{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T05:24:43Z","timestamp":1781846683970,"version":"3.54.5"},"reference-count":129,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T00:00:00Z","timestamp":1487808000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["MH084029"],"award-info":[{"award-number":["MH084029"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["MH074813"],"award-info":[{"award-number":["MH074813"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health (US)","doi-asserted-by":"crossref","award":["3U01MH092250-03S1"],"award-info":[{"award-number":["3U01MH092250-03S1"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000027","name":"National Institute on Alcohol Abuse and Alcoholism","doi-asserted-by":"publisher","award":["NCANDA-USA Consortium BD2K"],"award-info":[{"award-number":["NCANDA-USA Consortium BD2K"]}],"id":[{"id":"10.13039\/100000027","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["1R01EB020740-01A1"],"award-info":[{"award-number":["1R01EB020740-01A1"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["1P41EB019936-01A1"],"award-info":[{"award-number":["1P41EB019936-01A1"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["3R01MH092380-04S2"],"award-info":[{"award-number":["3R01MH092380-04S2"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["1U01MH108168-01"],"award-info":[{"award-number":["1U01MH108168-01"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"DOI":"10.1371\/journal.pcbi.1005350","type":"journal-article","created":{"date-parts":[[2017,2,23]],"date-time":"2017-02-23T13:33:17Z","timestamp":1487856797000},"page":"e1005350","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":732,"title":["Mindboggling morphometry of human brains"],"prefix":"10.1371","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0707-2889","authenticated-orcid":true,"given":"Arno","family":"Klein","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5312-6729","authenticated-orcid":true,"given":"Satrajit S.","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Forrest S.","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Giard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0698-0295","authenticated-orcid":true,"given":"Yrj\u00f6","family":"H\u00e4me","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eliezer","family":"Stavsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noah","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Rossa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Reuter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elias","family":"Chaibub Neto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3554-043X","authenticated-orcid":true,"given":"Anisha","family":"Keshavan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2017,2,23]]},"reference":[{"issue":"7","key":"ref1","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1001\/archpsyc.62.7.761","article-title":"Neural activation patterns of methamphetamine-dependent subjects during decision making predict relapse","volume":"62","author":"MP Paulus","year":"2005","journal-title":"Arch Gen Psychiatry [Internet]"},{"issue":"7","key":"ref2","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1001\/archgenpsychiatry.2009.62","article-title":"Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition","volume":"66","author":"N Koutsouleris","year":"2009","journal-title":"Arch Gen Psychiatry [Internet]"},{"issue":"5","key":"ref3","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.biopsych.2005.11.013","article-title":"Functional imaging as a predictor of schizophrenia","volume":"60","author":"HC Whalley","year":"2006","journal-title":"Biol Psychiatry [Internet]"},{"issue":"12","key":"ref4","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1097\/01.wnr.0000174407.09515.cc","article-title":"Amygdala reactivity to emotional faces predicts improvement in major depression","volume":"16","author":"T Canli","year":"2005","journal-title":"Neuroreport [Internet]"},{"issue":"5","key":"ref5","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.biopsych.2006.09.018","article-title":"Brain imaging correlates of depressive symptom severity and predictors of symptom improvement after antidepressant treatment","volume":"62","author":"C-H Chen","year":"2007","journal-title":"Biol Psychiatry [Internet]"},{"issue":"4","key":"ref6","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1176\/ajp.2007.164.4.599","article-title":"Neural responses to happy facial expressions in major depression following antidepressant treatment","volume":"164","author":"CHY Fu","year":"2007","journal-title":"Am J Psychiatry [Internet]"},{"issue":"3","key":"ref7","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1176\/appi.ajp.2008.07101682","article-title":"Anticipatory activation in the amygdala and anterior cingulate in generalized anxiety disorder and prediction of treatment response","volume":"166","author":"JB Nitschke","year":"2009","journal-title":"Am J Psychiatry [Internet]"},{"issue":"6","key":"ref8","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.biopsych.2009.04.036","article-title":"Dorsolateral prefrontal cortex activity