{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:29:22Z","timestamp":1780363762610,"version":"3.54.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81790650, 81790652"],"award-info":[{"award-number":["81790650, 81790652"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01NS091604, P50MH106435, P50DA046373, P20GM109040, R01DC017991, R21MH121831"],"award-info":[{"award-number":["R01NS091604, P50MH106435, P50DA046373, P20GM109040, R01DC017991, R21MH121831"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen International Cooperative Research Project","award":["GJHZ20180930110402104"],"award-info":[{"award-number":["GJHZ20180930110402104"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the \u201clevel set representation\u201d. A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject\u2019s cortical surfaces within 5\u00a0min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test\u2013retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.<\/jats:p>","DOI":"10.1186\/s40708-022-00155-7","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T09:02:47Z","timestamp":1646816567000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Fast cortical surface reconstruction from MRI using deep learning"],"prefix":"10.1186","volume":"9","author":[{"given":"Jianxun","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine S.","family":"Hubbard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingjia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louisa","family":"Dahmani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxuan","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yezhe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hesheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"155_CR1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1006\/nimg.1998.0395","volume":"9","author":"AM Dale","year":"1999","unstructured":"Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9:179\u2013194","journal-title":"Neuroimage"},{"key":"155_CR2","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1006\/jtbi.1994.1024","volume":"166","author":"LD Griffin","year":"1994","unstructured":"Griffin LD (1994) The intrinsic geometry of the cerebral cortex. J Theor Biol 166:261\u2013273","journal-title":"J Theor Biol"},{"key":"155_CR3","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.neuroimage.2004.06.043","volume":"23","author":"X Han","year":"2004","unstructured":"Han X, Pham DL, Tosun D et al (2004) CRUISE: cortical reconstruction using implicit surface evolution. Neuroimage 23:997\u20131012","journal-title":"Neuroimage"},{"key":"155_CR4","doi-asserted-by":"crossref","first-page":"11050","DOI":"10.1073\/pnas.200033797","volume":"97","author":"B Fischl","year":"2000","unstructured":"Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97:11050\u201311055","journal-title":"Proc Natl Acad Sci U S A"},{"key":"155_CR5","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S1361-8415(03)00006-9","volume":"7","author":"F Kruggel","year":"2003","unstructured":"Kruggel F, Bruckner MK, Arendt T et al (2003) Analyzing the neocortical fine-structure. Med Image Anal 7:251\u2013264","journal-title":"Med Image Anal"},{"key":"155_CR6","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1097\/00004728-199811000-00022","volume":"22","author":"A Manceaux-Demiau","year":"1998","unstructured":"Manceaux-Demiau A, Bryan RN, Davatzikos C (1998) A probabilistic ribbon model for shape analysis of the cerebral sulci: application to the central sulcus. J Comput Assist Tomogr 22:962\u2013971","journal-title":"J Comput Assist Tomogr"},{"key":"155_CR7","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl B (2012) FreeSurfer. Neuroimage 62:774\u2013781","journal-title":"Neuroimage"},{"key":"155_CR8","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1136\/jamia.2001.0080443","volume":"8","author":"DC Van Essen","year":"2001","unstructured":"Van Essen DC, Drury HA, Dickson J et al (2001) An integrated software suite for surface-based analyses of cerebral cortex. J Am Med Inform Assoc 8:443\u2013459","journal-title":"J Am Med Inform Assoc"},{"key":"155_CR9","doi-asserted-by":"crossref","first-page":"E6356","DOI":"10.1073\/pnas.1801582115","volume":"115","author":"TS Coalson","year":"2018","unstructured":"Coalson TS, Van Essen DC, Glasser MF (2018) The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc Natl Acad Sci U S A 115:E6356\u2013E6365","journal-title":"Proc