Fraguas D, Díaz-Caneja CM, Pina-Camacho L, Janssen J, Arango C. Progressive brain changes in children and adolescents with early-onset psychosis: A meta-analysis of longitudinal MRI studies. Schizophr Res 2016, 173: 132–139.

Article  PubMed  Google Scholar 

Moylan S, Maes M, Wray NR, Berk M. The neuroprogressive nature of major depressive disorder: Pathways to disease evolution and resistance, and therapeutic implications. Mol Psychiatry 2013, 18: 595–606.

Article  CAS  PubMed  Google Scholar 

Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE. Global burden of disease attributable to mental and substance use disorders: Findings from the global burden of disease study 2010. Lancet 2013, 382: 1575–1586.

Article  PubMed  Google Scholar 

Ma S, Chen T, Jia W, Liu J, Ding S, Li P, et al. Enhanced Beta2-band oscillations denote auditory hallucination in schizophrenia patients and a monkey model of psychosis. Neurosci Bull 2024, 40: 325–338.

Article  CAS  PubMed  Google Scholar 

Same K, Shobeiri P, Rashidi MM, Ghasemi E, Saeedi Moghaddam S, Mohammadi E, et al. A global, regional, and national burden and quality of care index for schizophrenia: Global burden of disease systematic analysis 1990–2019. Schizophr Bull 2024, 50: 1083–1093.

Article  PubMed  Google Scholar 

Andleeb H, Moltrecht B, Gayer-Anderson C, Arango C, Arrojo M, D’Andrea G, et al. Age-at-migration, ethnicity and psychosis risk: Findings from the EU-GEI case-control study. PLoS Ment Health 2024, 1: e0000134.

Article  Google Scholar 

Domschke K. Prevention in psychiatry: A role for epigenetics? World Psychiatry 2021, 20: 227–228.

Article  PubMed  PubMed Central  Google Scholar 

Ecker C, Rocha-Rego V, Johnston P, Mourao-Miranda J, Marquand A, Daly EM, et al. Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach. Neuroimage 2010, 49: 44–56.

Article  PubMed  Google Scholar 

Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Dysconnectivity in schizophrenia: Where are we now? Neurosci Biobehav Rev 2011, 35: 1110–1124.

Article  PubMed  Google Scholar 

Talpalaru A, Bhagwat N, Devenyi GA, Lepage M, Mallar Chakravarty M. Identifying schizophrenia subgroups using clustering and supervised learning. Schizophr Res 2019, 214: 51–59.

Article  PubMed  Google Scholar 

Xiao Y, Yan Z, Zhao Y, Tao B, Sun H, Li F, et al. Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI. Schizophr Res 2019, 214: 11–17.

Article  PubMed  Google Scholar 

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: 1742–1751.

Article  PubMed  PubMed Central  Google Scholar 

Skåtun KC, Kaufmann T, Doan NT, Alnæs D, Córdova-Palomera A, Jönsson EG, et al. Consistent functional connectivity alterations in schizophrenia spectrum disorder: A multisite study. Schizophr Bull 2017, 43: 914–924.

Article  PubMed  Google Scholar 

Cao B, Cho RY, Chen D, Xiu M, Wang L, Soares JC, et al. Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity. Mol Psychiatry 2020, 25: 906–913.

Article  PubMed  Google Scholar 

Liang S, Li Y, Zhang Z, Kong X, Wang Q, Deng W, et al. Classification of first-episode schizophrenia using multimodal brain features: A combined structural and diffusion imaging study. Schizophr Bull 2019, 45: 591–599.

Article  PubMed  Google Scholar 

Cetin-Karayumak S, Di Biase MA, Chunga N, Reid B, Somes N, Lyall AE, et al. White matter abnormalities across the lifespan of schizophrenia: A harmonized multi-site diffusion MRI study. Mol Psychiatry 2020, 25: 3208–3219.

Article  PubMed  Google Scholar 

Mikolas P, Hlinka J, Skoch A, Pitra Z, Frodl T, Spaniel F, et al. Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry 2018, 18: 97.

