{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:08:07Z","timestamp":1789517287754,"version":"build-2803163510"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-02093-2","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T06:24:38Z","timestamp":1765261478000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A novel strategy for enhanced schizophrenia detection using established CNN architectures"],"prefix":"10.1186","volume":"26","author":[{"given":"Ali","family":"Allahgholi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keivan","family":"Maghooli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babak","family":"Gholamine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"2093_CR1","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1038\/nature09552","volume":"468","author":"TR Insel","year":"2010","unstructured":"Insel TR. Rethinking schizophrenia. Nature. 2010;468:187\u201393.","journal-title":"Nature"},{"key":"2093_CR2","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1176\/appi.ajp.2008.07101562","volume":"165","author":"I Ellison-Wright","year":"2008","unstructured":"Ellison-Wright I, Glahn DC, Laird AR, Thelen SM, Bullmore E. The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. Am J Psychiatry. 2008;165:1015\u201323.","journal-title":"Am J Psychiatry"},{"key":"2093_CR3","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.biopsych.2008.03.031","volume":"64","author":"DC Glahn","year":"2008","unstructured":"Glahn DC, et al. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol Psychiatry. 2008;64:774\u201381.","journal-title":"Biol Psychiatry"},{"key":"2093_CR4","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1001\/archpsyc.60.11.1069","volume":"60","author":"K Kasai","year":"2003","unstructured":"Kasai K, et al. Differences and similarities in insular and temporal Pole mri gray matter volume abnormalities in first-episode schizophrenia and affective psychosis. Archiv Gener Psychiatry. 2003;60:1069\u201377.","journal-title":"Archiv Gener Psychiatry"},{"key":"2093_CR5","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1176\/appi.ajp.162.12.2233","volume":"162","author":"R Honea","year":"2005","unstructured":"Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry. 2005;162:2233\u201345.","journal-title":"Am J Psychiatry"},{"key":"2093_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0920-9964(01)00163-3","volume":"49","author":"ME Shenton","year":"2001","unstructured":"Shenton ME, Dickey CC, Frumin M, McCarley RW. A review of mri findings in schizophrenia. Schizophr Res. 2001;49:1\u201352.","journal-title":"Schizophr Res"},{"key":"2093_CR7","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1038\/nrn894","volume":"3","author":"AD Craig","year":"2002","unstructured":"Craig AD. How do you feel? interoception: the sense of the physiological condition of the body. Nat Rev Neurosci. 2002;3:655\u201366.","journal-title":"Nat Rev Neurosci"},{"key":"2093_CR8","doi-asserted-by":"crossref","unstructured":"Kittleson AR, Woodward ND, Heckers S, Sheffield JM. The insula: leveraging cellular and systems-level research to better understand its roles in health and schizophrenia. Neurosci Biobehav Rev. 2024;105643.","DOI":"10.1016\/j.neubiorev.2024.105643"},{"key":"2093_CR9","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.schres.2020.03.068","volume":"220","author":"JM Sheffield","year":"2020","unstructured":"Sheffield JM, Rogers BP, Blackford JU, Heckers S, Woodward ND. Insula functional connectivity in schizophrenia. Schizophr Res. 2020;220:69\u201377.","journal-title":"Schizophr Res"},{"key":"2093_CR10","doi-asserted-by":"publisher","first-page":"103949","DOI":"10.1016\/j.medengphy.2023.103949","volume":"112","author":"M Agarwal","year":"2023","unstructured":"Agarwal M, Singhal A. Fusion of pattern-based and statistical features for schizophrenia detection from eeg signals. Med Eng Phys. 2023;112:103949.","journal-title":"Med Eng Phys"},{"key":"2093_CR11","unstructured":"Aks\u00f6z A, et al. Analysis and classification of schizophrenia using event related potential signals. Comput Sci. 2022;32\u201336."},{"key":"2093_CR12","doi-asserted-by":"crossref","unstructured":"Azizi S, Hier DB, Wunsch DC. Schizophrenia classification using resting state eeg functional connectivity: source level outperforms sensor level. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 1770\u201373 (IEEE, 2021.","DOI":"10.1109\/EMBC46164.2021.9630713"},{"key":"2093_CR13","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1002\/hbm.24863","volume":"41","author":"D Lei","year":"2020","unstructured":"Lei D, et al. Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp. 2020;41:1119\u201335.","journal-title":"Hum Brain Mapp"},{"key":"2093_CR14","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/JBHI.2022.3168357","volume":"27","author":"M Tanveer","year":"2022","unstructured":"Tanveer M, et al. Diagnosis of schizophrenia: a comprehensive evaluation. IEEE J Biomed And Health Inf. 2022;27:1185\u201392.","journal-title":"IEEE J Biomed And Health Inf"},{"key":"2093_CR15","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1038\/s41398-020-00965-5","volume":"10","author":"W Yassin","year":"2020","unstructured":"Yassin W, et al. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Transl Psychiatry. 2020;10:278.","journal-title":"Transl Psychiatry"},{"key":"2093_CR16","doi-asserted-by":"publisher","first-page":"1384842","DOI":"10.3389\/fpsyt.2024.1384842","volume":"15","author":"A Kanyal","year":"2024","unstructured":"Kanyal A, et al. Multi-modal deep learning from imaging genomic data for schizophrenia classification. Front Psychiatry. 2024;15:1384842.","journal-title":"Front Psychiatry"},{"key":"2093_CR17","doi-asserted-by":"publisher","first-page":"631864","DOI":"10.3389\/fcell.2021.631864","volume":"9","author":"X Lin","year":"2021","unstructured":"Lin X, et al. Characteristics of multimodal brain connectomics in patients with schizophrenia and the unaffected first-degree relatives. Front Cell And Dev Biol. 2021;9:631864.","journal-title":"Front Cell And Dev Biol"},{"key":"2093_CR18","doi-asserted-by":"crossref","unstructured":"Rajesh KN, Kumar TS. Schizophrenia detection in adolescents from eeg signals using symmetrically weighted local binary patterns. EMBC. 2021;963\u201366.","DOI":"10.1109\/EMBC46164.2021.9630232"},{"key":"2093_CR19","doi-asserted-by":"publisher","first-page":"122937","DOI":"10.1016\/j.eswa.2023.122937","volume":"246","author":"S Srinivasan","year":"2024","unstructured":"Srinivasan S, Johnson SD. A novel approach to schizophrenia detection: optimized preprocessing and deep learning analysis of multichannel eeg data. Expert Syst With Appl. 2024;246:122937.","journal-title":"Expert Syst With Appl"},{"key":"2093_CR20","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.neunet.2015.04.002","volume":"68","author":"D Chyzhyk","year":"2015","unstructured":"Chyzhyk D, Savio A, Gra\u00f1a M. Computer aided diagnosis of schizophrenia on resting state fmri data by ensembles of elm. Neural Networks. 2015;68:23\u201333.","journal-title":"Neural Networks"},{"key":"2093_CR21","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s12021-013-9184-3","volume":"11","author":"RL Gollub","year":"2013","unstructured":"Gollub RL, et al. The mcic collection: a shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia. Neuroinformatics. 2013;11:367\u201388.","journal-title":"Neuroinformatics"},{"key":"2093_CR22","unstructured":"Bilder R, et al. Ucla consortium for neuropsychiatric phenomics la5c study. 2018."},{"key":"2093_CR23","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1016\/j.neuroimage.2015.06.065","volume":"124","author":"L Wang","year":"2016","unstructured":"Wang L, et al. Schizconnect: mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration. Neuroimage. 2016;124:1155\u201367.","journal-title":"Neuroimage"},{"key":"2093_CR24","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1176\/ajp.156.4.544","volume":"156","author":"DG Robinson","year":"1999","unstructured":"Robinson DG, et al. Predictors of treatment response from a first episode of schizophrenia or schizoaffective disorder. Am J Psychiatry. 1999;156:544\u201349.","journal-title":"Am J Psychiatry"},{"key":"2093_CR25","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1176\/appi.ajp.162.3.602","volume":"162","author":"PR Szeszko","year":"2005","unstructured":"Szeszko PR, et al. White matter abnormalities in first-episode schizophrenia or schizoaffective disorder: a diffusion tensor imaging study. Am J Psychiatry. 2005;162:602\u201305.","journal-title":"Am J Psychiatry"},{"key":"2093_CR26","doi-asserted-by":"publisher","first-page":"e0276975","DOI":"10.1371\/journal.pone.0276975","volume":"17","author":"J Soler-Vidal","year":"2022","unstructured":"Soler-Vidal J, et al. Brain correlates of speech perception in schizophrenia patients with and without auditory hallucinations. PLoS One. 2022;17:e0276975.","journal-title":"PLoS One"},{"key":"2093_CR27","doi-asserted-by":"crossref","unstructured":"Manj\u00f3n JV. Mri preprocessing. Imaging biomarkers: development and clinical integration 53\u201363. 