Skip to main content

Advertisement

SpringerLink
Log in

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

  • Review
  • Published:
Neuroscience Bulletin Aims and scope Submit manuscript

Abstract

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Charlson F, van Ommeren M, Flaxman A, Cornett J, Whiteford H, Saxena S. New WHO prevalence estimates of mental disorders in conflict settings: A systematic review and meta-analysis. Lancet 2019, 394: 240–248.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Fleischhacker WW, Arango C, Arteel P, Barnes TRE, Carpenter W, Duckworth K. Schizophrenia—time to commit to policy change. Schizophr Bull 2014, 40: S165–S194.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hjorthøj C, Stürup AE, McGrath JJ, Nordentoft M. Years of potential life lost and life expectancy in schizophrenia: A systematic review and meta-analysis. Lancet Psychiatry 2017, 4: 295–301.

    Article  PubMed  Google Scholar 

  4. Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 2022, 604: 502–508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Xu MQ, Sun WS, Liu BX, Feng GY, Yu L, Yang L, et al. Prenatal malnutrition and adult schizophrenia: Further evidence from the 1959–1961 Chinese famine. Schizophr Bull 2009, 35: 568–576.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wu Q, Wang X, Wang Y, Long YJ, Zhao JP, Wu RR. Developments in biological mechanisms and treatments for negative symptoms and cognitive dysfunction of schizophrenia. Neurosci Bull 2021, 37: 1609–1624.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Saugstad LF. Social class, marriage, and fertility in schizophrenia. Schizophr Bull 1989, 15: 9–43.

    Article  CAS  PubMed  Google Scholar 

  8. Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, et al. Candidate biomarkers in psychiatric disorders: State of the field. World Psychiatry 2023, 22: 236–262.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Konopaske GT, Lange N, Coyle JT, Benes FM. Prefrontal cortical dendritic spine pathology in schizophrenia and bipolar disorder. JAMA Psychiatry 2014, 71: 1323–1331.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Selemon LD, Zecevic N. Schizophrenia: A tale of two critical periods for prefrontal cortical development. Transl Psychiatry 2015, 5: e623.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Abi-Dargham A, Horga G. The search for imaging biomarkers in psychiatric disorders. Nat Med 2016, 22: 1248–1255.

    Article  CAS  PubMed  Google Scholar 

  12. Meyer-Lindenberg A, Weinberger DR. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci 2006, 7: 818–827.

    Article  CAS  PubMed  Google Scholar 

  13. Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JLR, Griffanti L, Douaud G, et al. Image processing and quality control for the first 10, 000 brain imaging datasets from UK Biobank. Neuroimage 2018, 166: 400–424.

    Article  PubMed  Google Scholar 

  14. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci 2016, 19: 1523–1536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, et al. The genetic architecture of the human cerebral cortex. Science 2020, 367: eaay6690.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lui JH, Hansen DV, Kriegstein AR. Development and evolution of the human neocortex. Cell 2011, 146: 18–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Silbereis JC, Pochareddy S, Zhu Y, Li M, Sestan N. The cellular and molecular landscapes of the developing human central nervous system. Neuron 2016, 89: 248–268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Geschwind DH, Rakic P. Cortical evolution: Judge the brain by its cover. Neuron 2013, 80: 633–647.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li D, Xie Y. An evolved human-specific epigenetic mechanism for cortical expansion and gyrification. Neurosci Bull 2021, 37: 1370–1372.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, MacKay CE, et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage 2006, 31: 1487–1505.

    Article  PubMed  Google Scholar 

  21. Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 2007, 34: 144–155.

    Article  CAS  PubMed  Google Scholar 

  22. Lope-Piedrafita S. Diffusion tensor imaging (DTI). Methods Mol Biol 2018, 1718: 103–116.

    Article  CAS  PubMed  Google Scholar 

  23. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012, 61: 1000–1016.

    Article  PubMed  Google Scholar 

  24. Heeger DJ, Ress D. What does fMRI tell us about neuronal activity? Nat Rev Neurosci 2002, 3: 142–151.

    Article  CAS  PubMed  Google Scholar 

  25. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001, 98: 676–682.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Spreng RN, Mar RA, Kim ASN. The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: A quantitative meta-analysis. J Cogn Neurosci 2009, 21: 489–510.

    Article  PubMed  Google Scholar 

  27. Raichle ME, Snyder AZ. A default mode of brain function: A brief history of an evolving idea. Neuroimage 2007, 37: 1083–1090;discussion 1097–1099.

  28. Damoiseaux JS. Effects of aging on functional and structural brain connectivity. Neuroimage 2017, 160: 32–40.

    Article  PubMed  Google Scholar 

  29. Sporns O, Tononi G, Kötter R. The human connectome: A structural description of the human brain. PLoS Comput Biol 2005, 1: e42.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Scholtens LH, van den Heuvel MP. Multimodal connectomics in psychiatry: Bridging scales from micro to macro. Biol Psychiatry Cogn Neurosci Neuroimaging 2018, 3: 767–776.

    PubMed  Google Scholar 

  31. Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 2014, 137: 2382–2395.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Griffa A, Baumann PS, Thiran JP, Hagmann P. Structural connectomics in brain diseases. NeuroImage 2013, 80: 515–526.

    Article  PubMed  Google Scholar 

  33. Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, et al. The human connectome project: A retrospective. Neuroimage 2021, 244: 118543.

    Article  CAS  PubMed  Google Scholar 

  34. Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023, 24: 747–760.

    Article  CAS  PubMed  Google Scholar 

  35. de Reus MA, Saenger VM, Kahn RS, van den Heuvel MP. An edge-centric perspective on the human connectome: Link communities in the brain. Philos Trans R Soc Lond B Biol Sci 2014, 369: 20130527.

