Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis

Molecular Psychiatry volume 28, pages 5309–5318 (2023)Cite this article

Subjects

Abstract

Although gray matter (GM) abnormalities are present from the early stages of psychosis, subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis (TA), which permits quantification of the complex interrelationship between contrasts at the individual voxel level, may capture subtle GM changes with more sensitivity than does volume or cortical thickness (CTh). We performed three-dimensional TA in nine GM regions of interest (ROIs) using T1 magnetic resonance images from 101 patients with first-episode psychosis (FEP), 85 patients at clinical high risk (CHR) for psychosis, and 147 controls. Via principal component analysis, three features of gray-level cooccurrence matrix – informational measure of correlation 1 (IMC1), autocorrelation (AC), and inverse difference (ID) – were selected to analyze cortical texture in the ROIs that showed a significant change in volume or CTh in the study groups. Significant reductions in GM volume and CTh of various frontotemporal regions were found in the FEP compared with the controls. Increased frontal AC was found in the FEP group compared to the controls after adjusting for volume and CTh changes. While volume and CTh were preserved in the CHR group, a stagewise nonlinear increase in frontal IMC1 was found, which exceeded both the controls and FEP group. Increased frontal IMC1 was also associated with a lesser severity of attenuated positive symptoms in the CHR group, while neither volume nor CTh was. The results of the current study suggest that frontal IMC1 may reflect subtle, dynamic GM changes and the symptomatology of the CHR stage with greater sensitivity, even in the absence of gross GM abnormalities. Some structural mechanisms that may contribute to texture changes (e.g., macrostructural cortical lamina, neuropil/myelination, cortical reorganization) and their possible implications are explored and discussed. Texture may be a useful tool to investigate subtle and dynamic GM abnormalities, especially during the CHR period.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Association of attenuated positive symptom severity in the clinical high-risk group with texture/volumetric features.
Fig. 2: Schematic illustration of the study findings.

Similar content being viewed by others

References

  1. Andreasen NC, Nopoulos P, Magnotta V, Pierson R, Ziebell S, Ho B-C. Progressive brain change in schizophrenia: a prospective longitudinal study of first-episode schizophrenia. Biol Psychiatry. 2011;70:672–9.

    PubMed  PubMed Central  Google Scholar 

  2. Vita A, De Peri L, Deste G, Sacchetti E. Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies. Transl Psychiatry. 2012;2:e190.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Van Haren NE, Schnack HG, Cahn W, Van Den Heuvel MP, Lepage C, Collins L, et al. Changes in cortical thickness during the course of illness in schizophrenia. Arch Gen Psychiatry. 2011;68:871–80.

    PubMed  Google Scholar 

  4. Zipursky RB, Lambe EK, Kapur S, Mikulis DJ. Cerebral gray matter volume deficits in first episode psychosis. Arch Gen Psychiatry. 1998;55:540–6.

    CAS  PubMed  Google Scholar 

  5. Job DE, Whalley HC, McConnell S, Glabus M, Johnstone EC, Lawrie SM. Structural gray matter differences between first-episode schizophrenics and normal controls using voxel-based morphometry. Neuroimage. 2002;17:880–9.

    PubMed  Google Scholar 

  6. Hulshoff Pol HE, Kahn RS. What happens after the first episode? A review of progressive brain changes in chronically ill patients with schizophrenia. Schizophr Bull. 2008;34:354–66.

    PubMed  PubMed Central  Google Scholar 

  7. Fusar-Poli P, Borgwardt S, Crescini A, Deste G, Kempton MJ, Lawrie S, et al. Neuroanatomy of vulnerability to psychosis: a voxel-based meta-analysis. Neurosci Biobehav Rev. 2011;35:1175–85.

    CAS  PubMed  Google Scholar 

  8. Mechelli A, Riecher-Rössler A, Meisenzahl EM, Tognin S, Wood SJ, Borgwardt SJ, et al. Neuroanatomical abnormalities that predate the onset of psychosis: a multicenter study. Arch Gen Psychiatry. 2011;68:489–95.

    PubMed  Google Scholar 

  9. Iwashiro N, Suga M, Takano Y, Inoue H, Natsubori T, Satomura Y, et al. Localized gray matter volume reductions in the pars triangularis of the inferior frontal gyrus in individuals at clinical high-risk for psychosis and first episode for schizophrenia. Schizophr Res. 2012;137:124–31.

