"10.1007","author":[{"given":"Tingting","family":"Dan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmin","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuobin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Laurienti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Won Hwa","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guorong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Sporns, O.: Networks of the Brain. MIT Press, Cambridge (2011)","DOI":"10.7551\/mitpress\/8476.001.0001"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"van den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacology 20(8), 519\u2013534 (2010)","DOI":"10.1016\/j.euroneuro.2010.03.008"},{"issue":"2","key":"35_CR3","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1038\/nrn730","volume":"3","author":"DJ Heeger","year":"2002","unstructured":"Heeger, D.J., Ress, D.: What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 3(2), 142\u2013151 (2002)","journal-title":"Nat. Rev. Neurosci."},{"issue":"22","key":"35_CR4","doi-asserted-by":"publisher","first-page":"8259","DOI":"10.1523\/JNEUROSCI.0440-11.2011","volume":"31","author":"MG Kitzbichler","year":"2011","unstructured":"Kitzbichler, M.G., et al.: Cognitive effort drives workspace configuration of human brain functional networks. J. Neurosci. 31(22), 8259\u20138270 (2011)","journal-title":"J. Neurosci."},{"issue":"6","key":"35_CR5","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.tics.2010.04.004","volume":"14","author":"SL Bressler","year":"2010","unstructured":"Bressler, S.L., Menon, V.: Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14(6), 277\u2013290 (2010)","journal-title":"Trends Cogn. Sci."},{"issue":"2","key":"35_CR6","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.neuron.2016.09.018","volume":"92","author":"JM Shine","year":"2016","unstructured":"Shine, J.M., et al.: The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92(2), 544\u2013554 (2016)","journal-title":"Neuron"},{"issue":"1","key":"35_CR7","doi-asserted-by":"publisher","first-page":"2505","DOI":"10.1038\/s41467-018-04723-6","volume":"9","author":"J Taghia","year":"2018","unstructured":"Taghia, J., et al.: Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition. Nat. Commun. 9(1), 2505 (2018)","journal-title":"Nat. Commun."},{"issue":"28","key":"35_CR8","doi-asserted-by":"publisher","first-page":"10341","DOI":"10.1073\/pnas.1400181111","volume":"111","author":"A Zalesky","year":"2014","unstructured":"Zalesky, A., et al.: Time-resolved resting-state brain networks. Proc. Natl. Acad. Sci. 111(28), 10341 (2014)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"48","key":"35_CR9","doi-asserted-by":"publisher","first-page":"12827","DOI":"10.1073\/pnas.1705120114","volume":"114","author":"D Vidaurre","year":"2017","unstructured":"Vidaurre, D., Smith, S.M., Woolrich, M.W.: Brain network dynamics are hierarchically organized in time. Proc. Natl. Acad. Sci. 114(48), 12827 (2017)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"12","key":"35_CR10","doi-asserted-by":"publisher","first-page":"4017","DOI":"10.1523\/JNEUROSCI.16-12-04017.1996","volume":"16","author":"SR Wicks","year":"1996","unstructured":"Wicks, S.R., Roehrig, C.J., Rankin, C.H.: A dynamic network simulation of the nematode tap withdrawal circuit: predictions concerning synaptic function using behavioral criteria. J. Neurosci. 16(12), 4017 (1996)","journal-title":"J. Neurosci."},{"key":"35_CR11","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.mri.2019.05.031","volume":"64","author":"M Khosla","year":"2019","unstructured":"Khosla, M., et al.: Machine learning in resting-state fMRI analysis. Magn. Reson. Imaging 64, 101\u2013121 (2019)","journal-title":"Magn. Reson. Imaging"},{"key":"35_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-030-00931-1_27","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"H Li","year":"2018","unstructured":"Li, H., Fan, Y.: Identification of temporal transition of functional states using recurrent neural networks from functional MRI. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 232\u2013239. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_27"},{"issue":"5999","key":"35_CR13","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1126\/science.1179047","volume":"329","author":"S Kondo","year":"2010","unstructured":"Kondo, S., Miura, T.: Reaction-diffusion model as a framework for understanding biological pattern formation. Science 329(5999), 1616\u20131620 (2010)","journal-title":"Science"},{"key":"35_CR14","unstructured":"Chen, R.T.Q., et al.: Neural ordinary differential equations. In: Advances in Neural Information Processing Systems (NIPS 2018), vol. 31 (2018)"},{"key":"35_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/978-3-030-87234-2_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Z Huang","year":"2021","unstructured":"Huang, Z., Cai, H., Dan, T., Lin, Y., Laurienti, P., Wu, G.: Detecting brain state changes by geometric deep learning of functional dynamics on riemannian manifold. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 543\u2013552. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_51"},{"key":"35_CR16","doi-asserted-by":"publisher","first-page":"116059","DOI":"10.1016\/j.neuroimage.2019.116059","volume":"202","author":"H Li","year":"2019","unstructured":"Li, H., Fan, Y.: Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks. Neuroimage 202, 116059 (2019)","journal-title":"Neuroimage"},{"key":"35_CR17","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3844\u20133852. Curran Associates Inc., Barcelona (2016)"},{"key":"35_CR18","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classificiation with graph convolutional networks. In: International Conference on Learning Representations ICLR (2016)"},{"key":"35_CR19","unstructured":"Chamberlain, B.P., et al.: GRAND: graph neural diffusion. In: 38th International Conference on Machine Learning, PMLR (2021)"},{"key":"35_CR20","unstructured":"Eliasof, M., Haber, E., Treister, E.: PDE-GCN: novel architectures for graph neural networks motivated by partial differential equations. In: 35th Conference on Neural Information Processing Systems (NIPS) (2021)"},{"key":"35_CR21","unstructured":"Veli\u010dkovi\u0107, P., et al.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"key":"35_CR22","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.neuroimage.2013.05.033","volume":"80","author":"DM Barch","year":"2013","unstructured":"Barch, D.M., et al.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169\u2013189 (2013)","journal-title":"Neuroimage"},{"key":"35_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/978-3-030-66843-3_9","volume-title":"Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology","author":"YH Chan","year":"2020","unstructured":"Chan, Y.H., Gupta, S., Kasun, L.L.C., Rajapakse, J.C.: Decoding task states by spotting salient patterns at time points and brain regions. In: Kia, S.M., et al. (eds.) MLCN\/RNO-AI -2020. LNCS, vol. 12449, pp. 88\u201397. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66843-3_9"},{"key":"35_CR24","doi-asserted-by":"publisher","unstructured":"Dan, T., et al.: Learning brain dynamics of evolving manifold functional MRI data using geometric-attention neural network. IEEE Trans. Med. Imaging, 1\u20131 (2022).\u00a0https:\/\/doi.org\/10.1109\/TMI.2022.3169640","DOI":"10.1109\/TMI.2022.3169640"},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Dan, T., et al.: Uncovering shape signatures of resting\u2010state functional connectivity by geometric deep learning on Riemannian manifold. Human Brain Mapping 43, 3970\u20133986 (2022)","DOI":"10.1002\/hbm.25897"},{"key":"35_CR26","unstructured":"Hasani, R., et al.: Liquid time-constant networks. arXiv preprint arXiv:2006.04439 (2020)"},{"key":"35_CR27","doi-asserted-by":"publisher","first-page":"117791","DOI":"10.1016\/j.neuroimage.2021.117791","volume":"230","author":"Y Lin","year":"2021","unstructured":"Lin, Y., et al.: Learning dynamic graph embeddings for accurate detection of cognitive state changes in functional brain networks. Neuroimage 230, 117791 (2021)","journal-title":"Neuroimage"},{"key":"35_CR28","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.neuroimage.2013.05.081","volume":"82","author":"X Shen","year":"2013","unstructured":"Shen, X., et al.: Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403\u2013415 (2013)","journal-title":"Neuroimage"},{"key":"35_CR29","doi-asserted-by":"publisher","unstructured":"Dai, C., et al.: Effects of sleep deprivation on working memory: change in functional connectivity between the Dorsal Attention, Default Mode, and Fronto-Parietal Networks. Front. Human Neurosci. 14, 360 (2020).\u00a0https:\/\/doi.org\/10.3389\/fnhum.2020.00360","DOI":"10.3389\/fnhum.2020.00360"},{"key":"35_CR30","doi-asserted-by":"publisher","unstructured":"Chai, W.J., Abd Hamid, A.I., Abdullah, J.M.: Working memory from the psychological and neurosciences perspectives: a review. Front. Psychol. 9, 401\u00a0(2018).\u00a0https:\/\/doi.org\/10.3389\/fpsyg.2018.00401","DOI":"10.3389\/fpsyg.2018.00401"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:47:35Z","timestamp":1710244055000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}