    Article  PubMed  PubMed Central  Google Scholar 

  36. van den Heuvel MP, Kahn RS, Goñi J, Sporns O. High-cost, high-capacity backbone for global brain communication. Proc Natl Acad Sci U S A 2012, 109: 11372–11377.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ge J, Yang G, Han M, Zhou S, Men W, Qin L, et al. Increasing diversity in connectomics with the Chinese human connectome project. Nat Neurosci 2023, 26: 163–172.

    Article  CAS  PubMed  Google Scholar 

  38. Chiang MC, Barysheva M, Shattuck DW, Lee AD, Madsen SK, Avedissian C, et al. Genetics of brain fiber architecture and intellectual performance. J Neurosci 2009, 29: 2212–2224.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bearden CE, van Erp TGM, Thompson PM, Toga AW, Cannon TD. Cortical mapping of genotype-phenotype relationships in schizophrenia. Hum Brain Mapp 2007, 28: 519–532.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Blokland GA, McMahon KL, Hoffman J, Zhu G, Meredith M, Martin NG, et al. Quantifying the heritability of task-related brain activation and performance during the N-back working memory task: A twin fMRI study. Biol Psychol 2008, 79: 70–79.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Glahn DC, Winkler AM, Kochunov P, Almasy L, Duggirala R, Carless MA, et al. Genetic control over the resting brain. Proc Natl Acad Sci U S A 2010, 107: 1223–1228.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Meda SA, Ruaño G, Windemuth A, O’Neil K, Berwise C, Dunn SM, et al. Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia. Proc Natl Acad Sci U S A 2014, 111: E2066–E2075.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bohlken MM, Mandl RCW, Brouwer RM, van den Heuvel MP, Hedman AM, Kahn RS, et al. Heritability of structural brain network topology: A DTI study of 156 twins. Hum Brain Mapp 2014, 35: 5295–5305.

    Article  PubMed  PubMed Central  Google Scholar 

  44. van den Heuvel MP, van Soelen IL, Stam CJ, Kahn RS, Boomsma DI, Hulshoff Pol HE. Genetic control of functional brain network efficiency in children. Eur Neuropsychopharmacol 2013, 23: 19–23.

    Article  PubMed  Google Scholar 

  45. Romme IAC, de Reus MA, Ophoff RA, Kahn RS, van den Heuvel MP. Connectome disconnectivity and cortical gene expression in patients with schizophrenia. Biol Psychiatry 2017, 81: 495–502.

    Article  CAS  PubMed  Google Scholar 

  46. Wheeler AL, Felsky D, Viviano JD, Stojanovski S, Ameis SH, Szatmari P, et al. BDNF-dependent effects on amygdala-cortical circuitry and depression risk in children and youth. Cereb Cortex 2018, 28: 1760–1770.

    Article  PubMed  Google Scholar 

  47. Paul LK, Brown WS, Adolphs R, Tyszka JM, Richards LJ, Mukherjee P, et al. Agenesis of the corpus callosum: Genetic, developmental and functional aspects of connectivity. Nat Rev Neurosci 2007, 8: 287–299.

    Article  CAS  PubMed  Google Scholar 

  48. Gong X, Lu W, Kendrick KM, Pu W, Wang C, Jin L, et al. A brain-wide association study of DISC1 genetic variants reveals a relationship with the structure and functional connectivity of the precuneus in schizophrenia. Hum Brain Mapp 2014, 35: 5414–5430.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Liu B, Fan L, Cui Y, Zhang X, Hou B, Li Y, et al. DISC1 Ser704Cys impacts thalamic-prefrontal connectivity. Brain Struct Funct 2015, 220: 91–100.

    Article  PubMed  Google Scholar 

  50. Liu B, Zhang X, Hou B, Li J, Qiu C, Qin W, et al. The impact of MIR137 on dorsolateral prefrontal-hippocampal functional connectivity in healthy subjects. Neuropsychopharmacology 2014, 39: 2153–2160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Egan MF, Goldberg TE, Kolachana BS, Callicott JH, Mazzanti CM, Straub RE, et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci U S A 2001, 98: 6917–6922.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Donohoe G, Morris DW, Clarke S, McGhee KA, Schwaiger S, Nangle JM, et al. Variance in neurocognitive performance is associated with dysbindin-1 in schizophrenia: A preliminary study. Neuropsychologia 2007, 45: 454–458.

    Article  PubMed  Google Scholar 

  53. Donohoe G, Rose E, Frodl T, Morris D, Spoletini I, Adriano F, et al. ZNF804A risk allele is associated with relatively intact gray matter volume in patients with schizophrenia. Neuroimage 2011, 54: 2132–2137.

    Article  CAS  PubMed  Google Scholar 

  54. Egan MF, Straub RE, Goldberg TE, Yakub I, Callicott JH, Hariri AR, et al. Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. Proc Natl Acad Sci U S A 2004, 101: 12604–12609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Otnaess MK, Djurovic S, Rimol LM, Kulle B, Kähler AK, Jönsson EG, et al. Evidence for a possible association of neurotrophin receptor (NTRK-3) gene polymorphisms with hippocampal function and schizophrenia. Neurobiol Dis 2009, 34: 518–524.

    Article  CAS  PubMed  Google Scholar 

  56. Nicodemus KK, Law AJ, Radulescu E, Luna A, Kolachana B, Vakkalanka R, et al. Biological validation of increased schizophrenia risk with NRG1, ERBB4, and AKT1 epistasis via functional neuroimaging in healthy controls. Arch Gen Psychiatry 2010, 67: 991–1001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lin Z, Long Y, Wu Z, Xiang Z, Ju Y, Liu Z. Associations between brain abnormalities and common genetic variants for schizophrenia: A narrative review of structural and functional neuroimaging findings. Ann Palliat Med 2021, 10: 10031–10052.

