{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T17:42:47Z","timestamp":1769276567992,"version":"3.49.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322533","type":"print"},{"value":"9783030322540","type":"electronic"}],"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\/978-3-030-32254-0_56","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"503-511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Brain Dynamics Through the Lens of Statistical Mechanics by Unifying Structure and Function"],"prefix":"10.1007","author":[{"given":"Igor","family":"Fortel","sequence":"first","affiliation":[]},{"given":"Mitchell","family":"Butler","sequence":"additional","affiliation":[]},{"given":"Laura E.","family":"Korthauer","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Olusola","family":"Ajilore","sequence":"additional","affiliation":[]},{"given":"Ira","family":"Driscoll","sequence":"additional","affiliation":[]},{"given":"Anastasios","family":"Sidiropoulos","sequence":"additional","affiliation":[]},{"given":"Yanfu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Schonfeld","sequence":"additional","affiliation":[]},{"given":"Alex","family":"Leow","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"1","key":"56_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1207\/s15516709cog0901_7","volume":"9","author":"DH Ackley","year":"1985","unstructured":"Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cogn. Sci. 9(1), 147\u2013169 (1985)","journal-title":"Cogn. Sci."},{"key":"56_CR2","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/S0896-6273(03)00851-1","volume":"41","author":"D Badre","year":"2004","unstructured":"Badre, D., Wagner, A.D.: Selection, integration, and conflict monitoring; assessing the nature and generality of prefrontal cognitive control mechanisms. Neuron 41, 473\u2013487 (2004)","journal-title":"Neuron"},{"issue":"3","key":"56_CR3","doi-asserted-by":"publisher","first-page":"179","DOI":"10.2307\/2987782","volume":"24","author":"J Besag","year":"1975","unstructured":"Besag, J.: Statistical analysis of non-lattice data. Statistician 24(3), 179 (1975)","journal-title":"Statistician"},{"key":"56_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03163-2","volume-title":"Monte Carlo Simulation in Statistical Physics: An Introduction","author":"K Binder","year":"2010","unstructured":"Binder, K., Heermann, D.W.: Monte Carlo Simulation in Statistical Physics: An Introduction. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-03163-2"},{"issue":"5","key":"56_CR5","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1002\/nbm.3502","volume":"29","author":"NG Dowell","year":"2016","unstructured":"Dowell, N.G., Evans, S.L., Tofts, P.S., King, S.L., Tabet, N., Rusted, J.M.: Structural and resting-state MRI detects regional brain differences in young and mid-age healthy APOE-e4 carriers compared with non-APOE-e4 carriers. NMR in Biomed. 29(5), 614\u2013624 (2016)","journal-title":"NMR in Biomed."},{"issue":"2096","key":"56_CR6","doi-asserted-by":"publisher","first-page":"20160287","DOI":"10.1098\/rsta.2016.0287","volume":"375","author":"T Ezaki","year":"2017","unstructured":"Ezaki, T., Watanabe, T., Ohzeki, M., Masuda, N.: Energy landscape analysis of neuroimaging data. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 375(2096), 20160287 (2017)","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"issue":"6","key":"56_CR7","doi-asserted-by":"publisher","first-page":"061922","DOI":"10.1103\/PhysRevE.79.061922","volume":"79","author":"D Fraiman","year":"2009","unstructured":"Fraiman, D., Balenzuela, P., Foss, J., Chialvo, D.R.: Ising-like dynamics in large-scale functional brain networks. Phys. Rev. E 79(6), 061922 (2009)","journal-title":"Phys. Rev. E"},{"key":"56_CR8","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1126\/science.1088545","volume":"302","author":"E Koechlin","year":"2003","unstructured":"Koechlin, E., Ody, C., Kouneiher, F.: The architecture of cognitive control in the human prefrontal cortex. Science 302, 1181\u20131185 (2003)","journal-title":"Science"},{"key":"56_CR9","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.neuroimage.2018.05.052","volume":"178","author":"L