Article  PubMed  PubMed Central  Google Scholar 

Ingalhalikar M, Kanterakis S, Gur R, Roberts TPL, Verma R. DTI based diagnostic prediction of a disease via pattern classification. Med Image Comput Comput Assist Interv 2010, 13: 558–565.

PubMed  Google Scholar 

Lei D, Pinaya WHL, Young J, van Amelsvoort T, Marcelis M, Donohoe G, et al. Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual. Hum Brain Mapp 2020, 41: 1119–1135.

Article  PubMed  Google Scholar 

Wang J, Ke P, Zang J, Wu F, Wu K. Discriminative analysis of schizophrenia patients using topological properties of structural and functional brain networks: A multimodal magnetic resonance imaging study. Front Neurosci 2022, 15: 785595.

Article  PubMed  PubMed Central  Google Scholar 

Lu XB, Zhang Y, Yang DY, Yang YZ, Wu FC, Ning YP, et al. Analysis of first-episode and chronic schizophrenia using multi-modal magnetic resonance imaging. Eur Rev Med Pharmacol Sci 2018, 22: 6422–6435.

PubMed  Google Scholar 

Yang Y, Zhang Y, Wu F, Lu X, Ning Y, Huang B, et al. Automatic classification of first-episode, drug-naive schizophrenia with multi-modal magnetic resonance imaging. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2017, 34: 674–680.

CAS  PubMed  Google Scholar 

Chen X, Zhou J, Ke P, Huang J, Xiong D, Huang Y, et al. Classification of schizophrenia patients using a graph convolutional network: A combined functional MRI and connectomics analysis. Biomed Signal Process Contr 2023, 80: 104293.

Article  CAS  Google Scholar 

Gao J, Qian M, Wang Z, Li Y, Luo N, Xie S, et al. Exploring schizophrenia classification through multimodal MRI and deep graph neural networks: Unveiling brain region-specific weight discrepancies and their association with cell-type specific transcriptomic features. Schizophr Bull 2024, 51: 217–235.

Article  PubMed  Google Scholar 

Lei D, Qin K, Pinaya WHL, Young J, Van Amelsvoort T, Marcelis M, et al. Graph convolutional networks reveal network-level functional dysconnectivity in schizophrenia. Schizophr Bull 2022, 48: 881–892.

Article  PubMed  PubMed Central  Google Scholar 

Ripke S, Walters J, O’Donovan M. Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia. medRxiv 2016, https://doi.org/10.1101/2020.09.12.20192922.

Article  Google Scholar 

Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 2016, 19: 1442–1453.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Guan F, Ni T, Zhu W, Keoki Williams L, Cui LB, Li M, et al. Integrative omics of schizophrenia: From genetic determinants to clinical classification and risk prediction. Mol Psychiatry 2022, 27: 113–126.

Article  PubMed  Google Scholar 

Cui Z, Zhong S, Xu P, He Y, Gong G. PANDA: A pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 2013, 7: 42.

Article  PubMed  PubMed Central  Google Scholar 

Lin L, Jin C, Fu Z, Zhang B, Bin G, Wu S. Predicting healthy older adult’s brain age based on structural connectivity networks using artificial neural networks. Comput Methods Programs Biomed 2016, 125: 8–17.

Article  PubMed  Google Scholar 

Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, et al. The human brainnetome atlas: A new brain atlas based on connectional architecture. Cereb Cortex 2016, 26: 3508–3526.

Article  PubMed  PubMed Central  Google Scholar 

Long Z, Duan X, Wang Y, Liu F, Zeng L, Zhao JP, et al. Disrupted structural connectivity network in treatment-naive depression. Prog Neuropsychopharmacol Biol Psychiatry 2015, 56: 18–26.

Article  PubMed  Google Scholar 

Wang B, Fan Y, Lu M, Li S, Song Z, Peng X, et al. Brain anatomical networks in world class gymnasts: A DTI tractography study. Neuroimage 2013, 65: 476–487.

Article  PubMed  Google Scholar 

Shu N, Liu Y, Li K, Duan Y, Wang J, Yu C, et al. Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cereb Cortex 2011, 21: 2565–2577.