{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:11:33Z","timestamp":1742911893620,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030592769"},{"type":"electronic","value":"9783030592776"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59277-6_11","type":"book-chapter","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T19:02:32Z","timestamp":1600455752000},"page":"118-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of PTSD and Non-PTSD Using Cortical Structural Measures in Machine Learning Analyses\u2014Preliminary Study of ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup"],"prefix":"10.1007","author":[{"name":"ENIGMA-Psychiatric Genomics Consortium PTSD Workgroup","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2993-7003","authenticated-orcid":false,"given":"Brian","family":"O\u2019Leary","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2316-7415","authenticated-orcid":false,"given":"Chia-Hao","family":"Shih","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2900-9087","authenticated-orcid":false,"given":"Tian","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5762-6456","authenticated-orcid":false,"given":"Hong","family":"Xie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4787-4528","authenticated-orcid":false,"given":"Andrew S.","family":"Cotton","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2029-5905","authenticated-orcid":false,"given":"Kevin S.","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6517-6969","authenticated-orcid":false,"given":"Rajendra","family":"Morey","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1524-2902","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,15]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1177\/070674371405900902","volume":"59","author":"J Sareen","year":"2014","unstructured":"Sareen, J.: Posttraumatic stress disorder in adults: impact, comorbidity, risk factors, and treatment. Can. J. Psychiatry 59, 460\u2013467 (2014). https:\/\/doi.org\/10.1177\/070674371405900902","journal-title":"Can. J. Psychiatry"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/978-1-4939-7148-0_12","volume-title":"Sleep and Combat-Related Post Traumatic Stress Disorder","author":"I Liberzon","year":"2018","unstructured":"Liberzon, I., Wang, X., Xie, H.: Brain structural abnormalities in posttraumatic stress disorder and relations with sleeping problems. In: Vermetten, E., Germain, A., Neylan, T.C. (eds.) Sleep and Combat-Related Post Traumatic Stress Disorder, pp. 145\u2013167. Springer, New York (2018). https:\/\/doi.org\/10.1007\/978-1-4939-7148-0_12"},{"key":"11_CR3","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1503\/jpn.100010","volume":"36","author":"C Eckart","year":"2011","unstructured":"Eckart, C., Stoppel, C., et al.: Structural alterations in lateral prefrontal, parietal and posterior midline regions of men with chronic posttraumatic stress disorder. J. Psychiatry Neurosci. 36, 176 (2011)","journal-title":"J. Psychiatry Neurosci."},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1097\/01.wnr.0000071767.24455.10","volume":"14","author":"SL Rauch","year":"2003","unstructured":"Rauch, S.L., et al.: Selectively reduced regional cortical volumes in post-traumatic stress disorder. NeuroReport 14, 913\u2013916 (2003)","journal-title":"NeuroReport"},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.jad.2005.11.006","volume":"90","author":"N Kitayama","year":"2006","unstructured":"Kitayama, N., Quinn, S., Bremner, J.D.: Smaller volume of anterior cingulate cortex in abuse-related posttraumatic stress disorder. J. Affect. Disord. 90, 171\u2013174 (2006)","journal-title":"J. Affect. Disord."},{"key":"11_CR6","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.pscychresns.2013.03.002","volume":"213","author":"L Chao","year":"2013","unstructured":"Chao, L., Weiner, M., Neylan, T.: Regional cerebral volumes in veterans with current versus remitted posttraumatic stress disorder. Psychiatry Res. Neuroimaging 213, 193\u2013201 (2013)","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"11_CR7","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.neuron.2016.09.039","volume":"92","author":"I Liberzon","year":"2016","unstructured":"Liberzon, I., Abelson, J.L.: Context processing and the neurobiology of post-traumatic stress disorder. Neuron 92, 14\u201330 (2016). https:\/\/doi.org\/10.1016\/j.neuron.2016.09.039","journal-title":"Neuron"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"13435","DOI":"10.1523\/JNEUROSCI.4287-13.2014","volume":"34","author":"SN Garfinkel","year":"2014","unstructured":"Garfinkel, S.N., et al.: Impaired contextual modulation of memories in PTSD: an fMRI and psychophysiological study of extinction retention and fear renewal. J. Neurosci. 