2017.","DOI":"10.1007\/978-3-319-43504-6_5"},{"key":"2093_CR28","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/radiol.11091710","volume":"261","author":"B Van Ginneken","year":"2011","unstructured":"Van Ginneken B, Schaefer-Prokop CM, Prokop M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology. 2011;261:719\u201332.","journal-title":"Radiology"},{"key":"2093_CR29","doi-asserted-by":"crossref","unstructured":"Buades A, Coll B, Morel J-M. A non-local algorithm for image denoising. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905). Ieee; 2005, 60\u201365, vol. 2.","DOI":"10.1109\/CVPR.2005.38"},{"key":"2093_CR30","doi-asserted-by":"publisher","first-page":"119474","DOI":"10.1016\/j.neuroimage.2022.119474","volume":"260","author":"A Hoopes","year":"2022","unstructured":"Hoopes A, Mora JS, Dalca AV, Fischl B, Hoffmann M. Synthstrip: skull-stripping for any brain image. NeuroImage. 2022;260:119474. https:\/\/doi.org\/10.1016\/j.neuroimage.2022.119474.","journal-title":"NeuroImage"},{"key":"2093_CR31","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","volume":"30","author":"GJ Brostow","year":"2009","unstructured":"Brostow GJ, Fauqueur J, Cipolla R. Semantic object classes in video: a high-definition ground truth database. Pattern Recognit Lett. 2009;30:88\u201397.","journal-title":"Pattern Recognit Lett"},{"key":"2093_CR32","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV). 2018.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2093_CR33","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016, 770\u201378, https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2093_CR34","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/s00371-008-0231-2","volume":"24","author":"GR Kuhn","year":"2008","unstructured":"Kuhn GR, Oliveira MM, Fernandes LA. An improved contrast enhancing approach for color-to-grayscale mappings. The Visual Comput. 2008;24:505\u201314.","journal-title":"The Visual Comput"},{"key":"2093_CR35","doi-asserted-by":"crossref","unstructured":"Mazhari A, Allahgholi A, Shafieian M. Automated detection of sdh and edh due to tbi from ct-scan images using cnn. 2023 30th National and 8th International Iranian Conference on Biomedical Engineering (ICBME). IEEE; 2023, 164\u201370.","DOI":"10.1109\/ICBME61513.2023.10488625"},{"key":"2093_CR36","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3389\/neuro.01.041.2008","volume":"2","author":"K Hugdahl","year":"2008","unstructured":"Hugdahl K, et al. Auditory hallucinations in schizophrenia: the role of cognitive, brain structural and genetic disturbances in the left temporal lobe. Front Hum Neurosci. 2008;2:131.","journal-title":"Front Hum Neurosci"},{"key":"2093_CR37","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1038\/mp.2008.34","volume":"14","author":"D Sun","year":"2009","unstructured":"Sun D, et al. Brain surface contraction mapped in first-episode schizophrenia: a longitudinal magnetic resonance imaging study. Mol Psychiatry. 2009;14:976\u201386.","journal-title":"Mol Psychiatry"},{"key":"2093_CR38","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1038\/sj.npp.1300850","volume":"30","author":"S Rg","year":"2005","unstructured":"Rg S. Measurement of brain metabolites by 1h magnetic resonance spectroscopy in patients with schizophrenia; a systematic review and meta-analysis. Neuropsychopharmacology. 2005;30:1949\u201362.","journal-title":"Neuropsychopharmacology"},{"key":"2093_CR39","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.schres.2010.08.027","volume":"123","author":"KP Wylie","year":"2010","unstructured":"Wylie KP, Tregellas JR. The role of the insula in schizophrenia. Schizophr Res. 2010;123:93\u2013104.","journal-title":"Schizophr Res"},{"key":"2093_CR40","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.schres.2011.03.020","volume":"129","author":"L Palaniyappan","year":"2011","unstructured":"Palaniyappan L, Mallikarjun P, Joseph V, White TP, Liddle PF. Regional contraction of brain surface area involves three large-scale networks in schizophrenia. Schizophr Res. 2011;129:163\u201368.","journal-title":"Schizophr Res"},{"key":"2093_CR41","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s00429-010-0262-0","volume":"214","author":"V Menon","year":"2010","unstructured":"Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct And Function. 2010;214:655\u201367.","journal-title":"Brain Struct And Function"},{"key":"2093_CR42","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1097\/WNP.0000000000000377","volume":"34","author":"LQ Uddin","year":"2017","unstructured":"Uddin LQ, Nomi JS, H\u00e9bert-Seropian B, Ghaziri J, Boucher O. Structure and function of the human insula. J Clin Neurophysiol. 