{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:58:13Z","timestamp":1790470693861,"version":"4.1.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030876012","type":"print"},{"value":"9783030876029","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87602-9_25","type":"book-chapter","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T14:13:23Z","timestamp":1632752003000},"page":"266-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates"],"prefix":"10.1007","author":[{"given":"Guris","family":"\u00d6zen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4565-4502","authenticated-orcid":false,"given":"Ahmed","family":"Nebli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5595-6673","authenticated-orcid":false,"given":"Islem","family":"Rekik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s10608-012-9508-x","volume":"37","author":"RB Price","year":"2013","unstructured":"Price, R.B., Paul, B., Schneider, W., Siegle, G.J.: Neural correlates of three neurocognitive intervention strategies: a preliminary step towards personalized treatment for psychological disorders. Cogn. Ther. Res. 37, 657\u2013672 (2013)","journal-title":"Cogn. Ther. Res."},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"104","DOI":"10.21037\/atm.2016.03.26","volume":"4","author":"L Tan","year":"2016","unstructured":"Tan, L., Jiang, T., Tan, L., Yu, J.T.: Toward precision medicine in neurological diseases. Ann. Transl. Med. 4, 104 (2016)","journal-title":"Ann. Transl. Med."},{"key":"25_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1007\/978-3-030-32245-8_88","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"BE Ezzine","year":"2019","unstructured":"Ezzine, B.E., Rekik, I.: Learning-guided infinite network atlas selection for predicting longitudinal brain network evolution from a single observation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 796\u2013805. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_88"},{"key":"25_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-030-59354-4_14","volume-title":"Predictive Intelligence in Medicine","author":"A Nebli","year":"2020","unstructured":"Nebli, A., Kaplan, U.A., Rekik, I.: Deep EvoGraphNet architecture for time-dependent brain graph data synthesis from a single timepoint. In: Rekik, I., Adeli, E., Park, S.H., Vald\u00e9s Hern\u00e1ndez, M.C. (eds.) PRIME 2020. LNCS, vol. 12329, pp. 144\u2013155. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59354-4_14"},{"key":"25_CR5","unstructured":"Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience (2021)"},{"key":"25_CR6","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks (2014)"},{"key":"25_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-030-59728-3_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"MB Gurbuz","year":"2020","unstructured":"Gurbuz, M.B., Rekik, I.: Deep graph normalizer: a geometric deep learning approach for estimating connectional brain templates. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 155\u2013165. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_16"},{"key":"25_CR8","unstructured":"Macqueen, J.: Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281\u2013297 (1967)"},{"key":"25_CR9","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1093\/biostatistics\/kxw050","volume":"18","author":"AF Mejia","year":"2017","unstructured":"Mejia, A.F., Nebel, M.B., Eloyan, A., Caffo, B., Lindquist, M.A.: PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data. Biostatistics 18, 521\u2013536 (2017)","journal-title":"Biostatistics"},{"key":"25_CR10","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175 (2017)"},{"key":"25_CR11","unstructured":"Kolay, S., Ray, K.: K+ means: an enhancement over k-means clustering algorithm (2017)"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs (2017)","DOI":"10.1109\/CVPR.2017.11"},{"key":"25_CR13","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","volume":"22","author":"D Marcus","year":"2010","unstructured":"Marcus, D., Fotenos, A., Csernansky, J., Morris, J., Buckner, R.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22, 2677\u20132684 (2010)","journal-title":"J. Cogn. Neurosci."},{"key":"25_CR14","unstructured":"Mahjoub, I., Mahjoub, M., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8 (2018)"},{"issue":"5","key":"25_CR15","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.1007\/s11682-019-00123-6","volume":"14","author":"A Nebli","year":"2019","unstructured":"Nebli, A., Rekik, I.: Gender differences in cortical morphological networks. Brain Imaging Behav. 14(5), 1831\u20131839 (2019). https:\/\/doi.org\/10.1007\/s11682-019-00123-6","journal-title":"Brain Imaging Behav."},{"key":"25_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1093\/cercor\/bhg087","volume":"14","author":"B Fischl","year":"2004","unstructured":"Fischl, B., et al.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11\u201322 (2004)","journal-title":"Cereb. Cortex"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Oldham, S., Fulcher, B., Parkes, L., Arnatkevici\u016bt\u0117, A., Suo, C., Fornito, A.: Consistency and differences between centrality measures across distinct classes of networks. PLOS ONE 14, e0220061 (2019)","DOI":"10.1371\/journal.pone.0220061"},{"key":"25_CR18","unstructured":"Loshchilov, I., Hutter, F.: Fixing weight decay regularization in adam (2017)"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Murtagh, F., Contreras, P.: Methods of hierarchical clustering (2011)","DOI":"10.1007\/978-3-642-04898-2_288"},{"key":"25_CR20","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1016\/j.clinph.2017.04.018","volume":"128","author":"L Koelewijn","year":"2017","unstructured":"Koelewijn, L., et al.: Alzheimer\u2019s disease disrupts alpha and beta-band resting-state oscillatory network connectivity. Clin. Neurophysiol. 128, 2347\u20132357 (2017)","journal-title":"Clin. Neurophysiol."},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Binnewijzend, M., et al.: Brain network alterations in Alzheimer\u2019s disease measured by eigenvector centrality in fMRI are related to cognition and cerebrospinal fluid biomarkers. Alzheimer\u2019s Dementia 9, P684 (2013)","DOI":"10.1016\/j.jalz.2013.04.347"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87602-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T00:03:34Z","timestamp":1790467414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87602-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030876012","9783030876029"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87602-9_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on PRedictive Intelligence In MEdicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"prime2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/basira-lab.com\/prime-miccai-2021\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","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":"25","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":"96% - 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":"3","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)"}},{"value":"The workshop was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}