    PubMed  Google Scholar 

  10. Ding Y, Ou Y, Pan P, Shan X, Chen J, Liu F, et al. Brain structural abnormalities as potential markers for detecting individuals with ultra-high risk for psychosis: a systematic review and meta-analysis. Schizophr Res. 2019;209:22–31.

    PubMed  Google Scholar 

  11. Zikidi K, Gajwani R, Gross J, Gumley AI, Lawrie SM, Schwannauer M, et al. Grey-matter abnormalities in clinical high-risk participants for psychosis. Schizophr Res. 2020;226:120–8.

    PubMed  PubMed Central  Google Scholar 

  12. Cropley VL, Lin A, Nelson B, Reniers RL, Yung AR, Bartholomeusz CF, et al. Baseline grey matter volume of non-transitioned “ultra high risk” for psychosis individuals with and without attenuated psychotic symptoms at long-term follow-up. Schizophr Res. 2016;173:152–8.

    PubMed  Google Scholar 

  13. Chung Y, Jacobson A, He G, van Erp TG, McEwen S, Addington J, et al. Prodromal symptom severity predicts accelerated gray matter reduction and third ventricle expansion among clinically high-risk youth developing psychotic disorders. Complex Psychiatry. 2015;1:13–22.

    Google Scholar 

  14. Walter A, Studerus E, Smieskova R, Kuster P, Aston J, Lang UE, et al. Hippocampal volume in subjects at high risk of psychosis: a longitudinal MRI study. Schizophr Res. 2012;142:217–22.

    PubMed  Google Scholar 

  15. Lee S, Lee H, Kim KW. Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume. J Psychiatry Neurosci. 2020;45:7–14.

    PubMed  Google Scholar 

  16. Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Müller H. Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal. 2014;18:176–96.

    PubMed  Google Scholar 

  17. Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2:1636–42.

    PubMed  Google Scholar 

  18. Fan Y, Feng M, Wang R. Application of radiomics in central nervous system diseases: a systematic literature review. Clin Neurol Neurosurg. 2019;187:105565.

    PubMed  Google Scholar 

  19. Zhang Y, Zhu H, Mitchell JR, Costello F, Metz LM. T2 MRI texture analysis is a sensitive measure of tissue injury and recovery resulting from acute inflammatory lesions in multiple sclerosis. Neuroimage. 2009;47:107–11.

    CAS  PubMed  Google Scholar 

  20. Holli KK, Wäljas M, Harrison L, Liimatainen S, Luukkaala T, Ryymin P, et al. Mild traumatic brain injury: tissue texture analysis correlated to neuropsychological and DTI findings. Acad Radiol. 2010;17:1096–102.

    PubMed  Google Scholar 

  21. Ishaque A, Mah D, Seres P, Luk C, Johnston W, Chenji S, et al. Corticospinal tract degeneration in ALS unmasked in T1‐weighted images using texture analysis. Hum Brain Mapp. 2019;40:1174–83.

    PubMed  Google Scholar 

  22. Sørensen L, Igel C, Liv Hansen N, Osler M, Lauritzen M, Rostrup E, et al. Early detection of Alzheimer’s disease using M RI hippocampal texture. Hum Brain Mapp. 2016;37:1148–61.

    PubMed  Google Scholar 

  23. Kassner A, Thornhill R. Texture analysis: a review of neurologic MR imaging applications. Am J Neuroradiol. 2010;31:809–16.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Ganeshan B, Miles KA, Young RC, Chatwin CR, Gurling HM, Critchley HD. Three-dimensional textural analysis of brain images reveals distributed grey-matter abnormalities in schizophrenia. Eur Radiol. 2010;20:941–8.

    PubMed  Google Scholar 

  25. Radulescu E, Ganeshan B, Shergill SS, Medford N, Chatwin C, Young RC, et al. Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia. Psychiatry Res Neuroimaging. 2014;223:179–86.

    Google Scholar 

  26. Latha M, Kavitha G. Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. Magn Reson Mater Phys Biol Med. 2018;31:483–99.

