{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T10:52:10Z","timestamp":1781175130014,"version":"3.54.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82101998"],"award-info":[{"award-number":["82101998"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62025601"],"award-info":[{"award-number":["62025601"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006164"],"award-info":[{"award-number":["62006164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["8212018014"],"award-info":[{"award-number":["8212018014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071908"],"award-info":[{"award-number":["82071908"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuropsychopharmacol."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1038\/s41386-024-02021-y","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T18:02:30Z","timestamp":1730916150000},"page":"531-539","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia"],"prefix":"10.1038","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2046-3948","authenticated-orcid":false,"given":"Wenjing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lituan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xusha","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-1455","authenticated-orcid":false,"given":"Hong","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3541-1769","authenticated-orcid":false,"given":"Su","family":"Lui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5912-4871","authenticated-orcid":false,"given":"Qiyong","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"2021_CR1","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1176\/appi.ajp.2015.15050641","volume":"173","author":"Q Gong","year":"2016","unstructured":"Gong Q, Lui S, Sweeney JA. A selective review of cerebral abnormalities in patients with first-episode schizophrenia before and after treatment. Am J Psychiatry. 2016;173:232\u201343.","journal-title":"Am J Psychiatry"},{"key":"2021_CR2","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1176\/appi.ajp.2020.20030340","volume":"178","author":"NV Kraguljac","year":"2021","unstructured":"Kraguljac NV, McDonald WM, Widge AS, Rodriguez CI, Tohen M, Nemeroff CB. Neuroimaging biomarkers in Schizophrenia. Am J Psychiatry. 2021;178:509\u201321.","journal-title":"Am J Psychiatry"},{"key":"2021_CR3","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1176\/appi.ajp.2015.14091108","volume":"172","author":"W Zhang","year":"2015","unstructured":"Zhang W, Deng W, Yao L, Xiao Y, Li F, Liu J, et al. Brain structural abnormalities in a group of never-medicated patients with long-term Schizophrenia. Am J Psychiatry. 2015;172:995\u20131003.","journal-title":"Am J Psychiatry"},{"key":"2021_CR4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.schres.2017.11.037","volume":"214","author":"Y Xiao","year":"2019","unstructured":"Xiao Y, Yan Z, Zhao Y, Tao B, Sun H, Li F, et al. Support vector machine-based classification of first episode drug-na\u00efve schizophrenia patients and healthy controls using structural MRI. Schizophr Res. 2019;214:11\u201317.","journal-title":"Schizophr Res"},{"key":"2021_CR5","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1093\/schbul\/sbac094","volume":"48","author":"J Zeng","year":"2022","unstructured":"Zeng J, Zhang W, Wu G, Wang X, Shah C, Li S, et al. Effects of antipsychotic medications and illness duration on brain features that distinguish Schizophrenia patients. Schizophr Bull. 2022;48:1354\u201362.","journal-title":"Schizophr Bull"},{"key":"2021_CR6","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.neubiorev.2012.01.004","volume":"36","author":"G Orru","year":"2012","unstructured":"Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36:1140\u201352.","journal-title":"Neurosci Biobehav Rev"},{"key":"2021_CR7","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1093\/schbul\/sbab112","volume":"48","author":"H Cao","year":"2022","unstructured":"Cao H, Wei X, Hu N, Zhang W, Xiao Y, Zeng J, et al. Cerebello-thalamo-cortical hyperconnectivity classifies patients and predicts long-term treatment outcome in first-episode Schizophrenia. Schizophr Bull. 2022;48:505\u201313.","journal-title":"Schizophr Bull"},{"key":"2021_CR8","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1038\/npp.2015.22","volume":"40","author":"J Kambeitz","year":"2015","unstructured":"Kambeitz J, Kambeitz-Ilankovic L, Leucht S, Wood S, Davatzikos C, Malchow B, et al. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology. 2015;40:1742\u201351.","journal-title":"Neuropsychopharmacology"},{"key":"2021_CR9","doi-asserted-by":"publisher","first-page":"S110","DOI":"10.1093\/schbul\/sbw053","volume":"42","author":"C Cabral","year":"2016","unstructured":"Cabral C, Kambeitz-Ilankovic L, Kambeitz J, Calhoun VD, Dwyer DB, von Saldern S, et al. Classifying Schizophrenia using multimodal multivariate pattern recognition analysis: evaluating the impact of individual clinical profiles on the neurodiagnostic performance. Schizophr Bull. 2016;42:S110\u20137.","journal-title":"Schizophr Bull"},{"key":"2021_CR10","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1038\/nm.4190","volume":"22","author":"A Abi-Dargham","year":"2016","unstructured":"Abi-Dargham A, Horga G. The search for imaging biomarkers in psychiatric disorders. Nat Med. 2016;22:1248\u201355.","journal-title":"Nat Med"},{"key":"2021_CR11","doi-asserted-by":"publisher","first-page":"5151","DOI":"10.1002\/hbm.25181","volume":"41","author":"JA Taylor","year":"2020","unstructured":"Taylor JA, Larsen KM, Garrido MI. Multi-dimensional predictions of psychotic symptoms via machine learning. Hum Brain Mapp. 2020;41:5151\u201363.","journal-title":"Hum Brain Mapp"},{"key":"2021_CR12","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1038\/s41380-018-0276-1","volume":"25","author":"D Wang","year":"2020","unstructured":"Wang D, Li M, Wang M, Schoeppe F, Ren J, Chen H, et al. Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatry. 2020;25:2119\u201329.","journal-title":"Mol Psychiatry"},{"key":"2021_CR13","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.neubiorev.2015.08.001","volume":"57","author":"T Wolfers","year":"2015","unstructured":"Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev. 2015;57:328\u201349.","journal-title":"Neurosci Biobehav Rev"},{"key":"2021_CR14","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.2147\/NDT.S202418","volume":"15","author":"R de Filippis","year":"2019","unstructured":"de Filippis R, Carbone EA, Gaetano R, Bruni A, Pugliese V, Segura-Garcia C, et al. Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatr Dis Treat. 2019;15:1605\u201327.","journal-title":"Neuropsychiatr Dis Treat"},{"key":"2021_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.expneurol.2021.113608","volume":"339","author":"F Eitel","year":"2021","unstructured":"Eitel F, Schulz MA, Seiler M, Walter H, Ritter K. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research. Exp Neurol. 2021;339:113608.","journal-title":"Exp Neurol"},{"key":"2021_CR16","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3389\/fnins.2014.00229","volume":"8","author":"SM Plis","year":"2014","unstructured":"Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, et al. Deep learning for neuroimaging: a validation study. Front Neurosci. 2014;8:229.","journal-title":"Front Neurosci"},{"key":"2021_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/srep38897","volume":"6","author":"WH Pinaya","year":"2016","unstructured":"Pinaya WH, Gadelha A, Doyle OM, Noto C, Zugman A, Cordeiro Q, et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci Rep. 2016;6:38897.","journal-title":"Sci Rep."},{"key":"2021_CR18","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s44220-023-00019-x","volume":"1","author":"W Zhang","year":"2023","unstructured":"Zhang W, Sweeney JA, Bishop JR, Gong Q, Lui S. Biological subtyping of psychiatric syndromes as a pathway for advances in drug discovery and personalized medicine. Nat Ment Health. 2023;1:88\u201399.","journal-title":"Nat Ment Health"},{"key":"2021_CR19","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1017\/S0033291716002683","volume":"47","author":"C Shah","year":"2017","unstructured":"Shah C, Zhang W, Xiao Y, Yao L, Zhao Y, Gao X, et al. Common pattern of gray-matter abnormalities in drug-naive and medicated first-episode schizophrenia: a multimodal meta-analysis. Psychol Med. 2017;47:401\u201313.","journal-title":"Psychol Med"},{"key":"2021_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105554","volume":"146","author":"D Sadeghi","year":"2022","unstructured":"Sadeghi D, Shoeibi A, Ghassemi N, Moridian P, Khadem A, Alizadehsani R, et al. An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works. Comput Biol Med. 2022;146:105554.","journal-title":"Comput Biol Med"},{"key":"2021_CR21","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.schres.2017.11.038","volume":"214","author":"JL Winterburn","year":"2019","unstructured":"Winterburn JL, Voineskos AN, Devenyi GA, Plitman E, de la Fuente-Sandoval C, Bhagwat N, et al. Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. Schizophr Res. 2019;214:3\u201310.","journal-title":"Schizophr Res"},{"key":"2021_CR22","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1017\/S0033291709992297","volume":"40","author":"J Moncrieff","year":"2010","unstructured":"Moncrieff J, Leo J. A systematic review of the effects of antipsychotic drugs on brain volume. Psychol Med. 2010;40:1409\u201322.","journal-title":"Psychol Med"},{"key":"2021_CR23","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.schres.2020.03.069","volume":"222","author":"M Kubota","year":"2020","unstructured":"Kubota M, Moriguchi S, Takahata K, Nakajima S, Horita N. Treatment effects on neurometabolite levels in schizophrenia: A systematic review and meta-analysis of proton magnetic resonance spectroscopy studies. Schizophr Res. 2020;222:122\u201332.","journal-title":"Schizophr Res"},{"key":"2021_CR24","doi-asserted-by":"crossref","unstructured":"Xi YB, Wu XS, Cui LB, Bai LJ, Gan SQ, Jia XY, et al. Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication. Br J Psychiatry. 2021;220:1\u20138.","DOI":"10.1192\/bjp.2021.169"},{"key":"2021_CR25","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1093\/schbul\/13.2.261","volume":"13","author":"SR Kay","year":"1987","unstructured":"Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13:261\u201376.","journal-title":"Schizophr Bull"},{"key":"2021_CR26","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.biopsych.2007.05.027","volume":"63","author":"RA Honea","year":"2008","unstructured":"Honea RA, Meyer-Lindenberg A, Hobbs KB, Pezawas L, Mattay VS, Egan MF, et al. Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings. Biol Psychiatry. 2008;63:465\u201374.","journal-title":"Biol Psychiatry"},{"key":"2021_CR27","doi-asserted-by":"publisher","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, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968\u201380.","journal-title":"Neuroimage"},{"key":"2021_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. In International Conference on Medical image computing and computer-assisted intervention 234-41 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2021_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/hbm.1058","volume":"15","author":"TE Nichols","year":"2002","unstructured":"Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:1\u201325.","journal-title":"Hum Brain Mapp"},{"key":"2021_CR30","first-page":"330","volume":"18","author":"P Golland","year":"2003","unstructured":"Golland P, Fischl B. Permutation tests for classification: towards statistical significance in image-based studies. Inf Process Med Imaging. 2003;18:330\u201341.","journal-title":"Inf Process Med Imaging"},{"key":"2021_CR31","unstructured":"Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1603-58."},{"key":"2021_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101665","volume":"61","author":"L Wang","year":"2020","unstructured":"Wang L, Zhang L, Zhu M, Qi X, Yi Z. Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Med Image Anal. 2020;61:101665.","journal-title":"Med Image Anal"},{"key":"2021_CR33","doi-asserted-by":"publisher","first-page":"e68910","DOI":"10.1371\/journal.pone.0068910","volume":"8","author":"M Xia","year":"2013","unstructured":"Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One. 2013;8:e68910.","journal-title":"PLoS One"},{"key":"2021_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2016.05.029","volume":"136","author":"CD Smyser","year":"2016","unstructured":"Smyser CD, Dosenbach NU, Smyser TA, Snyder AZ, Rogers CE, Inder TE, et al. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage. 2016;136:1\u20139.","journal-title":"Neuroimage"},{"key":"2021_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen J, Thibeau-Sutre E, Diaz-Melo M, Samper-Gonz\u00e1lez J, Routier A, Bottani S, et al. Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation. Med Image Anal. 2020;63:101694.","journal-title":"Med Image Anal"},{"key":"2021_CR36","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29:1310\u201320.","journal-title":"IEEE Trans Med Imaging"},{"key":"2021_CR37","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1001\/jama.2022.3580","volume":"327","author":"CA James","year":"2022","unstructured":"James CA, Wachter RM, Woolliscroft JO. Preparing clinicians for a clinical world influenced by artificial intelligence. JAMA. 2022;327:1333\u201334.","journal-title":"JAMA"},{"key":"2021_CR38","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.schres.2020.03.022","volume":"220","author":"S Liang","year":"2020","unstructured":"Liang S, Deng W, Li X, Wang Q, Greenshaw AJ, Guo W, et al. Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: a machine learning study. Schizophr Res. 2020;220:187\u201393.","journal-title":"Schizophr Res"},{"key":"2021_CR39","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neulet.2019.04.039","volume":"705","author":"H Zhuang","year":"2019","unstructured":"Zhuang H, Liu R, Wu C, Meng Z, Wang D, Liu D, et al. Multimodal classification of drug-na\u00efve first-episode schizophrenia combining anatomical, diffusion and resting state functional resonance imaging. Neurosci Lett. 2019;705:87\u201393.","journal-title":"Neurosci Lett"},{"key":"2021_CR40","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.pnpbp.2018.09.014","volume":"91","author":"M Walter","year":"2019","unstructured":"Walter M, Alizadeh S, Jamalabadi H, Lueken U, Dannlowski U, Walter H, et al. Translational machine learning for psychiatric neuroimaging. Prog Neuropsychopharmacol Biol Psychiatry. 2019;91:113\u201321.","journal-title":"Prog Neuropsychopharmacol Biol Psychiatry"},{"key":"2021_CR41","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1093\/cercor\/bhac421","volume":"33","author":"B Tang","year":"2023","unstructured":"Tang B, Zhang W, Liu J, Deng S, Hu N, Li S, et al. Altered controllability of white matter networks and related brain function changes in first-episode drug-naive schizophrenia. Cereb Cortex. 2023;33:1527\u201335.","journal-title":"Cereb Cortex"},{"key":"2021_CR42","doi-asserted-by":"publisher","first-page":"3719","DOI":"10.1038\/s41380-022-01731-3","volume":"27","author":"J Seitz-Holland","year":"2022","unstructured":"Seitz-Holland J, Wojcik JD, Cetin-Karayumak S, Lyall AE, Pasternak O, Rathi Y, et al. Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study. Mol Psychiatry. 2022;27:3719\u201330.","journal-title":"Mol Psychiatry"},{"key":"2021_CR43","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.schres.2020.12.011","volume":"228","author":"J Zeng","year":"2021","unstructured":"Zeng J, Zhang W, Xiao Y, Fu G, Liu L, Tang B, et al. Pretreatment abnormalities in white matter integrity predict one-year clinical outcome in first episode schizophrenia. Schizophr Res. 2021;228:241\u201348.","journal-title":"Schizophr Res"},{"key":"2021_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41537-023-00424-2","volume":"10","author":"J Zeng","year":"2024","unstructured":"Zeng J, Zhang T, Tang B, Li S, Yao L, Bishop JR, et al. Choroid plexus volume enlargement in first-episode antipsychotic-na\u00efve schizophrenia. Schizophrenia (Heidelb). 2024;10:1.","journal-title":"Schizophrenia (Heidelb)"},{"key":"2021_CR45","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1176\/appi.ajp.2019.18070825","volume":"176","author":"P Lizano","year":"2019","unstructured":"Lizano P, Lutz O, Ling G, Lee AM, Eum S, Bishop JR, et al. Association of choroid plexus enlargement with cognitive, inflammatory, and structural phenotypes across the psychosis spectrum. Am J Psychiatry. 2019;176:564\u201372.","journal-title":"Am J Psychiatry"},{"key":"2021_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104541","volume":"90","author":"W Zhang","year":"2023","unstructured":"Zhang W, Yang C, Cao Z, Li Z, Zhuo L, Tan Y, et al. Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging. EBioMedicine. 2023;90:104541.","journal-title":"EBioMedicine"},{"key":"2021_CR47","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1093\/oxfordjournals.schbul.a033321","volume":"24","author":"NC Andreasen","year":"1998","unstructured":"Andreasen NC, Paradiso S, O\u2019Leary DS. Cognitive dysmetria\u201d as an integrative theory of schizophrenia: a dysfunction in cortical-subcortical-cerebellar circuitry? Schizophr Bull. 1998;24:203\u201318.","journal-title":"Schizophr Bull"},{"key":"2021_CR48","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1093\/schbul\/sbm049","volume":"34","author":"H Picard","year":"2008","unstructured":"Picard H, Amado I, Mouchet-Mages S, Olie JP, Krebs MO. The role of the cerebellum in schizophrenia: an update of clinical, cognitive, and functional evidences. Schizophr Bull. 2008;34:155\u201372.","journal-title":"Schizophr Bull"},{"key":"2021_CR49","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1093\/schbul\/sbr134","volume":"38","author":"P Fusar-Poli","year":"2012","unstructured":"Fusar-Poli P, Radua J, McGuire P, Borgwardt S. Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naive VBM studies. Schizophr Bull. 2012;38:1297\u2013307.","journal-title":"Schizophr Bull"},{"key":"2021_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-06350-7","volume":"9","author":"H Cao","year":"2018","unstructured":"Cao H, Ch\u00e9n OY, Chung Y, Forsyth JK, McEwen SC, Gee DG, et al. Cerebello-thalamo-cortical hyperconnectivity as a state-independent functional neural signature for psychosis prediction and characterization. Nat Commun. 2018;9:3836.","journal-title":"Nat Commun"},{"key":"2021_CR51","doi-asserted-by":"publisher","first-page":"13170","DOI":"10.1073\/pnas.0700668104","volume":"104","author":"D Mantini","year":"2007","unstructured":"Mantini D, Perrucci MG, Del Gratta C, Romani GL, Corbetta M. Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci USA. 2007;104:13170\u20135.","journal-title":"Proc Natl Acad Sci USA"},{"key":"2021_CR52","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/j.tics.2011.08.003","volume":"15","author":"V Menon","year":"2011","unstructured":"Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15:483\u2013506.","journal-title":"Trends Cogn Sci"},{"key":"2021_CR53","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s12264-016-0090-1","volume":"33","author":"ML Hu","year":"2017","unstructured":"Hu ML, Zong XF, Mann JJ, Zheng JJ, Liao YH, Li ZC, et al. A review of the functional and anatomical default mode network in Schizophrenia. Neurosci Bull. 2017;33:73\u201384.","journal-title":"Neurosci Bull"},{"key":"2021_CR54","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.biopsych.2008.03.023","volume":"64","author":"PD Butler","year":"2008","unstructured":"Butler PD, Silverstein SM, Dakin SC. Visual perception and its impairment in schizophrenia. Biol Psychiatry. 2008;64:40\u20137.","journal-title":"Biol Psychiatry"},{"key":"2021_CR55","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1093\/schbul\/sbp110","volume":"35","author":"DC Javitt","year":"2009","unstructured":"Javitt DC. Sensory processing in schizophrenia: neither simple nor intact. Schizophr Bull. 2009;35:1059\u201364.","journal-title":"Schizophr Bull"},{"key":"2021_CR56","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.biopsych.2009.08.005","volume":"67","author":"R Lencer","year":"2010","unstructured":"Lencer R, Reilly JL, Harris MS, Sprenger A, Keshavan MS, Sweeney JA. Sensorimotor transformation deficits for smooth pursuit in first-episode affective psychoses and schizophrenia. Biol Psychiatry. 2010;67:217\u201323.","journal-title":"Biol Psychiatry"},{"key":"2021_CR57","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1176\/appi.ajp.2014.13121691","volume":"172","author":"DC Javitt","year":"2015","unstructured":"Javitt DC, Freedman R. Sensory processing dysfunction in the personal experience and neuronal machinery of schizophrenia. Am J Psychiatry. 2015;172:17\u201331.","journal-title":"Am J Psychiatry"}],"container-title":["Neuropsychopharmacology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41386-024-02021-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41386-024-02021-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41386-024-02021-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T08:19:09Z","timestamp":1736929149000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41386-024-02021-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,6]]},"references-count":57,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2021"],"URL":"https:\/\/doi.org\/10.1038\/s41386-024-02021-y","relation":{},"ISSN":["0893-133X","1740-634X"],"issn-type":[{"value":"0893-133X","type":"print"},{"value":"1740-634X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,6]]},"assertion":[{"value":"17 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"WZ consult to VeraSci. The remaining authors report no financial relationships with commercial interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}