34, 13435\u201313443 (2014)","journal-title":"J. Neurosci."},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1038\/npp.2015.255","volume":"41","author":"JA Greco","year":"2016","unstructured":"Greco, J.A., Liberzon, I.: Neuroimaging of fear-associated learning. Neuropsychopharmacol. 41, 320\u2013334 (2016)","journal-title":"Neuropsychopharmacol."},{"key":"11_CR10","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1002\/wps.20150","volume":"13","author":"RC Kessler","year":"2014","unstructured":"Kessler, R.C., et al.: How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys. World Psychiatry 13, 265\u2013274 (2014)","journal-title":"World Psychiatry"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/1744-859X-9-32","volume":"9","author":"DN Ditlevsen","year":"2010","unstructured":"Ditlevsen, D.N., Elklit, A.: The combined effect of gender and age on post traumatic stress disorder: do men and women show differences in the lifespan distribution of the disorder? Ann. Gen. Psychiatry 9, 32 (2010). https:\/\/doi.org\/10.1186\/1744-859X-9-32","journal-title":"Ann. Gen. Psychiatry"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.jpsychires.2014.08.017","volume":"59","author":"IR Galatzer-Levy","year":"2014","unstructured":"Galatzer-Levy, I.R., Karstoft, K.-I., Statnikov, A., Shalev, A.Y.: Quantitative forecasting of PTSD from early trauma responses: a machine learning application. J. Psychiatry Res. 59, 68\u201376 (2014)","journal-title":"J. Psychiatry Res."},{"key":"11_CR13","first-page":"4","volume":"12","author":"NS Mor","year":"2018","unstructured":"Mor, N.S., Dardeck, K.L.: Quantitative forecasting of risk for PTSD using ecological factors: a deep learning application. J. Soc. Behav. Health Sci. 12, 4 (2018)","journal-title":"J. Soc. Behav. Health Sci."},{"key":"11_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-030-00919-9_8","volume-title":"Machine Learning in Medical Imaging","author":"JS Choi","year":"2018","unstructured":"Choi, J.S., Lee, E., Suk, Hl: Regional abnormality representation learning in structural MRI for AD\/MCI diagnosis. In: Shi, Y., Suk, Hl, Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 64\u201372. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00919-9_8"},{"key":"11_CR15","doi-asserted-by":"publisher","unstructured":"A. Nunes, et al: Using structural MRI to identify bipolar disorders \u2013 13 site machine learning study in 3020 individuals from the ENIGMA bipolar disorders working group. Mol. Psychiatry, 1\u201314 (2018). https:\/\/doi.org\/10.1038\/s41380-018-0228-9","DOI":"10.1038\/s41380-018-0228-9"},{"key":"11_CR16","doi-asserted-by":"publisher","first-page":"4161","DOI":"10.1038\/s41598-018-22277-x","volume":"8","author":"JS Lee","year":"2018","unstructured":"Lee, J.S., et al.: Machine learning-based individual assessment of cortical atrophy pattern in alzheimer\u2019s disease spectrum: development of the classifier and longitudinal evaluation. Sci. Rep. 8, 4161 (2018)","journal-title":"Sci. Rep."},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Menikdiwela, M., Nguyen, C., Shaw, M.: Deep learning on brain cortical thickness data for disease classification. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1\u20135. IEEE (2018)","DOI":"10.1109\/DICTA.2018.8615775"},{"key":"11_CR18","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.jpsychires.2019.12.001","volume":"121","author":"LF Ramos-Lima","year":"2020","unstructured":"Ramos-Lima, L.F., Waikamp, V., Antonelli-Salgado, T., Passos, I.C., Freitas, L.H.M.: The use of machine learning techniques in trauma-related disorders: a systematic review. J. Psychiatr. Res. 121, 159\u2013172 (2020). https:\/\/doi.org\/10.1016\/j.jpsychires.2019.12.001","journal-title":"J. Psychiatr. Res."},{"key":"11_CR19","doi-asserted-by":"publisher","DOI":"10.1111\/acps.13029","volume-title":"Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging","author":"SN Gosnell","year":"2019","unstructured":"Gosnell, S.N., Fowler, J.C., Salas, R.: Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging. Acta Psychiatry, Scand (2019)"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1038\/mp.2016.110","volume":"22","author":"RC Kessler","year":"2017","unstructured":"Kessler, R.C., et al.: Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Mol. Psychiatry. 22, 544\u2013551 (2017)","journal-title":"Mol. Psychiatry."},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: FreeSurfer. NeuroImage. 62, 774\u2013781 (2012). https:\/\/doi.org\/10.1016\/j.neuroimage.2012.01.021","journal-title":"NeuroImage."