Natl Acad Sci U S A"},{"key":"155_CR10","first-page":"E5154","volume":"115","author":"XZ Kong","year":"2018","unstructured":"Kong XZ, Mathias SR, Guadalupe T et al (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A 115:E5154\u2013E5163","journal-title":"Proc Natl Acad Sci U S A"},{"key":"155_CR11","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1038\/s41562-020-0901-2","volume":"4","author":"W Xie","year":"2020","unstructured":"Xie W, Bainbridge WA, Inati SK et al (2020) Memorability of words in arbitrary verbal associations modulates memory retrieval in the anterior temporal lobe. Nat Hum Behav 4:937\u2013948","journal-title":"Nat Hum Behav"},{"key":"155_CR12","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1038\/s41380-018-0276-1","volume":"25","author":"D Wang","year":"2020","unstructured":"Wang D, Li M, Wang M et al (2020) Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatry 25:2119\u20132129","journal-title":"Mol Psychiatry"},{"key":"155_CR13","doi-asserted-by":"crossref","first-page":"8174","DOI":"10.1073\/pnas.0402680101","volume":"101","author":"N Gogtay","year":"2004","unstructured":"Gogtay N, Giedd JN, Lusk L et al (2004) Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A 101:8174\u20138179","journal-title":"Proc Natl Acad Sci U S A"},{"key":"155_CR14","doi-asserted-by":"crossref","first-page":"e1174","DOI":"10.1212\/WNL.0000000000010149","volume":"95","author":"H Liu","year":"2020","unstructured":"Liu H, Peng X, Dahmani L et al (2020) Patterns of motor recovery and structural neuroplasticity after basal ganglia infarcts. Neurology 95:e1174\u2013e1187","journal-title":"Neurology"},{"key":"155_CR15","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1093\/cercor\/bhaa366","volume":"31","author":"J Ren","year":"2021","unstructured":"Ren J, Xu T, Wang D et al (2021) Individual variability in functional organization of the human and monkey auditory cortex. Cereb Cortex 31:2450\u20132465","journal-title":"Cereb Cortex"},{"key":"155_CR16","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1038\/s41467-020-18823-9","volume":"11","author":"Y Yan","year":"2020","unstructured":"Yan Y, Dahmani L, Ren J et al (2020) Reconstructing lost BOLD signal in individual participants using deep machine learning. Nat Commun 11:5046","journal-title":"Nat Commun"},{"key":"155_CR17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","volume":"16","author":"O Esteban","year":"2019","unstructured":"Esteban O, Markiewicz CJ, Blair RW et al (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16:111\u2013116","journal-title":"Nat Methods"},{"key":"155_CR18","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1089\/brain.2018.0608","volume":"9","author":"L Sanfratello","year":"2019","unstructured":"Sanfratello L, Houck JM, Calhoun VD (2019) Dynamic functional network connectivity in schizophrenia with magnetoencephalography and functional magnetic resonance imaging: do different timescales tell a different story? Brain Connect 9:251\u2013262","journal-title":"Brain Connect"},{"key":"155_CR19","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1038\/s41467-020-15781-0","volume":"11","author":"A Sohrabpour","year":"2020","unstructured":"Sohrabpour A, Cai Z, Ye S et al (2020) Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources. Nat Commun 11:1946","journal-title":"Nat Commun"},{"key":"155_CR20","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neuroimage.2012.09.050","volume":"65","author":"R Dahnke","year":"2013","unstructured":"Dahnke R, Yotter RA, Gaser C (2013) Cortical thickness and central surface estimation. Neuroimage 65:336\u2013348","journal-title":"Neuroimage"},{"key":"155_CR21","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.neuroimage.2005.03.036","volume":"27","author":"JS Kim","year":"2005","unstructured":"Kim JS, Singh V, Lee JK et al (2005) Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 27:210\u2013221","journal-title":"Neuroimage"},{"key":"155_CR22","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1006\/nimg.2001.0831","volume":"14","author":"N Kriegeskorte","year":"2001","unstructured":"Kriegeskorte N, Goebel R (2001) An efficient algorithm for topologically correct segmentation of the cortical sheet in anatomical mr volumes. Neuroimage 14:329\u2013346","journal-title":"Neuroimage"},{"key":"155_CR23","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1006\/nimg.1999.0534","volume":"12","author":"D