{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:28:35Z","timestamp":1767065315632,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322472"},{"type":"electronic","value":"9783030322489"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32248-9_87","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"781-789","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Decoding Brain Functional Connectivity Implicated in AD and MCI"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8974-8482","authenticated-orcid":false,"given":"Sukrit","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2393-1110","authenticated-orcid":false,"given":"Yi Hao","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7944-1658","authenticated-orcid":false,"given":"Jagath C.","family":"Rajapakse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"87_CR1","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.neuroimage.2015.02.037","volume":"112","author":"E Challis","year":"2015","unstructured":"Challis, E., Hurley, P., Serra, L., Bozzali, M., Oliver, S., Cercignani, M.: Gaussian process classification of Alzheimer\u2019s disease and mild cognitive impairment from resting-state fMRI. NeuroImage 112, 232\u2013243 (2015)","journal-title":"NeuroImage"},{"issue":"10","key":"87_CR2","doi-asserted-by":"publisher","first-page":"5019","DOI":"10.1002\/hbm.23711","volume":"38","author":"X Chen","year":"2017","unstructured":"Chen, X., Zhang, H., Zhang, L., Shen, C., Lee, S.W., Shen, D.: Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. Hum. Brain Mapp. 38(10), 5019\u20135034 (2017)","journal-title":"Hum. Brain Mapp."},{"issue":"28","key":"87_CR3","doi-asserted-by":"publisher","first-page":"11583","DOI":"10.1073\/pnas.1220826110","volume":"110","author":"NA Crossley","year":"2013","unstructured":"Crossley, N.A., et al.: Cognitive relevance of the community structure of the human brain functional coactivation network. Proc. Nat. Acad. Sci. 110(28), 11583\u201311588 (2013)","journal-title":"Proc. Nat. Acad. Sci."},{"key":"87_CR4","doi-asserted-by":"publisher","first-page":"S141","DOI":"10.1016\/j.neurobiolaging.2014.03.041","volume":"36","author":"SR Das","year":"2015","unstructured":"Das, S.R., Pluta, J., Mancuso, L., Kliot, D., Yushkevich, P.A., Wolk, D.A.: Anterior and posterior MTL networks in aging and MCI. Neurobiol. Aging 36, S141\u2013S150 (2015)","journal-title":"Neurobiol. Aging"},{"key":"87_CR5","doi-asserted-by":"publisher","unstructured":"Esteban, O., Markiewicz, C., Blair, R.W., et al.: Fmriprep: a robust preprocessing pipeline for functional MRI (2018). https:\/\/doi.org\/10.1101\/306951","DOI":"10.1101\/306951"},{"key":"87_CR6","doi-asserted-by":"publisher","first-page":"327","DOI":"10.3389\/fnhum.2015.00327","volume":"9","author":"A Floren","year":"2015","unstructured":"Floren, A., Naylor, B., Miikkulainen, R., Ress, D.: Accurately decoding visual information from fMRI data obtained in a realistic virtual environment. Front. Hum. Neurosci. 9, 327 (2015)","journal-title":"Front. Hum. Neurosci."},{"key":"87_CR7","doi-asserted-by":"publisher","first-page":"615","DOI":"10.3389\/fnins.2017.00615","volume":"11","author":"H Guo","year":"2017","unstructured":"Guo, H., Zhang, F., Chen, J., Xu, Y., Xiang, J.: Machine learning classification combining multiple features of a hyper-network of fMRI data in Alzheimer\u2019s disease. Front. Neurosci. 11, 615 (2017)","journal-title":"Front. Neurosci."},{"key":"87_CR8","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.neuroimage.2016.04.003","volume":"145","author":"H Jang","year":"2017","unstructured":"Jang, H., Plis, S.M., Calhoun, V.D., Lee, J.H.: Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: evaluation using sensorimotor tasks. NeuroImage 145, 314\u2013328 (2017)","journal-title":"NeuroImage"},{"key":"87_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/978-3-030-00931-1_37","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"H Li","year":"2018","unstructured":"Li, H., Fan, Y.: Brain decoding from functional MRI using long short-term memory recurrent neural networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 320\u2013328. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_37"},{"key":"87_CR10","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, pp. 4765\u20134774 (2017)"},{"key":"87_CR11","doi-asserted-by":"publisher","first-page":"61","DOI":"10.3389\/fninf.2017.00061","volume":"11","author":"RJ Meszl\u00e9nyi","year":"2017","unstructured":"Meszl\u00e9nyi, R.J., Buza, K., Vidny\u00e1nszky, Z.: Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture. Front. Neuroinformatics 11, 61 (2017)","journal-title":"Front. Neuroinformatics"},{"issue":"3","key":"87_CR12","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.neuron.2012.12.028","volume":"77","author":"S Mueller","year":"2013","unstructured":"Mueller, S., Wang, D., Fox, M.D., Yeo, B.T., Sepulcre, J.: Individual variability in functional connectivity architecture of the human brain. Neuron 77(3), 586\u2013595 (2013)","journal-title":"Neuron"},{"issue":"4","key":"87_CR13","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1016\/j.neuron.2011.09.006","volume":"72","author":"JD Power","year":"2011","unstructured":"Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., et al.: Functional network organization of the human brain. Neuron 72(4), 665\u2013678 (2011)","journal-title":"Neuron"},{"key":"87_CR14","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 3145\u20133153. JMLR.org (2017)"},{"key":"87_CR15","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 3319\u20133328. JMLR.org (2017)"},{"issue":"5","key":"87_CR16","doi-asserted-by":"publisher","first-page":"2370","DOI":"10.1002\/hbm.23524","volume":"38","author":"R Yu","year":"2017","unstructured":"Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., Shen, D.: Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification. Hum. Brain Mapp. 38(5), 2370\u20132383 (2017)","journal-title":"Hum. Brain Mapp."},{"issue":"2","key":"87_CR17","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. Nanobiosci. 14(2), 237\u2013247 (2015)","journal-title":"IEEE Trans. Nanobiosci."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32248-9_87","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:26:37Z","timestamp":1728519997000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_87"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_87","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":"10 October 2019","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":"Shenzhen","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"3.07","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":"6.31","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}