predicts responsiveness to cognitive-behavioral therapy in schizophrenia","volume":"66","author":"V Kumari","year":"2009","journal-title":"Biol Psychiatry [Internet]"},{"issue":"1","key":"ref9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1001\/2013.jamapsychiatry.5","article-title":"Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging","volume":"70","author":"O Doehrmann","year":"2013","journal-title":"JAMA Psychiatry [Internet]"},{"key":"ref10","article-title":"Brain connectomics predict response to treatment in social anxiety disorder","author":"S Whitfield-Gabrieli","year":"2015","journal-title":"Mol Psychiatry [Internet]"},{"issue":"1","key":"ref11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neuron.2014.10.047","article-title":"Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience","volume":"85","author":"JDE Gabrieli","year":"2015","journal-title":"Neuron [Internet]"},{"issue":"11","key":"ref12","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1016\/j.biopsych.2009.10.008","article-title":"Endophenotypes: bridging genomic complexity and disorder heterogeneity","volume":"66","author":"TR Insel","year":"2009","journal-title":"Biol Psychiatry [Internet]"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/11566465_13","article-title":"Generalised overlap measures for assessment of pairwise and groupwise image registration and segmentation","author":"WR Crum","year":"2005","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2005 [Internet]. Springer"},{"issue":"4","key":"ref14","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/j.neuroimage.2006.09.055","article-title":"In praise of tedious anatomy","volume":"37","author":"JT Devlin","year":"2007","journal-title":"Neuroimage [Internet]"},{"issue":"9","key":"ref15","doi-asserted-by":"crossref","first-page":"e7200","DOI":"10.1371\/journal.pone.0007200","article-title":"The brain atlas concordance problem: quantitative comparison of anatomical parcellations","volume":"4","author":"JW Bohland","year":"2009","journal-title":"PLoS One [Internet]"},{"issue":"3","key":"ref16","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","article-title":"Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration","volume":"46","author":"A Klein","year":"2009","journal-title":"Neuroimage [Internet]"},{"key":"ref17","unstructured":"Avants B, Klein A, Tustison N, Wu J, Gee JC. Evaluation of open-access, automated brain extraction methods on multi-site multi-disorder data. In: 16th annual meeting for the Organization of Human Brain Mapping. 2010."},{"issue":"1","key":"ref18","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neuroimage.2010.01.091","article-title":"Evaluation of volume-based and surface-based brain image registration methods","volume":"51","author":"A Klein","year":"2010","journal-title":"Neuroimage [Internet]"},{"issue":"2","key":"ref19","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.neuroimage.2004.09.016","article-title":"Mindboggle: a scatterbrained approach to automate brain labeling","volume":"24","author":"A Klein","year":"2005","journal-title":"Neuroimage [Internet]"},{"key":"ref20","unstructured":"Rogelj P, Kovacic S, Gee JC. Validation of a nonrigid registration algorithm for multimodal data. In: Sonka M, Fitzpatrick JM, editors. Medical Imaging 2002 [Internet]. International Society for Optics and Photonics; 2002 [cited 2016 Aug 6]. p. 299\u2013307. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/proceedings.spiedigitallibrary.org\/proceeding.aspx?doi=10.1117\/12.467170\" xlink:type=\"simple\">http:\/\/proceedings.spiedigitallibrary.org\/proceeding.aspx?doi=10.1117\/12.467170<\/ext-link>"},{"issue":"4","key":"ref21","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1093\/cercor\/3.4.313","article-title":"Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology","volume":"3","author":"J Rademacher","year":"1993","journal-title":"Cereb Cortex [Internet]"},{"key":"ref22","first-page":"171","article-title":"101 labeled brain images and a consistent human cortical labeling protocol","volume":"6","author":"A Klein","year":"2012","journal-title":"Front Neurosci [Internet]"},{"key":"ref23","unstructured":"Klein A, Dal Canton T, Ghosh SS, Landman B, Worth A. Open labels: online feedback for a public resource of manually labeled brain images. In: 16th annual meeting for the Organization of Human Brain Mapping [Internet]. 2010. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/mfr.osf.io\/render?url=https:\/\/osf.io\/tmjbn\/?action=download%26mode=render\" xlink:type=\"simple\">https:\/\/mfr.osf.io\/render?url=https:\/\/osf.io\/tmjbn\/?action=download%26mode=render<\/ext-link>"},{"key":"ref24","article-title":"A multi-modal parcellation of human cerebral cortex","author":"MF Glasser","year":"2016","journal-title":"Nature [Internet]"},{"issue":"1","key":"ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2176\/nmc.45.1","article-title":"\u201cSulcal root\u201d generic model: a hypothesis to overcome the variability of the human cortex folding patterns","volume":"45","author":"J R\u00e9gis","year":"2005","journal-title":"Neurol Med Chir [Internet]"},{"issue":"6","key":"ref26","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1093\/cercor\/bhm174","article-title":"Deep sulcal landmarks provide an organizing framework for human cortical folding","volume":"18","author":"G Lohmann","year":"2008","journal-title":"Cereb Cortex [Internet]"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.neuroimage.2014.06.004","article-title":"Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants","volume":"100","author":"Y Meng","year":"2014","journal-title":"Neuroimage [Internet]"},{"issue":"4","key":"ref28","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.media.2011.02.008","article-title":"Cortical sulci recognition and spatial normalization","volume":"15","author":"M Perrot","year":"2011","journal-title":"Med Image Anal"},{"key":"ref29","unstructured":"Dryden IL, Mardia KV. Statistical shape analysis. 1998; <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/pdfs.semanticscholar.org\/6ba2\/73a7cfa282f73423110d00a5d20ad36766e1.pdf\" xlink:type=\"simple\">https:\/\/pdfs.semanticscholar.org\/6ba2\/73a7cfa282f73423110d00a5d20ad36766e1.pdf<\/ext-link>"},{"issue":"12","key":"ref30","doi-asserted-by":"crossref","first-page":"e50698","DOI":"10.1371\/journal.pone.0050698","article-title":"Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses","volume":"7","author":"R Bansal","year":"2012","journal-title":"PLoS One [Internet]"},{"issue":"1","key":"ref31","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neurobiolaging.2006.09.013","article-title":"Automated cortical thickness measurements from MRI can accurately separate Alzheimer\u2019s patients from normal elderly controls","volume":"29","author":"JP Lerch","year":"2008","journal-title":"Neurobiol Aging [Internet]"},{"issue":"1","key":"ref32","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neuroimage.2008.07.016","article-title":"Sulcal morphology changes and their relationship with cortical thickness and gyral white matter volume in mild cognitive impairment and Alzheimer\u2019s disease","volume":"43","author":"K Im","year":"2008","journal-title":"Neuroimage [Internet]"},{"issue":"4","key":"ref33","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.3233\/JAD-2010-100114","article-title":"Differences in cortical thickness in healthy controls, subjects with mild cognitive impairment, and Alzheimer\u2019s disease patients: a longitudinal study","volume":"21","author":"V Julkunen","year":"2010","journal-title":"J Alzheimers Dis [Internet]"},{"issue":"Pt 1","key":"ref34","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1093\/brain\/awv337","article-title":"Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant","volume":"139","author":"MD Steenwijk","year":"2016","journal-title":"Brain [Internet]"},{"issue":"9","key":"ref35","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1001\/archpsyc.60.9.878","article-title":"Regionally localized thinning of the cerebral cortex in schizophrenia","volume":"60","author":"GR Kuperberg","year":"2003","journal-title":"Arch Gen Psychiatry [Internet]"},{"issue":"2","key":"ref36","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.neuroimage.2009.12.047","article-title":"Predictive models of autism spectrum disorder based on brain regional cortical thickness","volume":"50","author":"Y Jiao","year":"2010","journal-title":"Neuroimage [Internet]"},{"issue":"6","key":"ref37","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1111\/j.1530-0277.2011.01452.x","article-title":"Cortical thickness, surface area, and volume of the brain reward system in alcohol dependence: relationships to relapse and extended abstinence","volume":"35","author":"TC Durazzo","year":"2011","journal-title":"Alcohol Clin Exp Res [Internet]"},{"issue":"12","key":"ref38","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1212\/01.wnl.0000340981.97664.2f","article-title":"The cortical signature of prodromal AD: regional thinning predicts mild AD dementia","volume":"72","author":"A Bakkour","year":"2009","journal-title":"Neurology [Internet]"},{"issue":"Pt 8","key":"ref39","doi-asserted-by":"crossref","first-page":"2036","DOI":"10.1093\/brain\/awp105","article-title":"Early diagnosis of Alzheimer\u2019s disease using cortical thickness: impact of cognitive reserve","volume":"132","author":"O Querbes","year":"2009","journal-title":"Brain [Internet]"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.neuroimage.2012.09.058","article-title":"Prediction of Alzheimer\u2019s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning","volume":"65","author":"SF Eskildsen","year":"2013","journal-title":"Neuroimage [Internet]"},{"issue":"12","key":"ref41","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1002\/hbm.22156","article-title":"Prediction of Alzheimer\u2019s disease and mild cognitive impairment using cortical morphological patterns","volume":"34","author":"C-Y Wee","year":"2013","journal-title":"Hum Brain Mapp [Internet]"},{"key":"ref42","first-page":"76","article-title":"Prediction of Conversion from Mild Cognitive Impairment to Alzheimer\u2019s Disease Using MRI and Structural Network Features","volume":"8","author":"R