{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:44:30Z","timestamp":1743072270555,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466700"},{"type":"electronic","value":"9783031466717"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-46671-7_6","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T11:02:26Z","timestamp":1699095746000},"page":"79-91","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Individual Functional Network Abnormalities Mapping via Graph Representation-Based Neural Architecture Search"],"prefix":"10.1007","author":[{"given":"Qing","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haixing","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinglei","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengliang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claire","family":"Coles","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoping","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dajiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1016\/S0140-6736(73)91092-1","volume":"302","author":"KL Jones","year":"1973","unstructured":"Jones, K.L., Smith, D.W.: Recognition of the fetal alcohol syndrome in early infancy. Lancet 302, 999\u20131001 (1973). https:\/\/doi.org\/10.1016\/S0140-6736(73)91092-1","journal-title":"Lancet"},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1097\/00004703-200110000-00024","volume":"22","author":"AD Sandler","year":"2001","unstructured":"Sandler, A.D.: Brain dysmorphology in individuals with severe prenatal alcohol exposure. J. Dev. Behav. Pediatr. 22, 341 (2001). https:\/\/doi.org\/10.1097\/00004703-200110000-00024","journal-title":"J. Dev. Behav. Pediatr."},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1111\/acer.14430","volume":"44","author":"G Bandoli","year":"2020","unstructured":"Bandoli, G., et al.: Patterns of prenatal alcohol exposure and alcohol-related dysmorphic features. Alcohol. Clin. Exp. Res. 44, 2045\u20132052 (2020)","journal-title":"Alcohol. Clin. Exp. Res."},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1111\/acer.14040","volume":"43","author":"SN Mattson","year":"2019","unstructured":"Mattson, S.N., Bernes, G.A., Doyle, L.R.: Fetal alcohol spectrum disorders: a review of the neurobehavioral deficits associated with prenatal alcohol exposure. Alcohol. Clin. Exp. Res. 43, 1046\u20131062 (2019)","journal-title":"Alcohol. Clin. Exp. Res."},{"key":"6_CR5","doi-asserted-by":"publisher","unstructured":"Zhao, S.,\u00a0et al.: A multi-stage sparse coding framework to explore the effects of prenatal alcohol exposure. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 28\u201336. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46720-7_4","DOI":"10.1007\/978-3-319-46720-7_4"},{"key":"6_CR6","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.gde.2016.03.011","volume":"233","author":"J Lv","year":"2015","unstructured":"Lv, J., et al.: Assessing effects of prenatal alcohol exposure using group-wise sparse representation of FMRI data. Psychiatry Res. 233, 254\u2013268 (2015). https:\/\/doi.org\/10.1016\/j.gde.2016.03.011","journal-title":"Psychiatry Res."},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1109\/TMI.2017.2715285","volume":"37","author":"H Huang","year":"2018","unstructured":"Huang, H., et al.: Modeling task fMRI data via deep convolutional autoencoder. IEEE Trans. Med. Imaging 37, 1551\u20131561 (2018). https:\/\/doi.org\/10.1109\/TMI.2017.2715285","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"2646","DOI":"10.1007\/s11682-021-00469-w","volume":"15","author":"Q Li","year":"2021","unstructured":"Li, Q., Dong, Q., Ge, F., Qiang, N., Wu, X., Liu, T.: Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent auto-encoder. Brain Imaging Behav. 15, 2646\u20132660 (2021). https:\/\/doi.org\/10.1007\/s11682-021-00469-w","journal-title":"Brain Imaging Behav."},{"key":"6_CR9","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/TCDS.2019.2916916","volume":"12","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., et al.: 4D Modeling of fMRI data via spatio-temporal convolutional neural networks (ST-CNN). IEEE Trans. Cognit. Dev. Syst. 12, 451\u2013460 (2020). https:\/\/doi.org\/10.1109\/TCDS.2019.2916916","journal-title":"IEEE Trans. Cognit. Dev. Syst."},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Zhao, L., et al.: Embedding human brain function via\u00a0transformer. Presented at the (2022). https:\/\/doi.org\/10.1007\/978-3-031-16431-6_35","DOI":"10.1007\/978-3-031-16431-6_35"},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Zhao, L., Dai, H., Jiang, X., Zhang, T., Zhu, D., Liu, T.: Exploring the functional difference of Gyri\/Sulci via hierarchical interpretable autoencoder. Presented at the (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_66","DOI":"10.1007\/978-3-030-87234-2_66"},{"key":"6_CR12","unstructured":"Yu, X., Zhang, L., Zhao, L., Lyu, Y., Liu, T., Zhu, D.: Disentangling spatial-temporal functional brain networks via twin-transformers (2022)"},{"key":"6_CR13","doi-asserted-by":"publisher","unstructured":"Li, Q.,\u00a0et al.: Simultaneous spatial-temporal decomposition of connectome-scale brain networks by deep sparse recurrent auto-encoders. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 579\u2013591. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_45","DOI":"10.1007\/978-3-030-20351-1_45"},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1109\/TBME.2021.3102466","volume":"69","author":"Q Li","year":"2022","unstructured":"Li, Q., Zhang, W., Zhao, L., Wu, X., Liu, T.: Evolutional neural architecture search for optimization of spatiotemporal brain network decomposition. IEEE Trans. Biomed. Eng. 69, 624\u2013634 (2022). https:\/\/doi.org\/10.1109\/TBME.2021.3102466","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"6_CR15","doi-asserted-by":"publisher","unstructured":"Zhang, W.,\u00a0et al.: Identify hierarchical structures from task-based fMRI data via hybrid spatiotemporal neural architecture search net. In: Shen, D.,\u00a0et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 745\u2013753. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_83","DOI":"10.1007\/978-3-030-32248-9_83"},{"key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102316","volume":"77","author":"S Liu","year":"2022","unstructured":"Liu, S., Ge, F., Zhao, L., Wang, T., Ni, D., Liu, T.: NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns. Med. Image Anal. 77, 102316 (2022). https:\/\/doi.org\/10.1016\/j.media.2021.102316","journal-title":"Med. Image Anal."},{"key":"6_CR17","doi-asserted-by":"publisher","unstructured":"Dai, H., Ge, F., Li, Q., Zhang, W., Liu, T.: Optimize CNN model for FMRI signal classification via Adanet-based neural architecture search. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1399\u20131403. IEEE (2020). https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098574","DOI":"10.1109\/ISBI45749.2020.9098574"},{"key":"6_CR18","doi-asserted-by":"publisher","unstructured":"Li, Q., Zhang, W., Lv, J., Wu, X., Liu, T.: Neural architecture search for optimization of spatial-temporal brain network decomposition. In: Martel, A.L.,\u00a0et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 377\u2013386. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_37","DOI":"10.1007\/978-3-030-59728-3_37"},{"key":"6_CR19","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: International Conference on Learning Representations \u2013 ICLR, pp. 1\u201312 (2019)"},{"key":"6_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101974","volume":"69","author":"Q Li","year":"2021","unstructured":"Li, Q., Wu, X., Liu, T.: Differentiable neural architecture search for optimal spatial\/temporal brain function network decomposition. Med. Image Anal. 69, 101974 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.101974","journal-title":"Med. Image Anal."},{"key":"6_CR21","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1007\/s11682-016-9624-4","volume":"11","author":"JR Wozniak","year":"2017","unstructured":"Wozniak, J.R., et al.: Functional connectivity abnormalities and associated cognitive deficits in fetal alcohol spectrum disorders (FASD). Brain Imaging Behav. 11, 1432\u20131445 (2017)","journal-title":"Brain Imaging Behav."},{"key":"6_CR22","doi-asserted-by":"publisher","unstructured":"Dai, H.,\u00a0et al.: Graph representation neural architecture search for optimal spatial\/temporal functional brain network decomposition. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) MLMI 2022. LNCS, vol. 13583, pp. 279\u2013287. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21014-3_29","DOI":"10.1007\/978-3-031-21014-3_29"},{"key":"6_CR23","unstructured":"Xu, K., Jegelka, S., Hu, W., Leskovec, J.: How powerful are graph neural networks? In: International Conference on Learning Representations \u2013 ICLR, pp. 1\u201317 (2019)"},{"key":"6_CR24","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1111\/j.1530-0277.2009.01028.x","volume":"33","author":"P Santhanam","year":"2009","unstructured":"Santhanam, P., Li, Z., Hu, X., Lynch, M., Coles, C.: Effects of prenatal alcohol exposure on brain activation during an arithmetic task: an fMRI study. Alcohol. Clin. Exp. Res. 33, 1901\u20131908 (2009)","journal-title":"Alcohol. Clin. Exp. Res."},{"key":"6_CR25","unstructured":"Graves, A.: Generating Sequences with Recurrent Neural Networks. http:\/\/arxiv.org\/abs\/1308.0850"},{"key":"6_CR26","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1037\/h0100860","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. B 67, 301\u2013320 (2005). https:\/\/doi.org\/10.1037\/h0100860","journal-title":"J. Roy. Stat. Soc. B"},{"key":"6_CR27","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297\u2013302 (1945)","journal-title":"Ecology"},{"key":"6_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/neuro.09.017.2009","volume":"3","author":"VD Calhoun","year":"2009","unstructured":"Calhoun, V.D., Eichele, T., Pearlson, G.: Functional brain networks in schizophrenia: a review. Front. Hum. Neurosci. 3, 1\u201312 (2009). https:\/\/doi.org\/10.3389\/neuro.09.017.2009","journal-title":"Front. Hum. Neurosci."},{"key":"6_CR29","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). https:\/\/doi.org\/10.1016\/j.neuroimage.2013.05.033","journal-title":"Neuroimage"},{"key":"6_CR30","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.pscychresns.2011.05.004","volume":"194","author":"P Santhanam","year":"2011","unstructured":"Santhanam, P., Coles, C.D., Li, Z., Li, L., Lynch, M.E., Hu, X.: Default mode network dysfunction in adults with prenatal alcohol exposure. Psychiatry Res Neuroimaging 194, 354\u2013362 (2011)","journal-title":"Psychiatry Res Neuroimaging"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46671-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T11:03:26Z","timestamp":1699095806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46671-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466700","9783031466717"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46671-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}