2017;34:300\u201306.","journal-title":"J Clin Neurophysiol"},{"key":"2093_CR43","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/nrn2555","volume":"10","author":"AD Craig","year":"2009","unstructured":"Craig AD. How do you feel\u2014now? the anterior insula and human awareness. Nat Rev Neurosci. 2009;10:59\u201370.","journal-title":"Nat Rev Neurosci"},{"key":"2093_CR44","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.schres.2013.12.013","volume":"152","author":"EM Poels","year":"2014","unstructured":"Poels EM, et al. Glutamatergic abnormalities in schizophrenia: a review of proton mrs findings. Schizophr Res. 2014;152:325\u201332.","journal-title":"Schizophr Res"},{"key":"2093_CR45","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1001\/jamapsychiatry.2016.0442","volume":"73","author":"K Merritt","year":"2016","unstructured":"Merritt K, Egerton A, Kempton MJ, Taylor MJ, McGuire PK. Nature of glutamate alterations in schizophrenia: a meta-analysis of proton magnetic resonance spectroscopy studies. JAMA Psychiatry. 2016;73:665\u201374.","journal-title":"JAMA Psychiatry"},{"key":"2093_CR46","doi-asserted-by":"publisher","first-page":"5215","DOI":"10.12998\/wjcc.v11.i22.5215","volume":"11","author":"X-J Xu","year":"2023","unstructured":"Xu X-J, Liu T-L, He L, Pu B. Changes in neurotransmitter levels, brain structural characteristics, and their correlation with panss scores in patients with first-episode schizophrenia. World J Clin Cases. 2023;11:5215.","journal-title":"World J Clin Cases"},{"key":"2093_CR47","doi-asserted-by":"publisher","first-page":"1000560","DOI":"10.3389\/fpsyt.2022.1000560","volume":"13","author":"Z Huang","year":"2022","unstructured":"Huang Z, et al. Negative symptoms correlate with altered brain structural asymmetry in amygdala and superior temporal region in schizophrenia patients. Front Psychiatry. 2022;13:1000560.","journal-title":"Front Psychiatry"},{"key":"2093_CR48","doi-asserted-by":"crossref","unstructured":"Chuang J, et al. Brain structural signatures of negative symptoms in depression and schizophrenia. front psychiatry. 2014;5:116.","DOI":"10.3389\/fpsyt.2014.00116"},{"key":"2093_CR49","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1038\/npp.2010.156","volume":"36","author":"TA Lesh","year":"2011","unstructured":"Lesh TA, Niendam TA, Minzenberg MJ, Carter CS. Cognitive control deficits in schizophrenia: mechanisms and meaning. Neuropsychopharmacology. 2011;36:316\u201338.","journal-title":"Neuropsychopharmacology"},{"key":"2093_CR50","doi-asserted-by":"publisher","first-page":"930","DOI":"10.3389\/fnhum.2013.00930","volume":"7","author":"A Manoliu","year":"2014","unstructured":"Manoliu A, et al. Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Front Hum Neurosci. 2014;7:930.","journal-title":"Front Hum Neurosci"},{"key":"2093_CR51","doi-asserted-by":"publisher","first-page":"3116","DOI":"10.1093\/cercor\/bht165","volume":"24","author":"A Anticevic","year":"2014","unstructured":"Anticevic A, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24:3116\u201330.","journal-title":"Cereb Cortex"},{"key":"2093_CR52","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1017\/S0033291717001283","volume":"48","author":"E Walton","year":"2018","unstructured":"Walton E, et al. Prefrontal cortical thinning links to negative symptoms in schizophrenia via the enigma consortium. Psychological Med. 2018;48:82\u201394.","journal-title":"Psychological Med"},{"key":"2093_CR53","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.biopsych.2019.06.013","volume":"86","author":"V Kebets","year":"2019","unstructured":"Kebets V, et al. Somatosensory-motor dysconnectivity spans multiple transdiagnostic dimensions of psychopathology. Biol Psychiatry. 2019;86:779\u201391.","journal-title":"Biol Psychiatry"},{"key":"2093_CR54","unstructured":"Inc. TM. Matlab version: 9.13.0 (r2022b). 2022."},{"key":"2093_CR55","doi-asserted-by":"publisher","first-page":"14433","DOI":"10.1038\/s41598-023-41359-z","volume":"13","author":"J Zhang","year":"2023","unstructured":"Zhang J, et al. Detecting schizophrenia with 3d structural brain mri using deep learning. Sci Rep. 2023;13:14433.","journal-title":"Sci Rep"},{"key":"2093_CR56","doi-asserted-by":"publisher","first-page":"105028","DOI":"10.1016\/j.compbiomed.2021.105028","volume":"141","author":"SK Khare","year":"2022","unstructured":"Khare SK, Bajaj V. A hybrid decision support system for automatic detection of schizophrenia using eeg signals. Comput In Biol Med. 2022;141:105028.","journal-title":"Comput In Biol And Med"},{"key":"2093_CR57","doi-asserted-by":"crossref","unstructured":"De Rosa A, et al. Machine learning algorithm unveils glutamatergic alterations in the post-mortem schizophrenia brain. Schizophrenia. 