Wei","year":"2016","journal-title":"Front Aging Neurosci [Internet]"},{"issue":"2","key":"ref43","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1212\/WNL.0b013e31823efc6c","article-title":"MRI cortical thickness biomarker predicts AD-like CSF and cognitive decline in normal adults","volume":"78","author":"BC Dickerson","year":"2012","journal-title":"Neurology [Internet]"},{"issue":"3","key":"ref44","first-page":"755","article-title":"Entorhinal Cortex Thickness Predicts Cognitive Decline in Alzheimer\u2019s Disease","author":"V Latha","year":"2013","journal-title":"J Alzheimers Dis [Internet]"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.ijdevneu.2015.07.007","article-title":"Cortical thickness predicts the first onset of major depression in adolescence","volume":"46","author":"LC Foland-Ross","year":"2015","journal-title":"Int J Dev Neurosci [Internet]"},{"issue":"5","key":"ref46","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1001\/archpsyc.63.5.540","article-title":"Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit\/hyperactivity disorder","volume":"63","author":"P Shaw","year":"2006","journal-title":"Arch Gen Psychiatry [Internet]"},{"issue":"32","key":"ref47","doi-asserted-by":"crossref","first-page":"10612","DOI":"10.1523\/JNEUROSCI.5413-09.2010","article-title":"Describing the brain in autism in five dimensions\u2014magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach","volume":"30","author":"C Ecker","year":"2010","journal-title":"J Neurosci [Internet]"},{"issue":"6, Supplement","key":"ref48","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/S1053-8119(01)91441-7","article-title":"BrainVISA: Software platform for visualization and analysis of multi-modality brain data","volume":"13","author":"Y Cointepas","year":"2001","journal-title":"Neuroimage [Internet]"},{"issue":"1","key":"ref49","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s12021-011-9127-9","article-title":"A library of cortical morphology analysis tools to study development, aging and genetics of cerebral cortex","volume":"10","author":"P Kochunov","year":"2012","journal-title":"Neuroinformatics [Internet]"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.neuroimage.2013.02.047","article-title":"Longitudinal changes in sulcal morphology associated with late-life aging and MCI","volume":"74","author":"T Liu","year":"2013","journal-title":"Neuroimage [Internet]"},{"issue":"3","key":"ref51","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.neuroimage.2007.08.049","article-title":"Cortical folding abnormalities in schizophrenia patients with resistant auditory hallucinations","volume":"39","author":"A Cachia","year":"2008","journal-title":"Neuroimage [Internet]"},{"issue":"4","key":"ref52","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/j.1399-5618.2009.00683.x","article-title":"Cortical folding difference between patients with early-onset and patients with intermediate-onset bipolar disorder","volume":"11","author":"J Penttil\u00e4","year":"2009","journal-title":"Bipolar Disord [Internet]"},{"issue":"4","key":"ref53","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1097\/WCO.0b013e32833a0afc","article-title":"In-vivo measurement of cortical morphology: means and meanings","volume":"23","author":"J-F Mangin","year":"2010","journal-title":"Curr Opin Neurol [Internet]"},{"issue":"7","key":"ref54","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1001\/archgenpsychiatry.2011.60","article-title":"Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder","volume":"68","author":"MJ Kempton","year":"2011","journal-title":"Arch Gen Psychiatry [Internet]"},{"key":"ref55","doi-asserted-by":"crossref","unstructured":"Mikhno A, Nuevo PM, Devanand DP, Parsey RV, Laine AF. Multimodal classification of Dementia using functional data, anatomical features and 3D invariant shape descriptors. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) [Internet]. IEEE; 2012 [cited 2016 Aug 6]. p. 606\u20139. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/dx.doi.org\/10.1109\/ISBI.2012.6235621\" xlink:type=\"simple\">http:\/\/dx.doi.org\/10.1109\/ISBI.2012.6235621<\/ext-link>","DOI":"10.1109\/ISBI.2012.6235621"},{"key":"ref56","first-page":"079145","article-title":"BIDS Apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods [Internet]","author":"KJ Gorgolewski","year":"2016","journal-title":"bioRxiv"},{"key":"ref57","first-page":"13","article-title":"Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python","volume":"5","author":"K Gorgolewski","year":"2011","journal-title":"Front Neuroinform [Internet]"},{"key":"ref58","article-title":"Automated brain labeling with Mindboggle","author":"A Klein","year":"2004"},{"key":"ref59","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2342-5-7","article-title":"Mindboggle: automated brain labeling with multiple atlases","volume":"5","author":"A Klein","year":"2005","journal-title":"BMC Med Imaging [Internet]"},{"issue":"6","key":"ref60","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1162\/jocn.1996.8.6.566","article-title":"MRI-Based Topographic Parcellation of Human Neocortex: An Anatomically Specified