    Article  PubMed  Google Scholar 

  58. Taquet M, Smith SM, Prohl AK, Peters JM, Warfield SK, Scherrer B, et al. A structural brain network of genetic vulnerability to psychiatric illness. Mol Psychiatry 2021, 26: 2089–2100.

    Article  PubMed  Google Scholar 

  59. Gong Q, Hu X, Pettersson-Yeo W, Xu X, Lui S, Crossley N, et al. Network-level dysconnectivity in drug-Naïve first-episode psychosis: Dissociating transdiagnostic and diagnosis-specific alterations. Neuropsychopharmacology 2017, 42: 933–940.

    Article  PubMed  Google Scholar 

  60. Smith SM, Douaud G, Chen W, Hanayik T, Alfaro-Almagro F, Sharp K, et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci 2021, 24: 737–745.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, Douaud G, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 2018, 562: 210–216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivières S, Jahanshad N, et al. Common genetic variants influence human subcortical brain structures. Nature 2015, 520: 224–229.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hibar DP, Adams HHH, Jahanshad N, Chauhan G, Stein JL, Hofer E, et al. Novel genetic loci associated with hippocampal volume. Nat Commun 2017, 8: 13624.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Satizabal CL, Adams HHH, Hibar DP, White CC, Knol MJ, Stein JL, et al. Genetic architecture of subcortical brain structures in 38, 851 individuals. Nat Genet 2019, 51: 1624–1636.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Flint J, Timpson N, Munafò M. Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease. Trends Neurosci 2014, 37: 733–741.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Preston GA, Weinberger DR. Intermediate phenotypes in schizophrenia: A selective review. Dialogues Clin Neurosci 2005, 7: 165–179.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Cohen MX, Young J, Baek JM, Kessler C, Ranganath C. Individual differences in extraversion and dopamine genetics predict neural reward responses. Brain Res Cogn Brain Res 2005, 25: 851–861.

    Article  CAS  PubMed  Google Scholar 

  68. Vogeley K, Schneider-Axmann T, Pfeiffer U, Tepest R, Bayer TA, Bogerts B, et al. Disturbed gyrification of the prefrontal region in male schizophrenic patients: A morphometric postmortem study. Am J Psychiatry 2000, 157: 34–39.

    Article  CAS  PubMed  Google Scholar 

  69. James AC, James S, Smith DM, Javaloyes A. Cerebellar, prefrontal cortex, and thalamic volumes over two time points in adolescent-onset schizophrenia. Am J Psychiatry 2004, 161: 1023–1029.

    Article  PubMed  Google Scholar 

  70. Jou RJ, Hardan AY, Keshavan MS. Reduced cortical folding in individuals at high risk for schizophrenia: A pilot study. Schizophr Res 2005, 75: 309–313.

    Article  PubMed  Google Scholar 

  71. van Erp TGM, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry 2016, 21: 547–553.

    Article  PubMed  Google Scholar 

  72. van Erp TGM, Walton E, Hibar DP, Schmaal L, Jiang W, Glahn DC, et al. Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing neuro imaging genetics through meta analysis (ENIGMA) consortium. Biol Psychiatry 2018, 84: 644–654.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Brugger SP, Howes OD. Heterogeneity and homogeneity of regional brain structure in schizophrenia: A meta-analysis. JAMA Psychiatry 2017, 74: 1104–1111.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Zalesky A, Fornito A, Seal ML, Cocchi L, Westin CF, Bullmore ET, et al. Disrupted axonal fiber connectivity in schizophrenia. Biol Psychiatry 2011, 69: 80–89.

    Article  PubMed  Google Scholar 

  75. Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, et al. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: Results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry 2018, 23: 1261–1269.

    Article  CAS  PubMed  Google Scholar 

  76. Balevich EC, Haznedar MM, Wang E, Newmark RE, Bloom R, Schneiderman JS, et al. Corpus callosum size and diffusion tensor anisotropy in adolescents and adults with schizophrenia. Psychiatry Res 2015, 231: 244–251.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Perlstein WM, Carter CS, Noll DC, Cohen JD. Relation of prefrontal cortex dysfunction to working memory and symptoms in schizophrenia. Am J Psychiatry 2001, 158: 1105–1113.

    Article  CAS  PubMed  Google Scholar 

  78. Anticevic A, Repovs G, Krystal JH, Barch DM. A broken filter: Prefrontal functional connectivity abnormalities in schizophrenia during working memory interference. Schizophr Res 2012, 141: 8–14.

    Article  PubMed  Google Scholar 

  79. Hua M, Peng Y, Zhou Y, Qin W, Yu C, Liang M. Disrupted pathways from limbic areas to thalamus in schizophrenia highlighted by whole-brain resting-state effective connectivity analysis. Prog Neuropsychopharmacol Biol Psychiatry 2020, 99: 109837.

    Article  PubMed  Google Scholar 

  80. Martino M, Magioncalda P, Yu H, Li X, Wang Q, Meng Y, et al. Abnormal resting-state connectivity in a substantia nigra-related striato-thalamo-cortical network in a large sample of first-episode drug-Naïve patients with schizophrenia. Schizophr Bull 2018, 44: 419–431.

    Article  PubMed  Google Scholar 

  81. Li A, Zalesky A, Yue W, Howes O, Yan H, Liu Y, et al. A neuroimaging biomarker for striatal dysfunction in schizophrenia. Nat Med 2020, 26: 558–565.