Macdonald","year":"2000","unstructured":"Macdonald D, Kabani N, Avis D et al (2000) Automated 3-D extraction of inner and outer surfaces of cerebral cortex from MRI. Neuroimage 12:340\u2013356","journal-title":"Neuroimage"},{"key":"155_CR24","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neuroimage.2017.09.060","volume":"165","author":"N Zaretskaya","year":"2018","unstructured":"Zaretskaya N, Fischl B, Reuter M et al (2018) Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE. Neuroimage 165:11\u201326","journal-title":"Neuroimage"},{"key":"155_CR25","doi-asserted-by":"crossref","unstructured":"Cruz RS, Lebrat L, Bourgeat P et al. (2021) Deepcsr: a 3d deep learning approach for cortical surface reconstruction. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. p 806\u2013815","DOI":"10.1109\/WACV48630.2021.00085"},{"key":"155_CR26","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2020.117012","volume":"219","author":"L Henschel","year":"2020","unstructured":"Henschel L, Conjeti S, Estrada S et al (2020) FastSurfer\u2014a fast and accurate deep learning based neuroimaging pipeline. Neuroimage 219:117012","journal-title":"Neuroimage"},{"key":"155_CR27","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2020.117161","volume":"221","author":"J Cheng","year":"2020","unstructured":"Cheng J, Dalca AV, Fischl B et al (2020) Cortical surface registration using unsupervised learning. Neuroimage 221:117161","journal-title":"Neuroimage"},{"key":"155_CR28","doi-asserted-by":"crossref","unstructured":"Michalkiewicz M, Pontes JK, Jack D et al. (2019) Deep level sets: implicit surface representations for 3D shape inference.","DOI":"10.1109\/ICCV.2019.00484"},{"key":"155_CR29","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giy082","author":"JM Huntenburg","year":"2018","unstructured":"Huntenburg JM, Steele CJ, Bazin PL (2018) Nighres: processing tools for high-resolution neuroimaging. Gigascience. https:\/\/doi.org\/10.1093\/gigascience\/giy082","journal-title":"Gigascience"},{"key":"155_CR30","volume":"1","author":"XN Zuo","year":"2014","unstructured":"Zuo XN, Anderson JS, Bellec P et al (2014) An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data 1:140049","journal-title":"Sci Data"},{"key":"155_CR31","doi-asserted-by":"crossref","DOI":"10.1038\/sdata.2017.10","volume":"4","author":"A Di Martino","year":"2017","unstructured":"Di Martino A, O\u2019connor D, Chen B et al (2017) Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Sci Data 4:170010","journal-title":"Sci Data"},{"key":"155_CR32","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, SaA K et al (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203\u2013211","journal-title":"Nat Methods"},{"key":"155_CR33","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1002\/mp.14676","volume":"48","author":"J Ma","year":"2021","unstructured":"Ma J, Wang Y, An X et al (2021) Toward data-efficient learning: a benchmark for COVID-19 CT lung and infection segmentation. Med Phys 48:1197\u20131210","journal-title":"Med Phys"},{"key":"155_CR34","first-page":"118","volume-title":"nnU-Net for brain tumor segmentation","author":"F Isensee","year":"2021","unstructured":"Isensee F, J\u00e4ger PF, Full PM et al (2021) nnU-Net for brain tumor segmentation. Springer International Publishing, Cham, pp 118\u2013132"},{"key":"155_CR35","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2015. MICCAI 2015. Lecture notes in computer science, vol 9351. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"155_CR36","series-title":"Lecture notes in computer science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical image computing and computer-assisted intervention \u2013 MICCAI 2016. MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2016. MICCAI 2016. Lecture notes in computer science, vol 9901. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"155_CR37","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.cmpb.2007.08.006","volume":"88","author":"PL Bazin","year":"2007","unstructured":"Bazin PL, Pham DL (2007) Topology correction of segmented medical images using a fast marching algorithm. Comput Methods Programs Biomed 88:182\u2013190","journal-title":"Comput Methods Programs Biomed"},{"key":"155_CR38","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TPAMI.2003.1201824","volume":"25","author":"X Han","year":"2003","unstructured":"Han X, Xu C, Prince JL (2003) A topology preserving level set method for geometric deformable models. IEEE Trans Pattern Anal Mach Intell 25:755\u2013768","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"155_CR39","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","volume":"33","author":"B Fischl","year":"2002","unstructured":"Fischl B, Salat DH, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341\u2013355","journal-title":"Neuron"},{"key":"155_CR40","unstructured":"Paszke A, Gross S, Chintala S et al. (2017) Automatic differentiation in pytorch."