Method with Estimate of Reliability","volume":"8","author":"VS Caviness Jr","year":"1996","journal-title":"J Cogn Neurosci [Internet]"},{"key":"ref61","article-title":"Mindboggle-101 manually labeled individual brains [Internet]","author":"Arno Klein","year":"2016","journal-title":"Harvard Dataverse"},{"key":"ref62","article-title":"Mindboggle-101 templates (unlabeled images from a population of brains) [Internet]","author":"Arno Klein","year":"2016","journal-title":"Harvard Dataverse"},{"key":"ref63","article-title":"Mindboggle-101 atlases (anatomical labels from a population of brains) [Internet]","author":"Arno Klein","year":"2016","journal-title":"Harvard Dataverse"},{"key":"ref64","first-page":"27","article-title":"Multi-atlas segmentation with joint label fusion and corrective learning-an open source implementation","volume":"7","author":"H Wang","year":"2013","journal-title":"Front Neuroinform [Internet]"},{"issue":"2","key":"ref65","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1006\/nimg.1998.0395","article-title":"Cortical surface-based analysis. I. Segmentation and surface reconstruction","volume":"9","author":"AM Dale","year":"1999","journal-title":"Neuroimage [Internet]"},{"issue":"2","key":"ref66","first-page":"195","article-title":"Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system","volume":"9","author":"B Fischl","year":"1999","journal-title":"Neuroimage [Internet]"},{"issue":"4","key":"ref67","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4","article-title":"High-resolution intersubject averaging and a coordinate system for the cortical surface","volume":"8","author":"B Fischl","year":"1999","journal-title":"Hum Brain Mapp [Internet]"},{"key":"ref68","article-title":"Mindboggle: Automated human brain MRI feature extraction, labeling, morphometry, and online visualization","author":"A Klein","year":"2012","journal-title":"Neuroinformatics [Internet]"},{"key":"ref69","article-title":"Mindboggle 2 interface: online visualization of extracted brain features with XTK","volume":"8","author":"K Arno","year":"2014","journal-title":"Front Neuroinform [Internet]"},{"key":"ref70","article-title":"Interactive online brain shape visualization [Internet]","author":"A Keshavan","year":"2016","journal-title":"Interactive online brain shape visualization [Internet]"},{"issue":"4","key":"ref71","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI): MRI methods","volume":"27","author":"CR Jack Jr","year":"2008","journal-title":"J Magn Reson Imaging [Internet]"},{"key":"ref72","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/j.1749-6632.2009.05063.x","article-title":"MRI measures of Alzheimer\u2019s disease and the AddNeuroMed study","volume":"1180","author":"A Simmons","year":"2009","journal-title":"Ann N Y Acad Sci [Internet]"},{"issue":"6","key":"ref73","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.jalz.2016.02.006","article-title":"Crowdsourced estimation of cognitive decline and resilience in Alzheimer\u2019s disease","volume":"12","author":"GI Allen","year":"2016","journal-title":"Alzheimers Dement [Internet]"},{"issue":"5","key":"ref74","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1136\/jamia.2001.0080443","article-title":"An integrated software suite for surface-based analyses of cerebral cortex","volume":"8","author":"DC Van Essen","year":"2001","journal-title":"J Am Med Inform Assoc [Internet]"},{"issue":"3","key":"ref75","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","article-title":"Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain","volume":"33","author":"B Fischl","year":"2002","journal-title":"Neuron [Internet]"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.neuroimage.2014.05.044","article-title":"Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements","volume":"99","author":"NJ Tustison","year":"2014","journal-title":"Neuroimage [Internet]"},{"issue":"4","key":"ref77","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s12021-011-9109-y","article-title":"An open source multivariate framework for n-tissue segmentation with evaluation on public data","volume":"9","author":"BB Avants","year":"2011","journal-title":"Neuroinformatics [Internet]"},{"issue":"4","key":"ref78","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1002\/hbm.20599","article-title":"Evaluation of automated brain MR image segmentation and volumetry methods","volume":"30","author":"F Klauschen","year":"2009","journal-title":"Hum Brain Mapp [Internet]"},{"issue":"3","key":"ref79","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1016\/j.neuroimage.2009.12.028","article-title":"Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies","volume":"53","author":"AM Winkler","year":"2010","journal-title":"Neuroimage [Internet]"},{"issue":"5","key":"ref80","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1002\/hbm.23137","article-title":"Neurobiological origin of spurious brain morphological changes: A quantitative MRI study: Computational Anatomy Studies of the Brain","volume":"37","author":"S Lorio","year":"2016","journal-title":"Hum Brain Mapp [Internet]"},{"issue":"20","key":"ref81","doi-asserted-by":"crossref","first-page":"11050","DOI":"10.1073\/pnas.200033797","article-title":"Measuring the thickness of the human cerebral cortex from magnetic resonance images","volume":"97","author":"B Fischl","year":"2000","journal-title":"Proceedings of the National Academy of Sciences [Internet]"},{"issue":"1","key":"ref82","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.neuroimage.2006.02.051","article-title":"Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer","volume":"32","author":"X Han","year":"2006","journal-title":"Neuroimage [Internet]"},{"key":"ref83","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neuroimage.2012.09.050","article-title":"Cortical thickness and central surface estimation","volume":"65","author":"R Dahnke","year":"2013","journal-title":"Neuroimage [Internet]"},{"key":"ref84","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.neuroimage.2012.12.016","article-title":"Cortical thickness determination of the human brain using high resolution 3T and 7T MRI data","volume":"70","author":"F L\u00fcsebrink","year":"2013","journal-title":"Neuroimage [Internet]"},{"issue":"7","key":"ref85","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1093\/cercor\/bhh032","article-title":"Thinning of the cerebral cortex in aging","volume":"14","author":"DH Salat","year":"2004","journal-title":"Cereb Cortex [Internet]"},{"issue":"9","key":"ref86","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1093\/cercor\/bhn232","article-title":"High consistency of regional cortical thinning in aging across multiple samples","volume":"19","author":"AM Fjell","year":"2009","journal-title":"Cereb Cortex [Internet]"},{"issue":"4","key":"ref87","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1016\/j.neuroimage.2012.03.026","article-title":"Measuring and comparing brain cortical surface area and other areal quantities","volume":"61","author":"AM Winkler","year":"2012","journal-title":"Neuroimage [Internet]"},{"key":"ref88","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.nicl.2014.02.012","article-title":"Increased cortical curvature reflects white matter atrophy in individual patients with early multiple sclerosis","volume":"6","author":"M Deppe","year":"2014","journal-title":"Neuroimage Clin [Internet]"},{"key":"ref89","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.nicl.2016.01.003","article-title":"Mean cortical curvature reflects cytoarchitecture restructuring in mild traumatic brain injury","volume":"11","author":"JB King","year":"2016","journal-title":"Neuroimage Clin [Internet]"},{"issue":"5","key":"ref90","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1142\/S0129065711002948","article-title":"Intrinsic curvature: a marker of millimeter-scale tangential cortico-cortical connectivity?","volume":"21","author":"L Ronan","year":"2011","journal-title":"Int J Neural Syst [Internet]"},{"issue":"8","key":"ref91","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1093\/cercor\/bht082","article-title":"Differential tangential expansion as a mechanism for cortical gyrification","volume":"24","author":"L Ronan","year":"2014","journal-title":"Cereb Cortex [Internet]"},{"issue":"2","key":"ref92","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1006\/nimg.2001.0975","article-title":"Automated sulcal segmentation using watersheds on the cortical surface","volume":"15","author":"ME Rettmann","year":"2002","journal-title":"Neuroimage [Internet]"},{"issue":"3","key":"ref93","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/S1361-8415(00)00024-4","article-title":"Automatic labelling of the human cortical surface using sulcal basins","volume":"4","author":"G Lohmann","year":"2000","journal-title":"Med Image Anal [Internet]"},{"issue":"4","key":"ref94","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TMI.2006.886810","article-title":"A geometric method for automatic extraction of sulcal fundi","volume":"26","author":"C-Y Kao","year":"2007","journal-title":"IEEE Trans Med Imaging [Internet]"},{"issue":"Pt 1","key":"ref95","first-page":"270","article-title":"A novel method for cortical sulcal fundi extraction","volume":"11","author":"G Li","year":"2008","journal-title":"Med Image Comput Comput Assist Interv [Internet]"},{"issue":"4","key":"ref96","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1016\/j.neuroimage.2012.04.021","article-title":"Automatic sulcal line extraction on cortical surfaces using geodesic path density maps","volume":"61","author":"A Le Troter","year":"2012","journal-title":"Neuroimage [Internet]"},{"issue":"3","key":"ref97","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1093\/cercor\/bhp127","article-title":"Spatial distribution of deep sulcal landmarks and hemispherical asymmetry on the cortical surface","volume":"20","author":"K Im","year":"2010","journal-title":"Cereb Cortex [Internet]"},{"issue":"3","key":"ref98","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1016\/j.neuroimage.2011.04.062","article-title":"Quantitative comparison and analysis of sulcal patterns using sulcal graph matching: a twin study","volume":"57","author":"K Im","year":"2011","journal-title":"Neuroimage [Internet]"},{"key":"ref99","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.media.2016.04.011","article-title":"Structural graph-based morphometry: A multiscale searchlight framework based on sulcal pits","volume":"35","author":"S Takerkart","year":"2017","journal-title":"Med Image Anal [Internet]"},{"issue":"3","key":"ref100","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.jmb.2006.07.022","article-title":"Travel depth, a new shape descriptor for macromolecules: application to ligand binding","volume":"362","author":"RG Coleman","year":"2006","journal-title":"J Mol Biol [Internet]"},{"issue":"1","key":"ref101","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/TCBB.2009.53","article-title":"Fast surface-based travel depth estimation algorithm for macromolecule surface shape description","volume":"8","author":"J Giard","year":"2011","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform [Internet]"},{"issue":"2","key":"ref102","doi-asserted-by":"crossref","first-page":"e55977","DOI":"10.1371\/journal.pone.0055977","article-title":"Automated sulcal depth measurement on cortical surface reflecting geometrical properties of sulci","volume":"8","author":"HJ Yun","year":"2013","journal-title":"PLoS One [Internet]"},{"issue":"2","key":"ref103","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.media.2008.09.001","article-title":"Depth potential function for folding pattern representation, registration and analysis","volume":"13","author":"M Boucher","year":"2009","journal-title":"Med Image Anal [Internet]"},{"key":"ref104","unstructured":"Bao F, Lee N, Hame Y, Im K, Riviera D, Li G, et al. Automated extraction of nested sulcal features from human brain MRI data. In: 17th annual meeting for the Organization of Human Brain Mapping [Internet]. 2011. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/binarybottle\/nestedsulcusfeatures_HBM2011\" xlink:type=\"simple\">https:\/\/github.com\/binarybottle\/nestedsulcusfeatures_HBM2011<\/ext-link>"},{"key":"ref105","article-title":"A graph-based database of hierarchical brain features","author":"N Lee","year":"2011","journal-title":"Frontiers in Neuroinformatics [Internet]"},{"issue":"4","key":"ref106","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.cad.2005.10.011","article-title":"Laplace\u2013Beltrami spectra as \u201cShape-DNA\u201d of surfaces and solids","volume":"38","author":"M Reuter","year":"2006","journal-title":"Comput Aided Des Appl [Internet]"},{"issue":"10","key":"ref107","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.cad.2009.02.007","article-title":"Laplace\u2013Beltrami eigenvalues and topological features of eigenfunctions for statistical shape analysis","volume":"41","author":"M Reuter","year":"2009","journal-title":"Comput Aided Des Appl [Internet]"},{"issue":"4","key":"ref108","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1016\/j.neuroimage.2012.02.084","article-title":"Within-subject template estimation for unbiased longitudinal image analysis","volume":"61","author":"M Reuter","year":"2012","journal-title":"Neuroimage [Internet]"},{"issue":"9","key":"ref109","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1109\/TPAMI.2012.275","article-title":"WESD\u2014Weighted Spectral Distance for measuring shape dissimilarity","volume":"35","author":"E Konukoglu","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell [Internet]"},{"key":"ref110","doi-asserted-by":"crossref","unstructured":"Celebi ME, Aslandogan YA. A comparative study of three moment-based shape descriptors. In: International Conference on Information Technology: Coding and Computing (ITCC\u201905)\u2014Volume II [Internet]. IEEE; 2005 [cited 2016 Aug 6]. p. 788\u201393 Vol. 1. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/dx.doi.org\/10.1109\/ITCC.2005.3\" xlink:type=\"simple\">http:\/\/dx.doi.org\/10.1109\/ITCC.2005.3<\/ext-link>","DOI":"10.1109\/ITCC.2005.3"},{"issue":"3","key":"ref111","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.media.2004.06.016","article-title":"Brain morphometry using 3D moment invariants","volume":"8","author":"J-F Mangin","year":"2004","journal-title":"Med Image Anal [Internet]"},{"issue":"11","key":"ref112","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.cad.2004.01.005","article-title":"Shape retrieval using 3D Zernike descriptors","volume":"36","author":"M Novotni","year":"2004","journal-title":"Comput Aided Des Appl [Internet]"},{"issue":"3","key":"ref113","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/TPAMI.2010.139","article-title":"Efficient 3D geometric and Zernike moments computation from unstructured surface meshes","volume":"33","author":"JM Pozo","year":"2011","journal-title":"IEEE Trans Pattern Anal Mach Intell [Internet]"},{"issue":"5","key":"ref114","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1212\/WNL.58.5.695","article-title":"Regional and progressive thinning of the cortical ribbon in Huntington\u2019s disease","volume":"58","author":"HD Rosas","year":"2002","journal-title":"Neurology [Internet]"},{"issue":"2","key":"ref115","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1006\/nimg.2000.0652","article-title":"Measurement