    Article  CAS  PubMed  Google Scholar 

  82. Liang S, Li T. Functional striatal abnormalities: A distinct brain signature of schizophrenia. Neurosci Bull 2021, 37: 284–286.

    Article  PubMed  Google Scholar 

  83. Fornito A, Harrison BJ, Goodby E, Dean A, Ooi C, Nathan PJ, et al. Functional dysconnectivity of corticostriatal circuitry as a risk phenotype for psychosis. JAMA Psychiatry 2013, 70: 1143–1151.

    Article  PubMed  Google Scholar 

  84. Whitfield-Gabrieli S, Ford JM. Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol 2012, 8: 49–76.

    Article  PubMed  Google Scholar 

  85. 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: 73–84.

    Article  CAS  PubMed  Google Scholar 

  86. Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD. Aberrant “default mode” functional connectivity in schizophrenia. Am J Psychiatry 2007, 164: 450–457.

    Article  PubMed  Google Scholar 

  87. Dauvermann MR, Mothersill D, Rokita KI, King S, Holleran L, Ruan K, et al. Changes in default-mode network associated with childhood trauma in schizophrenia. Schizophr Bull 2021, 47: 1482–1494.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Sendi MSE, Zendehrouh E, Ellis CA, Liang Z, Fu Z, Mathalon DH, et al. Aberrant dynamic functional connectivity of default mode network in schizophrenia and links to symptom severity. Front Neural Circuits 2021, 15: 649417.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Jamea AA, Alblowi M, Alghamdi J, Alosaimi FD, Albadr F, Abualait T, et al. Altered default mode network activity and cortical thickness as vulnerability indicators for SCZ: A preliminary resting state MRI study. Eur Rev Med Pharmacol Sci 2021, 25: 669–677.

    CAS  PubMed  Google Scholar 

  90. Liu H, Kaneko Y, Ouyang X, Li L, Hao Y, Chen EY, et al. Schizophrenic patients and their unaffected siblings share increased resting-state connectivity in the task-negative network but not its anticorrelated task-positive network. Schizophr Bull 2012, 38: 285–294.

    Article  PubMed  Google Scholar 

  91. Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A 2009, 106: 1279–1284.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Sasabayashi D, Takahashi T, Takayanagi Y, Nemoto K, Ueno M, Furuichi A, et al. Resting state hyperconnectivity of the default mode network in schizophrenia and clinical high-risk state for psychosis. Cereb Cortex 2023, 33: 8456–8464.

    Article  PubMed  Google Scholar 

  93. Zhou Y, Fan L, Qiu C, Jiang T. Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia. Neurosci Bull 2015, 31: 207–219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Li H, Tang J, Chen L, Liao Y, Zhou B, He Y, et al. Reduced middle cingulate gyrus volume in late-onset schizophrenia in a Chinese Han population: A voxel-based structural MRI study. Neurosci Bull 2015, 31: 626–627.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Marino M, Spironelli C, Mantini D, Craven AR, Ersland L, Angrilli A, et al. Default mode network alterations underlie auditory verbal hallucinations in schizophrenia. J Psychiatr Res 2022, 155: 24–32.

    Article  PubMed  Google Scholar 

  96. Diederen KMJ, Neggers SFW, de Weijer AD, van Lutterveld R, Daalman K, Eickhoff SB, et al. Aberrant resting-state connectivity in non-psychotic individuals with auditory hallucinations. Psychol Med 2013, 43: 1685–1696.

    Article  CAS  PubMed  Google Scholar 

  97. Kahn RS. On the origins of schizophrenia. Am J Psychiatry 2020, 177: 291–297.

    Article  PubMed  Google Scholar 

  98. Friston KJ, Frith CD. Schizophrenia: A disconnection syndrome? Clin Neurosci 1995, 3: 89–97.

    CAS  PubMed  Google Scholar 

  99. van den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RCW, Cahn W, et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 2013, 70: 783–792.

    Article  PubMed  Google Scholar 

  100. Bassett DS, Bullmore E, Verchinski BA, Mattay VS, Weinberger DR, Meyer-Lindenberg A. Hierarchical organization of human cortical networks in health and schizophrenia. J Neurosci 2008, 28: 9239–9248.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, et al. Functional connectivity and brain networks in schizophrenia. J Neurosci 2010, 30: 9477–9487.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Cocchi L, Harding IH, Lord A, Pantelis C, Yucel M, Zalesky A. Disruption of structure-function coupling in the schizophrenia connectome. Neuroimage Clin 2014, 4: 779–787.

    Article  PubMed  PubMed Central  Google Scholar 

  103. van den Heuvel MP, Mandl RCW, Stam CJ, Kahn RS, Hulshoff Pol HE. Aberrant frontal and temporal complex network structure in schizophrenia: A graph theoretical analysis. J Neurosci 2010, 30: 15915–15926.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Cui LB, Wei Y, Xi YB, Griffa A, De Lange SC, Kahn RS, et al. Connectome-based patterns of first-episode medication-Naïve patients with schizophrenia. Schizophr Bull 2019, 45: 1291–1299.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Cui LB, Liu K, Li C, Wang LX, Guo F, Tian P, et al. Putamen-related regional and network functional deficits in first-episode schizophrenia with auditory verbal hallucinations. Schizophr Res 2016, 173: 13–22.

    Article  PubMed  Google Scholar 

  106. Li B, Cui LB, Xi YB, Friston KJ, Guo F, Wang HN, et al. Abnormal effective connectivity in the brain is involved in auditory verbal hallucinations in schizophrenia. Neurosci Bull 2017, 33: 281–291.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Damaraju E, Allen EA, Belger A, Ford JM, McEwen S, Mathalon DH, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clin 2014, 5: 298–308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Woodward ND, Karbasforoushan H, Heckers S. Thalamocortical dysconnectivity in schizophrenia. Am J Psychiatry 2012, 169: 1092–1099.