},{"key":"155_CR41","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"155_CR42","volume-title":"PLTMG, a software package for solving elliptic partial differential equations: users' guide 6.0","author":"RE Bank","year":"1990","unstructured":"Bank RE (1990) PLTMG, a software package for solving elliptic partial differential equations: users\u2019 guide 6.0. Society for Industrial and Applied Mathematics, Philadelphia"},{"key":"155_CR43","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.neuroimage.2013.12.012","volume":"90","author":"K Fujimoto","year":"2014","unstructured":"Fujimoto K, Polimeni JR, Van Der Kouwe AJ et al (2014) Quantitative comparison of cortical surface reconstructions from MP2RAGE and multi-echo MPRAGE data at 3 and 7 T. Neuroimage 90:60\u201373","journal-title":"Neuroimage"},{"key":"155_CR44","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1006\/nimg.1998.0396","volume":"9","author":"B Fischl","year":"1999","unstructured":"Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195\u2013207","journal-title":"Neuroimage"},{"key":"155_CR45","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.eplepsyres.2015.02.015","volume":"112","author":"AJ Ristic","year":"2015","unstructured":"Ristic AJ, Dakovic M, Kerr M et al (2015) Cortical thickness, surface area and folding in patients with psychogenic nonepileptic seizures. Epilepsy Res 112:84\u201391","journal-title":"Epilepsy Res"},{"key":"155_CR46","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan RS, Segonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968\u2013980","journal-title":"Neuroimage"},{"key":"155_CR47","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1093\/cercor\/bhg087","volume":"14","author":"B Fischl","year":"2004","unstructured":"Fischl B, Van Der Kouwe A, Destrieux C et al (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11\u201322","journal-title":"Cereb Cortex"},{"key":"155_CR48","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","volume":"62","author":"DC Van Essen","year":"2012","unstructured":"Van Essen DC, Ugurbil K, Auerbach E et al (2012) The Human Connectome Project: a data acquisition perspective. Neuroimage 62:2222\u20132231","journal-title":"Neuroimage"},{"key":"155_CR49","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1177\/0271678X17709198","volume":"37","author":"JS Siegel","year":"2017","unstructured":"Siegel JS, Shulman GL, Corbetta M (2017) Measuring functional connectivity in stroke: approaches and considerations. J Cereb Blood Flow Metab 37:2665\u20132678","journal-title":"J Cereb Blood Flow Metab"},{"key":"155_CR50","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2020.100994","volume":"7","author":"BR Diamond","year":"2020","unstructured":"Diamond BR, Donald CLM, Frau-Pascual A et al (2020) Optimizing the accuracy of cortical volumetric analysis in traumatic brain injury. MethodsX 7:100994","journal-title":"MethodsX"},{"key":"155_CR51","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1002\/ana.26303","volume":"91","author":"W Cui","year":"2022","unstructured":"Cui W, Wang Y, Ren J et al (2022) Personalized fMRI delineates functional regions preserved within brain tumors. Ann Neurol 91:353\u2013366","journal-title":"Ann Neurol"},{"key":"155_CR52","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neuroimage.2013.04.127","volume":"80","author":"MF Glasser","year":"2013","unstructured":"Glasser MF, Sotiropoulos SN, Wilson JA et al (2013) The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80:105\u2013124","journal-title":"Neuroimage"},{"key":"155_CR53","first-page":"393","volume":"19","author":"F Segonne","year":"2005","unstructured":"Segonne F, Grimson E, Fischl B (2005) A genetic algorithm for the topology correction of cortical surfaces. Inf Process Med Imaging 19:393\u2013405","journal-title":"Inf Process Med Imaging"},{"key":"155_CR54","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TPAMI.2003.1201824","volume":"25","author":"H