of Cortical Thickness Using an Automated 3-D Algorithm: A Validation Study","volume":"13","author":"N Kabani","year":"2001","journal-title":"Neuroimage [Internet]"},{"issue":"3","key":"ref116","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.media.2010.01.005","article-title":"An automated pipeline for cortical sulcal fundi extraction","volume":"14","author":"G Li","year":"2010","journal-title":"Med Image Anal [Internet]"},{"key":"ref117","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.neuroimage.2013.05.007","article-title":"Brain morphometry reproducibility in multi-center 3T MRI studies: a comparison of cross-sectional and longitudinal segmentations","volume":"83","author":"J Jovicich","year":"2013","journal-title":"Neuroimage [Internet]"},{"key":"ref118","unstructured":"Klein A, Chaibub Neto E, Giard J, Bao F, Hame Y, Reuter M, et al. Shape analysis of 101 healthy human brains. In: 20th annual meeting for the Organization of Human Brain Mapping [Internet]. 2014. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/mfr.osf.io\/render?url=https:\/\/osf.io\/w2vda\/?action=download%26mode=render\" xlink:type=\"simple\">https:\/\/mfr.osf.io\/render?url=https:\/\/osf.io\/w2vda\/?action=download%26mode=render<\/ext-link>"},{"key":"ref119","unstructured":"Klein A, Chaibub Neto E, Ghosh S, ADNI. Detailed shape analysis of healthy brains and brains with Alzheimer\u2019s disease. In: 21st annual meeting for the Organization of Human Brain Mapping [Internet]. 2015. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/mfr.osf.io\/render?url=https:\/\/osf.io\/xfts3\/?action=download%26mode=render\" xlink:type=\"simple\">https:\/\/mfr.osf.io\/render?url=https:\/\/osf.io\/xfts3\/?action=download%26mode=render<\/ext-link>"},{"key":"ref120","doi-asserted-by":"crossref","first-page":"140049","DOI":"10.1038\/sdata.2014.49","article-title":"An open science resource for establishing reliability and reproducibility in functional connectomics","volume":"1","author":"X-N Zuo","year":"2014","journal-title":"Sci Data [Internet]"},{"key":"ref121","doi-asserted-by":"crossref","first-page":"140037","DOI":"10.1038\/sdata.2014.37","article-title":"Reliability of brain volume measurements: a test-retest dataset","volume":"1","author":"J Maclaren","year":"2014","journal-title":"Scientific Data [Internet]"},{"key":"ref122","doi-asserted-by":"crossref","first-page":"160016","DOI":"10.1038\/sdata.2016.16","article-title":"A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity","volume":"3","author":"L Huang","year":"2016","journal-title":"Sci Data [Internet]"},{"key":"ref123","doi-asserted-by":"crossref","first-page":"150031","DOI":"10.1038\/sdata.2015.31","article-title":"Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures","volume":"2","author":"AJ Holmes","year":"2015","journal-title":"Sci Data [Internet]"},{"key":"ref124","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn Human Connectome Project: an overview","volume":"80","author":"DC Van Essen","year":"2013","journal-title":"Neuroimage [Internet]"},{"key":"ref125","doi-asserted-by":"crossref","first-page":"140054","DOI":"10.1038\/sdata.2014.54","article-title":"A high resolution 7-Tesla resting-state fMRI test-retest dataset with cognitive and physiological measures","volume":"2","author":"KJ Gorgolewski","year":"2015","journal-title":"Sci Data [Internet]"},{"key":"ref126","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.dib.2015.10.004","article-title":"Benchmark data for sulcal pits extraction algorithms","volume":"5","author":"G Auzias","year":"2015","journal-title":"Data Brief [Internet]"},{"key":"ref127","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.neuroimage.2016.05.030","article-title":"Consistent cortical reconstruction and multi-atlas brain segmentation","volume":"138","author":"Y Huo","year":"2016","journal-title":"Neuroimage [Internet]"},{"key":"ref128","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview","volume":"61","author":"J Schmidhuber","year":"2015","journal-title":"Neural Netw [Internet]"},{"key":"ref129","doi-asserted-by":"crossref","unstructured":"Lee N, Laine AF, Klein A. Towards a deep learning approach to brain parcellation. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro [Internet]. IEEE; 2011 [cited 2016 Aug 6]. p. 321\u20134. <ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/dx.doi.org\/10.1109\/ISBI.2011.5872414\" xlink:type=\"simple\">http:\/\/dx.doi.org\/10.1109\/ISBI.2011.5872414<\/ext-link>","DOI":"10.1109\/ISBI.2011.5872414"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pcbi.1005350","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T13:34:04Z","timestamp":1658669644000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1005350"}},"subtitle":[],"editor":[{"given":"Dina","family":"Schneidman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2017,2,23]]},"references-count":129,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,2,23]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1005350","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/091322","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,23]]}}}