    Article  PubMed  Google Scholar 

  109. Zabor EC, Kaizer AM, Hobbs BP. Randomized controlled trials. Chest 2020, 158: S79–S87.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Roiz-Santiañez R, Suarez-Pinilla P, Crespo-Facorro B. Brain structural effects of antipsychotic treatment in schizophrenia: A systematic review. Curr Neuropharmacol 2015, 13: 422–434.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Deakin B, Suckling J, Barnes TRE, Byrne K, Chaudhry IB, Dazzan P, et al. The benefit of minocycline on negative symptoms of schizophrenia in patients with recent-onset psychosis (BeneMin): A randomised, double-blind, placebo-controlled trial. Lancet Psychiatry 2018, 5: 885–894.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Voineskos AN, Mulsant BH, Dickie EW, Neufeld NH, Rothschild AJ, Whyte EM, et al. Effects of antipsychotic medication on brain structure in patients with major depressive disorder and psychotic features: Neuroimaging findings in the context of a randomized placebo-controlled clinical trial. JAMA Psychiatry 2020, 77: 674–683.

    Article  PubMed  Google Scholar 

  113. Woodward ML, Gicas KM, Warburton DE, White RF, Rauscher A, Leonova O, et al. Hippocampal volume and vasculature before and after exercise in treatment-resistant schizophrenia. Schizophr Res 2018, 202: 158–165.

    Article  CAS  PubMed  Google Scholar 

  114. Morimoto T, Matsuda Y, Matsuoka K, Yasuno F, Ikebuchi E, Kameda H, et al. Computer-assisted cognitive remediation therapy increases hippocampal volume in patients with schizophrenia: A randomized controlled trial. BMC Psychiatry 2018, 18: 83.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Yang M, He H, Duan M, Chen X, Chang X, Lai Y, et al. The effects of music intervention on functional connectivity strength of the brain in schizophrenia. Neural Plast 2018, 2018: 2821832.

    Article  PubMed  PubMed Central  Google Scholar 

  116. Consortium IS, Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 2009, 460: 748–752.

    Article  Google Scholar 

  117. Chen Q, Ursini G, Romer AL, Knodt AR, Mezeivtch K, Xiao E, et al. Schizophrenia polygenic risk score predicts mnemonic hippocampal activity. Brain 2018, 141: 1218–1228.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Neilson E, Bois C, Gibson J, Duff B, Watson A, Roberts N, et al. Effects of environmental risks and polygenic loading for schizophrenia on cortical thickness. Schizophr Res 2017, 184: 128–136.

    Article  PubMed  Google Scholar 

  119. Kauppi K, Westlye LT, Tesli M, Bettella F, Brandt CL, Mattingsdal M, et al. Polygenic risk for schizophrenia associated with working memory-related prefrontal brain activation in patients with schizophrenia and healthy controls. Schizophr Bull 2015, 41: 736–743.

    Article  PubMed  Google Scholar 

  120. Alnæs D, Kaufmann T, van der Meer D, Córdova-Palomera A, Rokicki J, Moberget T, et al. Brain heterogeneity in schizophrenia and its association with polygenic risk. JAMA Psychiatry 2019, 76: 739–748.

    Article  PubMed  PubMed Central  Google Scholar 

  121. Stauffer EM, Bethlehem RAI, Warrier V, Murray GK, Romero-Garcia R, Seidlitz J, et al. Grey and white matter microstructure is associated with polygenic risk for schizophrenia. Mol Psychiatry 2021, 26: 7709–7718.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Qi S, Sui J, Pearlson G, Bustillo J, Perrone-Bizzozero NI, Kochunov P, et al. Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network. Nat Commun 2022, 13: 4929.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Liu B, Zhang X, Cui Y, Qin W, Tao Y, Li J, et al. Polygenic risk for schizophrenia influences cortical gyrification in 2 independent general populations. Schizophr Bull 2017, 43: 673–680.

    PubMed  Google Scholar 

  124. Liu S, Li A, Liu Y, Li J, Wang M, Sun Y, et al. MIR137 polygenic risk is associated with schizophrenia and affects functional connectivity of the dorsolateral prefrontal cortex. Psychol Med 2020, 50: 1510–1518.

    Article  PubMed  Google Scholar 

  125. Liu S, Li A, Liu Y, Yan H, Wang M, Sun Y, et al. Polygenic effects of schizophrenia on hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity. Br J Psychiatry 2020, 216: 267–274.

    Article  PubMed  Google Scholar 

  126. Cao H, Zhou H, Cannon TD. Functional connectome-wide associations of schizophrenia polygenic risk. Mol Psychiatry 2021, 26: 2553–2561.

    Article  PubMed  Google Scholar 

  127. Franke B, Stein JL, Ripke S, Anttila V, Hibar DP, van Hulzen KJE, et al. Genetic influences on schizophrenia and subcortical brain volumes: Large-scale proof of concept. Nat Neurosci 2016, 19: 420–431.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Caseras X, Tansey KE, Foley S, Linden D. Association between genetic risk scoring for schizophrenia and bipolar disorder with regional subcortical volumes. Transl Psychiatry 2015, 5: e692.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Grama S, Willcocks I, Hubert JJ, Pardiñas AF, Legge SE, Bracher-Smith M, et al. Polygenic risk for schizophrenia and subcortical brain anatomy in the UK Biobank cohort. Transl Psychiatry 2020, 10: 309.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. van der Merwe C, Passchier R, Mufford M, Ramesar R, Dalvie S, Stein DJ. Polygenic risk for schizophrenia and associated brain structural changes: A systematic review. Compr Psychiatry 2019, 88: 77–82.

