{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:16:34Z","timestamp":1742912194105,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819985456"},{"type":"electronic","value":"9789819985463"}],"license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8546-3_14","type":"book-chapter","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:02:17Z","timestamp":1703530937000},"page":"170-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Consistency Guided Multiview Hypergraph Embedding Learning with\u00a0Multiatlas-Based Functional Connectivity Networks Using Resting-State fMRI"],"prefix":"10.1007","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Li","family":"Xiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Baggio, H.C., et al.: Cerebellar resting-state functional connectivity in Parkinson\u2019s disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level. NeuroImage: Clinical 22, p. 101720 (2019)","key":"14_CR1","DOI":"10.1016\/j.nicl.2019.101720"},{"key":"14_CR2","volume-title":"Graphs and Hypergraphs","author":"C Berge","year":"1976","unstructured":"Berge, C.: Graphs and Hypergraphs. North-Holland, Amsterdam (1976)"},{"issue":"8","key":"14_CR3","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1002\/hbm.25387","volume":"42","author":"J Chen","year":"2021","unstructured":"Chen, J., et al.: Sparse deep neural networks on imaging genetics for schizophrenia case-control classification. Hum. Brain Mapp. 42(8), 2556\u20132568 (2021)","journal-title":"Hum. Brain Mapp."},{"key":"14_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2021.802305","volume":"15","author":"Y Chu","year":"2022","unstructured":"Chu, Y., Wang, G., Cao, L., Qiao, L., Liu, M.: Multi-scale graph representation learning for autism identification with functional MRI. Front. Neuroinform. 15, 802305 (2022)","journal-title":"Front. Neuroinform."},{"issue":"3","key":"14_CR5","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R.S., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968\u2013980 (2006)","journal-title":"Neuroimage"},{"issue":"6","key":"14_CR6","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1038\/mp.2013.78","volume":"19","author":"A Di Martino","year":"2014","unstructured":"Di Martino, A., et al.: The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659\u2013667 (2014)","journal-title":"Mol. Psychiatry"},{"issue":"5","key":"14_CR7","first-page":"2548","volume":"44","author":"Y Gao","year":"2022","unstructured":"Gao, Y., et al.: Hypergraph learning: Methods and practices. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2548\u20132566 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"14_CR8","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1002\/hbm.24812","volume":"41","author":"X Guo","year":"2020","unstructured":"Guo, X., et al.: Altered inter-and intrahemispheric functional connectivity dynamics in autistic children. Hum. Brain Mapp. 41(2), 419\u2013428 (2020)","journal-title":"Hum. Brain Mapp."},{"key":"14_CR9","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.neuroimage.2017.12.052","volume":"169","author":"SI Ktena","year":"2018","unstructured":"Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage 169, 431\u2013442 (2018)","journal-title":"Neuroimage"},{"issue":"1","key":"14_CR10","doi-asserted-by":"publisher","first-page":"27","DOI":"10.5607\/en.2020.29.1.27","volume":"29","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Xu, L., Li, J., Yu, J., Yu, X.: Attentional connectivity-based prediction of autism using heterogeneous rs-fMRI data from CC200 atlas. Exp. Neurobiol. 29(1), 27 (2020)","journal-title":"Exp. Neurobiol."},{"issue":"2","key":"14_CR11","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.neuroimage.2009.04.069","volume":"47","author":"CS Monk","year":"2009","unstructured":"Monk, C.S., et al.: Abnormalities of intrinsic functional connectivity in autism spectrum disorders. Neuroimage 47(2), 764\u2013772 (2009)","journal-title":"Neuroimage"},{"key":"14_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.103977","volume":"78","author":"K Qin","year":"2022","unstructured":"Qin, K., et al.: Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites. EBioMedicine 78, 103977 (2022)","journal-title":"EBioMedicine"},{"issue":"1","key":"14_CR13","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., et al.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, 618\u2013626 (2017)","key":"14_CR14","DOI":"10.1109\/ICCV.2017.74"},{"issue":"1","key":"14_CR15","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neulet.2010.03.080","volume":"476","author":"DK Shukla","year":"2010","unstructured":"Shukla, D.K., Keehn, B., M\u00fcller, R.A.: Regional homogeneity of fMRI time series in autism spectrum disorders. Neurosci. Lett. 476(1), 46\u201351 (2010)","journal-title":"Neurosci. Lett."},{"issue":"1","key":"14_CR16","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273\u2013289 (2002)","journal-title":"Neuroimage"},{"issue":"8","key":"14_CR17","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.euroneuro.2010.03.008","volume":"20","author":"MP Van Den Heuvel","year":"2010","unstructured":"Van Den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: A review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20(8), 519\u2013534 (2010)","journal-title":"Eur. Neuropsychopharmacol."},{"key":"14_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102063","volume":"71","author":"M Wang","year":"2021","unstructured":"Wang, M., Huang, J., Liu, M., Zhang, D.: Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI. Med. Image Anal. 71, 102063 (2021)","journal-title":"Med. Image Anal."},{"issue":"1","key":"14_CR19","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1038\/s41398-019-0641-0","volume":"9","author":"RH Wichers","year":"2019","unstructured":"Wichers, R.H., et al.: Modulation of brain activation during executive functioning in autism with citalopram. Transl. Psychiatry 9(1), 286 (2019)","journal-title":"Transl. Psychiatry"},{"issue":"7","key":"14_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0068910","volume":"8","author":"M Xia","year":"2013","unstructured":"Xia, M., Wang, J., He, Y.: BrainNet Viewer: A network visualization tool for human brain connectomics. PLoS ONE 8(7), e68910 (2013)","journal-title":"PLoS ONE"},{"key":"14_CR21","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s12021-016-9299-4","volume":"14","author":"CG Yan","year":"2016","unstructured":"Yan, C.G., Wang, X.D., Zuo, X.N., Zang, Y.F.: DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics 14, 339\u2013351 (2016)","journal-title":"Neuroinformatics"},{"doi-asserted-by":"crossref","unstructured":"Yang, X., Schrader, P.T., Zhang, N.: A deep neural network study of the ABIDE repository on autism spectrum classification. Int. J. Adv. Comput. Sci. Appl. 11(4), (2020)","key":"14_CR22","DOI":"10.14569\/IJACSA.2020.0110401"},{"issue":"48","key":"14_CR23","doi-asserted-by":"publisher","first-page":"17514","DOI":"10.1523\/JNEUROSCI.3127-11.2011","volume":"31","author":"S Yu","year":"2011","unstructured":"Yu, S., et al.: Higher-order interactions characterized in cortical activity. J. Neurosci. 31(48), 17514\u201317526 (2011)","journal-title":"J. Neurosci."},{"issue":"2","key":"14_CR24","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1109\/TNB.2015.2403274","volume":"14","author":"X Zhang","year":"2015","unstructured":"Zhang, X., Hu, B., Ma, X., Xu, L.: Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression. IEEE Trans. Nanobioscience 14(2), 237\u2013247 (2015)","journal-title":"IEEE Trans. Nanobioscience"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8546-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T19:14:04Z","timestamp":1703531644000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8546-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"ISBN":["9789819985456","9789819985463"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8546-3_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"26 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","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":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","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":"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":"1420","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":"532","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":"37% - 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":"3,78","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":"3,69","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)"}}]}}