    CAS  Google Scholar 

  27. Korda A, Ruef A, Neufang S, Davatzikos C, Borgwardt S, Meisenzahl E, et al. Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions. Psychiatry Res Neuroimaging. 2021;313:111303.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Korda AI, Andreou C, Rogg HV, Avram M, Ruef A, Davatzikos C, et al. Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence. Transl Psychiatry. 2022;12:481.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Park YW, Choi D, Lee J, Ahn SS, Lee S-K, Lee S-H, et al. Differentiating patients with schizophrenia from healthy controls by hippocampal subfields using radiomics. Schizophr Res. 2020;223:337–44.

    PubMed  Google Scholar 

  30. Jalbrzikowski M, Hayes RA, Wood SJ, Nordholm D, Zhou JH, Fusar-Poli P, et al. Association of structural magnetic resonance imaging measures with psychosis onset in individuals at clinical high risk for developing psychosis: an ENIGMA working group mega-analysis: an ENIGMA working group mega-analysis. JAMA Psychiatry. 2021;78:753–66.

    PubMed  Google Scholar 

  31. Lee S, Kim KW. Initiative ftAsDN. Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer’s disease. Eur J Neurol. 2021;28:735–44.

    PubMed  Google Scholar 

  32. Lee TY, Hwang WJ, Kim NS, Park I, Lho SK, Moon S-Y, et al. Prediction of psychosis: model development and internal validation of a personalized risk calculator. Psychol Med. 2020;52:1–9.

  33. Yi J-S, Ahn Y-M, Shin H-K, An S-K, Joo Y-H, Kim S-H, et al. Reliability and validity of the Korean version of the Positive and Negative Syndrome Scale. J Korean Neuropsychiatr Assoc. 2001;40:1090–105.

  34. Jung MH, Jang JH, Kang D-H, Choi J-S, Shin NY, Kim HS, et al. The reliability and validity of the Korean version of the structured interview for prodromal syndrome. Psychiatry Investig. 2010;7:257.

    PubMed  PubMed Central  Google Scholar 

  35. Lobbestael J, Leurgans M, Arntz A. Inter‐rater reliability of the Structured Clinical Interview for DSM‐IV Axis I disorders (SCID I) and Axis II disorders (SCID II). Clin Psychol Psychother. 2011;18:75–9.

    PubMed  Google Scholar 

  36. Schwarz CG, Gunter JL, Wiste HJ, Przybelski SA, Weigand SD, Ward CP, et al. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer’s disease severity. NeuroImage Clin. 2016;11:802–12.

    PubMed  PubMed Central  Google Scholar 

  37. Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;6:610–21.

    Google Scholar 

  38. Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004;22:81–91.

    CAS  PubMed  Google Scholar 

  39. Mahmoud-Ghoneim D, Alkaabi MK, de Certaines JD, Goettsche F-M. The impact of image dynamic range on texture classification of brain white matter. BMC Med Imaging. 2008;8:1–8.

    Google Scholar 

  40. Ortiz A, Palacio AA, Górriz JM, Ramírez J, Salas-González D. Segmentation of brain MRI using SOM-FCM-based method and 3D statistical descriptors. Comput Math Methods Med. 2013;2013.

  41. Gardner DM, Murphy AL, O’Donnell H, Centorrino F, Baldessarini RJ. International consensus study of antipsychotic dosing. Am J Psychiatry. 2010;167:686–93.

    PubMed  Google Scholar 

  42. Dukart J, Smieskova R, Harrisberger F, Lenz C, Schmidt A, Walter A, et al. Age-related brain structural alterations as an intermediate phenotype of psychosis. J Psychiatry Neurosci JPN. 2017;42:307.

    PubMed  Google Scholar 

  43. Gupta CN, Calhoun VD, Rachakonda S, Chen J, Patel V, Liu J, et al. Patterns of gray matter abnormalities in schizophrenia based on an international mega-analysis. Schizophr Bull. 2015;41:1133–42.

    PubMed  Google Scholar 

  44. Wolkin A, Rusinek H, Vaid G, Arena L, Lafargue T, Sanfilipo M, et al. Structural magnetic resonance image averaging in schizophrenia. Am J Psychiatry. 1998;155:1064–73.