    Article  PubMed  Google Scholar 

  131. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013, 37: 658–665.

    Article  PubMed  PubMed Central  Google Scholar 

  132. Davey Smith G, Hemani G. Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 2014, 23: R89–R98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Price AL, Weale ME, Patterson N, Myers SR, Need AC, Shianna KV, et al. Long-range LD can confound genome scans in admixed populations. Am J Hum Genet 2008, 83: 132–135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet 2018, 27: R195–R208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Burgess S, Daniel RM, Butterworth AS, Thompson SG, Consortium EI. Network Mendelian randomization: Using genetic variants as instrumental variables to investigate mediation in causal pathways. Int J Epidemiol 2015, 44: 484–495.

    Article  Google Scholar 

  136. Wendt FR, Pathak GA, Lencz T, Krystal JH, Gelernter J, Polimanti R. Multivariate genome-wide analysis of education, socioeconomic status and brain phenome. Nat Hum Behav 2021, 5: 482–496.

    Article  PubMed  Google Scholar 

  137. Luby J, Belden A, Botteron K, Marrus N, Harms MP, Babb C, et al. The effects of poverty on childhood brain development: The mediating effect of caregiving and stressful life events. JAMA Pediatr 2013, 167: 1135–1142.

    Article  PubMed  PubMed Central  Google Scholar 

  138. MacKey AP, Finn AS, Leonard JA, Jacoby-Senghor DS, West MR, Gabrieli CFO, et al. Neuroanatomical correlates of the income-achievement gap. Psychol Sci 2015, 26: 925–933.

    Article  PubMed  Google Scholar 

  139. Noble KG, Houston SM, Brito NH, Bartsch H, Kan E, Kuperman JM, et al. Family income, parental education and brain structure in children and adolescents. Nat Neurosci 2015, 18: 773–778.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Walters RK, Polimanti R, Johnson EC, McClintick JN, Adams MJ, Adkins AE, et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat Neurosci 2018, 21: 1656–1669.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Hjorthøj C, Østergaard MLD, Benros ME, Toftdahl NG, Erlangsen A, Andersen JT, et al. Association between alcohol and substance use disorders and all-cause and cause-specific mortality in schizophrenia, bipolar disorder, and unipolar depression: A nationwide, prospective, register-based study. Lancet Psychiatry 2015, 2: 801–808.

    Article  PubMed  Google Scholar 

  142. Li L, Yu H, Liu Y, Meng YJ, Li XJ, Zhang C, et al. Lower regional grey matter in alcohol use disorders: Evidence from a voxel-based meta-analysis. BMC Psychiatry 2021, 21: 247.

    Article  PubMed  PubMed Central  Google Scholar 

  143. Jones SA, Nagel BJ. Altered frontostriatal white matter microstructure is associated with familial alcoholism and future binge drinking in adolescence. Neuropsychopharmacology 2019, 44: 1076–1083.

    Article  PubMed  PubMed Central  Google Scholar 

  144. Zahr NM, Pitel AL, Chanraud S, Sullivan EV. Contributions of studies on alcohol use disorders to understanding cerebellar function. Neuropsychol Rev 2010, 20: 280–289.

    Article  PubMed  PubMed Central  Google Scholar 

  145. Gurillo P, Jauhar S, Murray RM, MacCabe JH. Does tobacco use cause psychosis? Systematic review meta-analysis. Lancet Psychiatr 2015, 2: 718–725.

    Article  Google Scholar 

  146. Schneider CE, White T, Hass J, Geisler D, Wallace SR, Roessner V, et al. Smoking status as a potential confounder in the study of brain structure in schizophrenia. J Psychiatr Res 2014, 50: 84–91.

    Article  PubMed  Google Scholar 

  147. Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: Subsample and 2-sample instrumental variable estimators. Am J Epidemiol 2013, 178: 1177–1184.

    Article  PubMed  PubMed Central  Google Scholar 

  148. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ 2018, 362: k601.

    Article  PubMed  PubMed Central  Google Scholar 

  149. Guo J, Yu K, Dong SS, Yao S, Rong Y, Wu H, et al. Mendelian randomization analyses support causal relationships between brain imaging-derived phenotypes and risk of psychiatric disorders. Nat Neurosci 2022, 25: 1519–1527.

    Article  CAS  PubMed  Google Scholar 

  150. Williams JA, Burgess S, Suckling J, Lalousis PA, Batool F, Griffiths SL, et al. Inflammation and brain structure in schizophrenia and other neuropsychiatric disorders: A Mendelian randomization study. JAMA Psychiatry 2022, 79: 498–507.

    Article  PubMed  PubMed Central  Google Scholar 

  151. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 2015, 47: 1236–1241.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. 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 

  153. Yassin W, Nakatani H, Zhu Y, Kojima M, Owada K, Kuwabara H, et al. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Transl Psychiatry 2020, 10: 278.

    Article  PubMed  PubMed Central  Google Scholar 

  154. Guo Y, Qiu J, Lu W. Support vector machine-based schizophrenia classification using morphological information from amygdaloid and hippocampal subregions. Brain Sci 2020, 10: 562.

    Article  PubMed  PubMed Central  Google Scholar 

  155. Koutsouleris N, Riecher-Rössler A, Meisenzahl EM, Smieskova R, Studerus E, Kambeitz-Ilankovic L, et al. Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophr Bull 2015, 41: 471–482.

    Article  PubMed  Google Scholar 

  156. Zang J, Huang Y, Kong L, Lei B, Ke P, Li H, et al. Effects of brain atlases and machine learning methods on the discrimination of schizophrenia patients: A multimodal MRI study. Front Neurosci 2021, 15: 697168.