Xiao","year":"2003","unstructured":"Xiao H, Chenyang X, Prince JL (2003) A topology preserving level set method for geometric deformable models. IEEE Trans Pattern Anal Mach Intell 25:755\u2013768","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"155_CR55","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-3-319-73074-5_5","volume-title":"Compressed sensing and its applications","author":"D Jakubovitz","year":"2019","unstructured":"Jakubovitz D, Giryes R, Rodrigues MR (2019) Generalization error in deep learning. Compressed sensing and its applications. Springer, Cham, pp 153\u2013193"},{"key":"155_CR56","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1016\/j.ekir.2019.04.008","volume":"4","author":"S Kannan","year":"2019","unstructured":"Kannan S, Morgan LA, Liang B et al (2019) Segmentation of glomeruli within trichrome images using deep learning. Kidney Int Rep 4:955\u2013962","journal-title":"Kidney Int Rep"},{"key":"155_CR57","doi-asserted-by":"crossref","unstructured":"Moerel M, Yacoub E, Gulban OF et al. (2020) Using high spatial resolution fMRI to understand representation in the auditory network. Prog Neurobiol. 101887","DOI":"10.1016\/j.pneurobio.2020.101887"},{"key":"155_CR58","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.neuroimage.2016.09.010","volume":"143","author":"J Ahveninen","year":"2016","unstructured":"Ahveninen J, Chang WT, Huang S et al (2016) Intracortical depth analyses of frequency-sensitive regions of human auditory cortex using 7TfMRI. Neuroimage 143:116\u2013127","journal-title":"Neuroimage"},{"key":"155_CR59","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1152\/jn.00808.2018","volume":"121","author":"RM Braga","year":"2019","unstructured":"Braga RM, Van Dijk KRA, Polimeni JR et al (2019) Parallel distributed networks resolved at high resolution reveal close juxtaposition of distinct regions. J Neurophysiol 121:1513\u20131534","journal-title":"J Neurophysiol"},{"key":"155_CR60","doi-asserted-by":"crossref","first-page":"2898","DOI":"10.1093\/cercor\/bhaa398","volume":"31","author":"J Ren","year":"2021","unstructured":"Ren J, Hubbard CS, Ahveninen J et al (2021) Dissociable auditory cortico-cerebellar pathways in the human brain estimated by intrinsic functional connectivity. Cereb Cortex 31:2898\u20132912","journal-title":"Cereb Cortex"},{"key":"155_CR61","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.brs.2021.11.005","volume":"15","author":"J Ren","year":"2021","unstructured":"Ren J, Chi Q, Hubbard CS et al (2021) Personalized functional imaging identifies brain stimulation target for a patient with trauma-induced functional disruption. Brain Stimul 15:53\u201356","journal-title":"Brain Stimul"},{"key":"155_CR62","volume":"227","author":"M Li","year":"2021","unstructured":"Li M, Dahmani L, Wang D et al (2021) Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks. Neuroimage 227:117680","journal-title":"Neuroimage"},{"key":"155_CR63","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1038\/s41380-019-0627-6","volume":"25","author":"Q Xu","year":"2020","unstructured":"Xu Q, Guo L, Cheng J et al (2020) CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research. Mol Psychiatry 25:517\u2013529","journal-title":"Mol Psychiatry"},{"key":"155_CR64","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1038\/nn.4393","volume":"19","author":"KL Miller","year":"2016","unstructured":"Miller KL, Alfaro-Almagro F, Bangerter NK et al (2016) Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 19:1523\u20131536","journal-title":"Nat Neurosci"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-022-00155-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-022-00155-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-022-00155-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T09:06:07Z","timestamp":1646816767000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-022-00155-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,9]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["155"],"URL":"https:\/\/doi.org\/10.1186\/s40708-022-00155-7","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,9]]},"assertion":[{"value":"2 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"H.L. is on the chief scientific advisory board for Neural Galaxy LLC. The other authors declare no competing financial interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}