    CAS  PubMed  Google Scholar 

  45. Lawrie S, Abukmeil S, Chiswick A, Egan V, Santosh C, Best J. Qualitative cerebral morphology in schizophrenia: a magnetic resonance imaging study and systematic literature review. Schizophr Res. 1997;25:155–66.

    CAS  PubMed  Google Scholar 

  46. Rosa P, Zanetti M, Duran F, Santos L, Menezes P, Scazufca M, et al. What determines continuing grey matter changes in first-episode schizophrenia and affective psychosis? Psychol Med. 2015;45:817–28.

    CAS  PubMed  Google Scholar 

  47. van Haren NE, Cahn W, Pol HH, Kahn R. Schizophrenia as a progressive brain disease. Eur Psychiatry. 2008;23:245–54.

    PubMed  Google Scholar 

  48. Does MD. Inferring brain tissue composition and microstructure via MR relaxometry. NeuroImage. 2018;182:136–48.

    PubMed  Google Scholar 

  49. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci. 2000;97:11050–5.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Sprooten E, O’Halloran R, Dinse J, Lee WH, Moser DA, Doucet GE, et al. Depth-dependent intracortical myelin organization in the living human brain determined by in vivo ultra-high field magnetic resonance imaging. NeuroImage. 2019;185:27–34.

    CAS  PubMed  Google Scholar 

  51. Harrison PJ. The neuropathology of schizophrenia: a critical review of the data and their interpretation. Brain. 1999;122:593–624.

    PubMed  Google Scholar 

  52. Wagstyl K, Ronan L, Whitaker K, Goodyer I, Roberts N, Crow T, et al. Multiple markers of cortical morphology reveal evidence of supragranular thinning in schizophrenia. Transl Psychiatry. 2016;6:e780.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Williams M, Chaudhry R, Perera S, Pearce R, Hirsch S, Ansorge O, et al. Changes in cortical thickness in the frontal lobes in schizophrenia are a result of thinning of pyramidal cell layers. Eur Arch Psychiatry Clin Neurosci. 2013;263:25–39.

    CAS  PubMed  Google Scholar 

  54. Wiegand LC, Warfield SK, Levitt JJ, Hirayasu Y, Salisbury DF, Heckers S, et al. Prefrontal cortical thickness in first-episode psychosis: a magnetic resonance imaging study. Biol Psychiatry. 2004;55:131–40.

    PubMed  PubMed Central  Google Scholar 

  55. Bakhshi K, Chance S. The neuropathology of schizophrenia: a selective review of past studies and emerging themes in brain structure and cytoarchitecture. Neurosci. 2015;303:82–102.

    CAS  Google Scholar 

  56. Harrison PJ. Postmortem studies in schizophrenia. Dialog Clin Neurosci. 2022;2:349–57.

  57. Uranova NA, Vostrikov VM, Orlovskaya DD, Rachmanova VI. Oligodendroglial density in the prefrontal cortex in schizophrenia and mood disorders: a study from the Stanley Neuropathology Consortium. Schizophr Res. 2004;67:269–75.

    PubMed  Google Scholar 

  58. Kolomeets NS, Uranova NA. Reduced oligodendrocyte density in layer 5 of the prefrontal cortex in schizophrenia. Eur Arch Psychiatry Clin Neurosci. 2019;269:379–86.

    PubMed  Google Scholar 

  59. Foong J, Symms M, Barker G, Maier M, Woermann F, Miller D, et al. Neuropathological abnormalities in schizophrenia: evidence from magnetization transfer imaging. Brain. 2001;124:882–92.

    CAS  PubMed  Google Scholar 

  60. Price G, Cercignani M, Chu EM, Barnes TR, Barker GJ, Joyce EM, et al. Brain pathology in first-episode psychosis: magnetization transfer imaging provides additional information to MRI measurements of volume loss. Neuroimage. 2010;49:185–92.

    PubMed  Google Scholar 

  61. Maani R, Yang YH, Kalra S. Voxel-based texture analysis of the brain. PLoS One. 2015;10:e0117759.

    PubMed  PubMed Central  Google Scholar 

  62. Maani R, Yang Y-H, Emery D, Kalra S. Cerebral degeneration in amyotrophic lateral sclerosis revealed by 3-dimensional texture analysis. Front. Neurosci. 2016;10:120.