    Article  PubMed  PubMed Central  Google Scholar 

  157. Ardekani BA, Tabesh A, Sevy S, Robinson DG, Bilder RM, Szeszko PR. Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers. Hum Brain Mapp 2011, 32: 1–9.

    Article  PubMed  Google Scholar 

  158. Zhu Y, Nakatani H, Yassin W, Maikusa N, Okada N, Kunimatsu A, et al. Application of a machine learning algorithm for structural brain images in chronic schizophrenia to earlier clinical stages of psychosis and autism spectrum disorder: A multiprotocol imaging dataset study. Schizophr Bull 2022, 48: 563–574.

    Article  PubMed  PubMed Central  Google Scholar 

  159. Sun H, Lui S, Yao L, Deng W, Xiao Y, Zhang W, et al. Two patterns of white matter abnormalities in medication-naive patients with first-episode schizophrenia revealed by diffusion tensor imaging and cluster analysis. JAMA Psychiatry 2015, 72: 678–686.

    Article  PubMed  Google Scholar 

  160. 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 

  161. 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: 3–10.

    Article  PubMed  Google Scholar 

  162. Keshavan MS, Collin G, Guimond S, Kelly S, Prasad KM, Lizano P. Neuroimaging in schizophrenia. Neuroimaging Clin N Am 2020, 30: 73–83.

    Article  PubMed  Google Scholar 

  163. Shi D, Li Y, Zhang H, Yao X, Wang S, Wang G, et al. Machine learning of schizophrenia detection with structural and functional neuroimaging. Dis Markers 2021, 2021: 9963824.

    Article  PubMed  PubMed Central  Google Scholar 

  164. 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 

  165. Kraguljac NV, McDonald WM, Widge AS, Rodriguez CI, Tohen M, Nemeroff CB. Neuroimaging biomarkers in schizophrenia. Am J Psychiatry 2021, 178: 509–521.

    Article  PubMed  PubMed Central  Google Scholar 

  166. Goldsmith DR, Crooks CL, Walker EF, Cotes RO. An update on promising biomarkers in schizophrenia. Focus (Am Psychiatr Publ) 2018, 16: 153–163.

    PubMed  Google Scholar 

  167. Sarpal DK, Argyelan M, Robinson DG, Szeszko PR, Karlsgodt KH, John M, et al. Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. Am J Psychiatry 2016, 173: 69–77.

    Article  PubMed  Google Scholar 

  168. Blessing EM, Murty VP, Zeng B, Wang J, Davachi L, Goff DC. Anterior hippocampal-cortical functional connectivity distinguishes antipsychotic Naïve first-episode psychosis patients from controls and may predict response to second-generation antipsychotic treatment. Schizophr Bull 2020, 46: 680–689.

    Article  PubMed  Google Scholar 

  169. Svancer P, Spaniel F. Brain ventricular volume changes in schizophrenia. A narrative review. Neurosci Lett 2021, 759: 136065.

    Article  CAS  PubMed  Google Scholar 

  170. Ebdrup BH, Glenthøj B, Rasmussen H, Aggernaes B, Langkilde AR, Paulson OB, et al. Hippocampal and caudate volume reductions in antipsychotic-naive first-episode schizophrenia. J Psychiatry Neurosci 2010, 35: 95–104.

    Article  PubMed  PubMed Central  Google Scholar 

  171. Smucny J, Dienel SJ, Lewis DA, Carter CS. Mechanisms underlying dorsolateral prefrontal cortex contributions to cognitive dysfunction in schizophrenia. Neuropsychopharmacology 2022, 47: 292–308.

    Article  PubMed  Google Scholar 

  172. Jiang JB, Cao Y, An NY, Yang Q, Cui LB. Magnetic resonance imaging-based connectomics in first-episode schizophrenia: From preclinical study to clinical translation. Front Psychiatry 2020, 11: 565056.

    Article  PubMed  PubMed Central  Google Scholar 

  173. Thompson PM, Jahanshad N, Ching CRK, Salminen LE, Thomopoulos SI, Bright J, et al. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries. Transl Psychiatry 2020, 10: 100.

    Article  PubMed  PubMed Central  Google Scholar 

  174. Kochunov P, Thompson PM, Hong LE. Toward high reproducibility and accountable heterogeneity in schizophrenia research. JAMA Psychiatry 2019, 76: 680.

    Article  PubMed  PubMed Central  Google Scholar 

  175. Okada N, Fukunaga M, Yamashita F, Koshiyama D, Yamamori H, Ohi K, et al. Abnormal asymmetries in subcortical brain volume in schizophrenia. Mol Psychiatry 2016, 21: 1460–1466.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry 2021, 78: 195–209.

    Article  PubMed  Google Scholar 

  177. Bodnar M, Harvey PO, Malla AK, Joober R, Lepage M. The parahippocampal gyrus as a neural marker of early remission in first-episode psychosis: A voxel-based morphometry study. Clin Schizophr Relat Psychoses 2011, 4: 217–228.

    Article  PubMed  Google Scholar 

  178. Takayanagi Y, Sasabayashi D, Takahashi T, Furuichi A, Kido M, Nishikawa Y, et al. Reduced cortical thickness in schizophrenia and schizotypal disorder. Schizophr Bull 2020, 46: 387–394.

    PubMed  Google Scholar 

  179. Wible CG, Anderson J, Shenton ME, Kricun A, Hirayasu Y, Tanaka S, et al. Prefrontal cortex, negative symptoms, and schizophrenia: An MRI study. Psychiatry Res 2001, 108: 65–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Kyriakopoulos M, Dima D, Roiser JP, Corrigall R, Barker GJ, Frangou S. Abnormal functional activation and connectivity in the working memory network in early-onset schizophrenia. J Am Acad Child Adolesc Psychiatry 2012, 51: 911-920.e2.