Korthauer","year":"2018","unstructured":"Korthauer, L., Zhan, L., Ajilore, O., Leow, A., Driscoll, I.: Disrupted topology of the resting state structural connectome in middle-aged APOE $$\\upvarepsilon $$4 carriers. Neuroimage 178, 295\u2013305 (2018)","journal-title":"Neuroimage"},{"key":"56_CR10","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1126\/science.288.5472.1835","volume":"288","author":"AW MacDonald 3rd","year":"2000","unstructured":"MacDonald 3rd, A.W., Cohen, J.D., Stenger, V.A., Carter, C.S.: Dissociating the role of the dorsolateral prefrontal and anterior cingulate cortex in cognitive control. Science 288, 1835\u20131838 (2000)","journal-title":"Science"},{"issue":"4","key":"56_CR11","doi-asserted-by":"publisher","first-page":"e93616","DOI":"10.1371\/journal.pone.0093616","volume":"9","author":"D Marinazzo","year":"2014","unstructured":"Marinazzo, D., Pellicoro, M., Wu, G., Angelini, L., Cort\u00e9s, J.M., Stramaglia, S.: Information transfer and criticality in the ising model on the human connectome. PLoS ONE 9(4), e93616 (2014)","journal-title":"PLoS ONE"},{"issue":"3","key":"56_CR12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1080\/00018732.2017.1341604","volume":"66","author":"HC Nguyen","year":"2017","unstructured":"Nguyen, H.C., Zecchina, R., Berg, J.: Inverse statistical prob-lems: from the inverse Ising problem to data science. Adv. Phys. 66(3), 197\u2013261 (2017)","journal-title":"Adv. Phys."},{"issue":"1","key":"56_CR13","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/0165-0173(94)00007-C","volume":"20","author":"A Parent","year":"1995","unstructured":"Parent, A., Hazrati, L.: Functional anatomy of the basal ganglia. I. the cortico-basal ganglia-thalamo-cortical loop. Brain Res. Rev. 20(1), 91\u2013127 (1995)","journal-title":"Brain Res. Rev."},{"issue":"4","key":"56_CR14","doi-asserted-by":"publisher","first-page":"1834","DOI":"10.1093\/cercor\/bhz016","volume":"29","author":"AL Petrache","year":"2019","unstructured":"Petrache, A.L.: Aberrant excitatory-inhibitory synaptic mechanisms in entorhinal cortex microcircuits during the pathogenesis of alzheimer\u2019s disease. Cereb. Cortex 29(4), 1834\u20131850 (2019)","journal-title":"Cereb. Cortex"},{"issue":"1","key":"56_CR15","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1038\/s41598-017-18729-5","volume":"8","author":"S Ren","year":"2018","unstructured":"Ren, S., et al.: Amyloid $$\\upbeta $$ causes excitation\/inhibition imbalance through dopamine receptor 1-dependent disruption of fast-spiking GABAergic input in anterior cingulate cortex. Sci. Rep. 8(1), 302 (2018)","journal-title":"Sci. Rep."},{"issue":"7087","key":"56_CR16","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1038\/nature04701","volume":"440","author":"E Schneidman","year":"2006","unstructured":"Schneidman, E., Berry, M.J., Segev, R., Bialek, W.: Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440(7087), 1007\u20131012 (2006)","journal-title":"Nature"},{"issue":"1","key":"56_CR17","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1038\/ncomms2388","volume":"4","author":"T Watanabe","year":"2013","unstructured":"Watanabe, T., et al.: A pairwise maximum entropy model accurately describes resting-state human brain networks. Nat. Commun. 4(1), 1370 (2013)","journal-title":"Nat. Commun."},{"issue":"1","key":"56_CR18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3390\/e12010089","volume":"12","author":"F Yeh","year":"2010","unstructured":"Yeh, F., et al.: Maximum entropy approaches to living neural networks. Entropy 12(1), 89\u2013106 (2010)","journal-title":"Entropy"}],"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\/978-3-030-32254-0_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:13:39Z","timestamp":1728519219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32254-0_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322533","9783030322540"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32254-0_56","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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"}]}}