    PubMed  PubMed Central  Google Scholar 

  63. Tak K, Lee S, Choi E, Suh SW, Oh DJ, Moon W, et al. Magnetic resonance imaging texture of medial pulvinar in dementia with lewy bodies. Dement Geriatr Cogn Disord. 2020;49:8–15.

    PubMed  Google Scholar 

  64. Sui YV, Bertisch H, Lee H-H, Storey P, Babb JS, Goff DC, et al. Quantitative macromolecular proton fraction mapping reveals altered cortical myelin profile in schizophrenia spectrum disorders. Cereb Cortex Commun. 2021;2:tgab015.

  65. Rowley CD, Sehmbi M, Bazin PL, Tardif CL, Minuzzi L, Frey BN, et al. Age‐related mapping of intracortical myelin from late adolescence to middle adulthood using T1‐weighted MRI. Hum Brain Mapp. 2017;38:3691–703.

    PubMed  PubMed Central  Google Scholar 

  66. Edwards LJ, Kirilina E, Mohammadi S, Weiskopf N. Microstructural imaging of human neocortex in vivo. Neuroimage. 2018;182:184–206.

    PubMed  Google Scholar 

  67. Palaniyappan L. Progressive cortical reorganisation: a framework for investigating structural changes in schizophrenia. Neurosci Biobehav Rev. 2017;79:1–13.

    PubMed  Google Scholar 

  68. Abel S, Weiller C, Huber W, Willmes K, Specht K. Therapy-induced brain reorganization patterns in aphasia. Brain. 2015;138:1097–112.

    PubMed  Google Scholar 

  69. Kerr AL, Cheng S-Y, Jones TA. Experience-dependent neural plasticity in the adult damaged brain. J Commun Disord. 2011;44:538–48.

    PubMed  PubMed Central  Google Scholar 

  70. Westlye LT, Walhovd KB, Dale AM, Bjørnerud A, Due-Tønnessen P, Engvig A, et al. Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity. Neuroimage. 2010;52:172–85.

    PubMed  Google Scholar 

  71. Palaniyappan L, Das T, Dempster K. The neurobiology of transition to psychosis: clearing the cache. J Psychiatry Neurosci. 2017;42:294–9.

    PubMed  PubMed Central  Google Scholar 

  72. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:1–9.

    Google Scholar 

  73. Van Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–e107.

    PubMed  PubMed Central  Google Scholar 

  74. Moyer CE, Shelton MA, Sweet RA. Dendritic spine alterations in schizophrenia. Neurosci Lett. 2015;601:46–53.

    CAS  PubMed  Google Scholar 

Download references

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) and the KBRI basic research program through Korea Brain Research Institute, funded by the Ministry of Science & ICT (grant nos. 2019R1C1C1002457, 2020M3E5D9079910, and 21-BR-03-01).

Author information

Authors and Affiliations

  1. Department of Public Health Service, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

    Sun Young Moon

  2. Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea

    Sun Young Moon, Ki Woong Kim & Jun Soo Kwon

  3. Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea

    Hyungyou Park, Won Lee, Subin Lee, Ki Woong Kim & Jun Soo Kwon

  4. Research and Development Division, 40FY Inc., Seongnam, Republic of Korea

    Silvia Kyungjin Lho

  5. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea

    Minah Kim & Jun Soo Kwon

  6. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea

    Minah Kim, Ki Woong Kim & Jun Soo Kwon

  7. Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

    Ki Woong Kim

Authors
  1. Sun Young Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hyungyou Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Won Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Subin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Silvia Kyungjin Lho
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Minah Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ki Woong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jun Soo Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JSK, KWK, and SYM conceived the project. SYM designed the study methodology and wrote the first manuscript with the help of all other authors. HP and WL performed MRI and texture analysis with the help of SL, KWK, and SYM. MK and JSK provided the resources and supervised the project. Review and editing of the first manuscript were performed by MK, JSK, and SYM.

Corresponding author

Correspondence to Jun Soo Kwon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

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

Moon, S.Y., Park, H., Lee, W. et al. Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis. Mol Psychiatry 28, 5309–5318 (2023). https://doi.org/10.1038/s41380-023-02163-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-023-02163-3

Search

Advanced search

Quick links