    Article  PubMed  Google Scholar 

  181. Stoyanov D, Aryutova K, Kandilarova S, Paunova R, Arabadzhiev Z, Todeva-Radneva A, et al. Diagnostic task specific activations in functional MRI and aberrant connectivity of Insula with middle frontal gyrus can inform the differential diagnosis of psychosis. Diagnostics 2021, 11: 95.

    Article  PubMed  PubMed Central  Google Scholar 

  182. Nielsen MØ, Rostrup E, Wulff S, Glenthøj B, Ebdrup BH. Striatal reward activity and antipsychotic-associated weight change in patients with schizophrenia undergoing initial treatment. JAMA Psychiatry 2016, 73: 121–128.

    Article  PubMed  Google Scholar 

  183. Homan P, Argyelan M, Fales CL, Barber AD, DeRosse P, Szeszko PR, et al. Striatal volume and functional connectivity correlate with weight gain in early-phase psychosis. Neuropsychopharmacology 2019, 44: 1948–1954.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Harvey PD. Mood symptoms, cognition, and everyday functioning: In major depression, bipolar disorder, and schizophrenia. Innov Clin Neurosci 2011, 8: 14–18.

    PubMed  PubMed Central  Google Scholar 

  185. Duff BJ, MacRitchie KAN, Moorhead TWJ, Lawrie SM, Blackwood DHR. Human brain imaging studies of DISC1 in schizophrenia, bipolar disorder and depression: A systematic review. Schizophr Res 2013, 147: 1–13.

    Article  PubMed  Google Scholar 

  186. Kempton MJ, Salvador Z, Munafò MR, Geddes JR, Simmons A, Frangou S, et al. Structural neuroimaging studies in major depressive disorder. Meta-analysis and comparison with bipolar disorder. Arch Gen Psychiatry 2011, 68: 675–690.

  187. Schmaal L, Pozzi E, Ho TC, van Velzen LS, Veer IM, Opel N, et al. ENIGMA MDD: Seven years of global neuroimaging studies of major depression through worldwide data sharing. Transl Psychiatry 2020, 10: 172.

    Article  PubMed  PubMed Central  Google Scholar 

  188. Ho NF, Chong PLH, Lee DR, Chew QH, Chen G, Sim K. The amygdala in schizophrenia and bipolar disorder: A synthesis of structural MRI, diffusion tensor imaging, and resting-state functional connectivity findings. Harv Rev Psychiatry 2019, 27: 150–164.

    Article  PubMed  Google Scholar 

  189. Botvinik-Nezer R, Holzmeister F, Camerer CF, Dreber A, Huber J, Johannesson M, et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 2020, 582: 84–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia-an overview. JAMA. Psychiatry 2020, 77: 201–210.

    Google Scholar 

  191. Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet 2016, 388: 86–97.

    Article  PubMed  PubMed Central  Google Scholar 

  192. van den Heuvel MP, Scholtens LH, de Reus MA, Kahn RS. Associated microscale spine density and macroscale connectivity disruptions in schizophrenia. Biol Psychiatry 2016, 80: 293–301.

    Article  PubMed  Google Scholar 

  193. Price AJ, Jaffe AE, Weinberger DR. Cortical cellular diversity and development in schizophrenia. Mol Psychiatry 2021, 26: 203–217.

    Article  PubMed  Google Scholar 

  194. Wang M, Yan H, Tian X, Yue W, Liu Y, Fan L, et al. Neuroimaging and multiomics reveal cross-scale circuit abnormalities in schizophrenia. Nat Ment Health 2023, 1: 633–654.

    Article  Google Scholar 

  195. Whiting D, Gulati G, Geddes JR, Fazel S. Association of schizophrenia spectrum disorders and violence perpetration in adults and adolescents from 15 countries: A systematic review and meta-analysis. JAMA Psychiatry 2022, 79: 120–132.

    Article  PubMed  Google Scholar 

  196. Olfson M, Stroup TS, Huang C, Wall MM, Crystal S, Gerhard T. Suicide risk in medicare patients with schizophrenia across the life span. JAMA Psychiatry 2021, 78: 876–885.

    Article  PubMed  Google Scholar 

  197. Widmayer S, Borgwardt S, Lang UE, Stieglitz RD, Huber CG. Functional neuroimaging correlates of aggression in psychosis: A systematic review with recommendations for future research. Front Psychiatry 2018, 9: 777.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This review was supported by grants from the Science Fund for Distinguished Young Scholars of Shaanxi Province (2021JC-02), Innovation Capability Support Program of Shaanxi Province (2022TD-44), Key Research and Development Project of Shaanxi Province (2022GXLH-01-22), the National Natural Science Foundation of China (82101601), the China Postdoctoral Science Foundation (2023T160517, 2021M702612) and the Fundamental Research Funds for the Central Universities. We would like to thank the support of the High-Performance Computing Platform and Instrument Analysis Center of Xi’an Jiaotong University.

Author information

Authors and Affiliations

  1. Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

    Jing Guo, Changyi He, Huimiao Song, Huiwu Gao, Shan-Shan Dong & Tie-Lin Yang

  2. Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China

    Shi Yao

Authors
  1. Jing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changyi He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huimiao Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huiwu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shi Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shan-Shan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tie-Lin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shan-Shan Dong or Tie-Lin Yang.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflicts of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, J., He, C., Song, H. et al. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci. Bull. (2024). https://doi.org/10.1007/s12264-024-01214-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12264-024-01214-1

Keywords

Search

Navigation