},{"key":"11_CR22","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, 968\u2013980 (2006). https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.021","journal-title":"NeuroImage."},{"key":"11_CR23","unstructured":"Genetics Protocols\u2009\u00ab\u2009\u202fENIGMA, (n.d.). http:\/\/enigma.ini.usc.edu\/protocols\/genetics-protocols\/. Accessed 15 June 2020"},{"key":"11_CR24","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"11_CR25","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67(2), 301\u2013320 (2005)","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"11_CR27","unstructured":"Kingma, D.P., Ba, J., Adam: A method for stochastic optimization. ArXiv14126980 Cs (2017). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"11_CR28","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1503\/jpn.140142","volume":"40","author":"T Hajek","year":"2015","unstructured":"Hajek, T., Cooke, C., Kopecek, M., Novak, T., Hoschl, C., Alda, M.: Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study. J. Psychiatry Neurosci. JPN. 40, 316\u2013324 (2015). https:\/\/doi.org\/10.1503\/jpn.140142","journal-title":"J. Psychiatry Neurosci. JPN."},{"key":"11_CR29","doi-asserted-by":"publisher","first-page":"e6353","DOI":"10.1371\/journal.pone.0006353","volume":"4","author":"SG Costafreda","year":"2009","unstructured":"Costafreda, S.G., Chu, C., Ashburner, J., Fu, C.H.: Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4, e6353 (2009)","journal-title":"PLoS ONE"},{"key":"11_CR30","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1016\/j.neuroimage.2010.11.079","volume":"55","author":"Q Gong","year":"2011","unstructured":"Gong, Q., et al.: Prognostic prediction of therapeutic response in depression using high-field MR imaging. Neuroimage. 55, 1497\u20131503 (2011)","journal-title":"Neuroimage."},{"key":"11_CR31","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.neuroimage.2009.08.024","volume":"49","author":"C Ecker","year":"2010","unstructured":"Ecker, C., et al.: Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49, 44\u201356 (2010)","journal-title":"Neuroimage"},{"key":"11_CR32","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.biopsych.2017.09.006","volume":"83","author":"MW Logue","year":"2018","unstructured":"Logue, M.W., et al.: Smaller hippocampal volume in posttraumatic stress disorder: a multisite ENIGMA-PGC study: subcortical volumetry results from posttraumatic stress disorder consortia. Biol. Psychiatry 83, 244\u2013253 (2018). https:\/\/doi.org\/10.1016\/j.biopsych.2017.09.006","journal-title":"Biol. Psychiatry"},{"key":"11_CR33","doi-asserted-by":"publisher","first-page":"e13946","DOI":"10.2196\/13946","volume":"6","author":"S Wshah","year":"2019","unstructured":"Wshah, S., Skalka, C., Price, M.: Predicting posttraumatic stress disorder risk: a machine learning approach. JMIR Ment. Health. 6, e13946 (2019). https:\/\/doi.org\/10.2196\/13946","journal-title":"JMIR Ment. Health."},{"key":"11_CR34","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.bpsc.2015.12.005","volume":"1","author":"VD Calhoun","year":"2016","unstructured":"Calhoun, V.D., Sui, J.: Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 1, 230\u2013244 (2016). https:\/\/doi.org\/10.1016\/j.bpsc.2015.12.005","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging."},{"key":"11_CR35","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neuroimage.2014.05.018","volume":"102","author":"K Uluda\u011f","year":"2014","unstructured":"Uluda\u011f, K., Roebroeck, A.: General overview on the merits of multimodal neuroimaging data fusion. NeuroImage. 102, 3\u201310 (2014). https:\/\/doi.org\/10.1016\/j.neuroimage.2014.05.018","journal-title":"NeuroImage."}],"container-title":["Lecture Notes in Computer Science","Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59277-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T23:17:01Z","timestamp":1619306221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59277-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030592769","9783030592776"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59277-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"15 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Padua","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"brain2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.bi2020.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Cyberchair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57","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":"33","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":"58% - 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":"3-5","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 conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}