2022;8, 8.","DOI":"10.1038\/s41537-022-00231-1"},{"key":"2093_CR58","doi-asserted-by":"publisher","first-page":"893788","DOI":"10.3389\/fninf.2022.893788","volume":"16","author":"E Santos Febles","year":"2022","unstructured":"Santos Febles E, Ontivero Ortega M, Vald\u00e9s Sosa M, Sahli H. Machine learning techniques for the diagnosis of schizophrenia based on event-related potentials. Front Neuroinf. 2022;16:893788.","journal-title":"Front Neuroinf"},{"key":"2093_CR59","doi-asserted-by":"publisher","first-page":"e0265300","DOI":"10.1371\/journal.pone.0265300","volume":"17","author":"AH Algumaei","year":"2022","unstructured":"Algumaei AH, Algunaid RF, Rushdi MA, Yassine IA. Feature and decision-level fusion for schizophrenia detection based on resting-state fmri data. PLoS One. 2022;17:e0265300.","journal-title":"PLoS One"},{"key":"2093_CR60","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s40846-022-00758-9","volume":"42","author":"M\u00c1 Luj\u00e1n","year":"2022","unstructured":"Luj\u00e1n M\u00c1, et al. Mental disorder diagnosis from eeg signals employing automated leaning procedures based on radial basis functions. J Med Biol Eng. 2022;42:853\u201359.","journal-title":"J Med And Biol Eng"},{"key":"2093_CR61","doi-asserted-by":"publisher","first-page":"2250013","DOI":"10.1142\/S0129065722500137","volume":"32","author":"A Zandbagleh","year":"2022","unstructured":"Zandbagleh A, Mirzakuchaki S, Daliri MR, Premkumar P, Sanei S. Classification of low and high schizotypy levels via evaluation of brain connectivity. Int J Neural Syst. 2022;32:2250013.","journal-title":"Int J Neural Syst"},{"key":"2093_CR62","doi-asserted-by":"crossref","unstructured":"Shi D, et al. Machine learning of schizophrenia detection with structural and functional neuroimaging. Disease Markers. 2021 (2021;9963824.","DOI":"10.1155\/2021\/9963824"},{"key":"2093_CR63","first-page":"45","volume":"37","author":"X Du","year":"2020","unstructured":"Du X, et al. Research on electroencephalogram specifics in patients with schizophrenia under cognitive load. Sheng wu yi xue Gong Cheng xue za zhi=. J Biomed Eng= Shengwu Yixue Gongchengxue Zazhi. 2020;37:45\u201353.","journal-title":"J Biomed Eng= Shengwu Yixue Gongchengxue Zazhi"},{"key":"2093_CR64","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1093\/schbul\/sby189","volume":"46","author":"S Vieira","year":"2020","unstructured":"Vieira S, et al. Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence. Schizophr Bull. 2020;46:17\u201326.","journal-title":"Schizophr Bull"},{"key":"2093_CR65","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1007\/s00429-020-02078-7","volume":"225","author":"H Yang","year":"2020","unstructured":"Yang H, Di X, Gong Q, Sweeney J, Biswal B. Investigating inhibition deficit in schizophrenia using task-modulated brain networks. Brain Struct And Function. 2020;225:1601\u201313.","journal-title":"Brain Struct And Function"},{"key":"2093_CR66","unstructured":"Tanveer M. Investigating white matter abnormalities associated with schizophrenia using deep learning model and voxel-based morphometry. 2023."},{"key":"2093_CR67","doi-asserted-by":"publisher","first-page":"1075564","DOI":"10.3389\/fpsyt.2022.1075564","volume":"13","author":"Y Wen","year":"2023","unstructured":"Wen Y, et al. Bridging structural mri with cognitive function for individual level classification of early psychosis via deep learning. Front Psychiatry. 2023;13:1075564.","journal-title":"Front Psychiatry"},{"key":"2093_CR68","unstructured":"Shenton ME, Whitford TJ, Kubicki M. Structural neuroimaging in schizophrenia from methods to insights to treatments. Dialogues in clinical neuroscience. 2022."}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02093-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-02093-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-02093-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T03:04:52Z","timestamp":1768359892000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12880-025-02093-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":68,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2093"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-02093-2","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]},"assertion":[{"value":"7 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable. All authors have approved the final manuscript and consent to its publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"27"}}