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Magnetic resonance advanced imaging analysis in adolescents: cortical thickness study to identify attenuated psychosis syndrome

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

Psychosis is a symptom common to several mental illnesses and a defining feature of schizophrenia spectrum disorders, whose onset typically occurs in adolescence. Neuroradiological studies have reported evidence of brain structural abnormalities in patients with overt psychosis. However, early identification of brain structural changes in young subjects at risk for developing psychosis (such as those with Attenuated Psychosis Syndrome –APS) is currently lacking.

Methods

Brain 3D T1-weighted and 64 directions diffusion-weighted images were acquired on 55 help-seeking adolescents (12–17 years old) with psychiatric disorders who referred to our Institute. Patients were divided into three groups: non-APS (n = 20), APS (n = 20), and Early-Onset Psychosis (n = 15). Cortical thickness was calculated from T1w images, and Tract-Based Spatial Statistics analysis was performed to study the distribution of white matter fractional anisotropy and all diffusivity metrics. A thorough neuropsychological test battery was adopted to investigate cognitive performance in several domains.

Results

In patients with Attenuated Psychotic Syndrome, the left superior frontal gyrus was significantly thinner compared to patients with non-APS (p = 0.048), and their right medial orbitofrontal cortex thickness was associated with lower working memory scores (p = 0.0025, r = -0.668 for the working memory index and p = 0.001, r = -0.738 for the digit span). Early-Onset Psychosis patients showed thinner left pars triangularis compared to non-APS individuals (p = 0.024), and their left pars orbitalis was associated with impaired performance at the symbol search test (p = 0.005, r = -0.726). No differences in diffusivity along main tracts were found between sub-groups (p > 0.05).

Conclusion

This study showed specific associations between structural imaging features and cognitive performance in patients with APS. Characterizing this disorder using neuroimaging could reveal useful information that may aid in the development and evaluation of preventive strategies in these individuals.

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Data availability

The data that support the findings of this study are available from the corresponding author upon request (https://doi.org/10.5281/zenodo.6563681).

Abbreviations

AD:

Axial Diffusivity

APS:

Attenuated Psychosis Syndrome

BLIPS:

Brief Limited Intermittent Psychotic Symptoms

CAARMS:

Comprehensive Assessment of At-Risk Mental State

CC:

Core-Coding

CHR-P:

Clinical High-Risk state for Psychosis

CGI-S:

Clinical Global Impression-Severity

CT:

Cortical Thickness

DS:

Digit Span

DSM-5:

Diagnostic and Statistical Manual of mental disorders, Fifth edition

DTI:

Diffusion Tensor Imaging

DUP:

Duration of Untreated Psychosis

EOP:

Early-Onset Psychosis

FA:

Fractional Anisotropy

FMRIB:

Functional Magnetic Resonance Imaging of the Brain

FSL:

FMRIB Software Library

GRD:

Genetic Risk and Deterioration syndrome

HARDI:

High Angular Resolution Diffusion-weighted Imaging

IQ:

Intelligence Quotient

K-SADS-PL:

Kiddie Schedule for Affective Disorders and Schizophrenia – Present and Lifetime version

LN:

Letter-Number sequencing

MD:

Mean Diffusivity

MNI:

Montreal Neurological Institute

MRI:

Magnetic Resonance Imaging

non-APS:

Non-Attenuated Psychotic Syndrome

PS:

Processing Speed

RD:

Radial Diffusivity

SCID-5-PD:

Structured Clinical Interview for DSM-5 Personality Disorders

sd:

standard deviation

SE-EPI:

Spin-Echo EchoplanarImaging

SES:

Socio-Economical Status

SOFAS:

Social and Occupational Functioning Assessment Scale

SS:

Symbol Search

TBSS:

Tract-Based Spatial Statistics

TFCE:

Threshold Free Cluster Enhancement

WAIS:

Wechsler Adult Intelligence Scale

WISC:

Wechsler Intelligence Scale for Children

WM:

Working Memory

References

  1. Arciniegas DB (2015) Psychosis. Continuum (Minneap Minn) 21:715–736. https://doi.org/10.1212/01.con.0000466662.89908.e7

    Article  PubMed  Google Scholar 

  2. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edn. American Psychiatric Association, Washington, DC

    Book  Google Scholar 

  3. Radua J, Ramella-Cravaro V, Ioannidis JPA, Reichenberg A, Phiphopthatsanee N, Amir T et al (2018) What causes psychosis? An umbrella review of risk and protective factors. World Psychiatry 17:49–66. https://doi.org/10.1002/wps.20490

    Article  PubMed  PubMed Central  Google Scholar 

  4. Kahn RS, Sommer IE, Murray RM, Meyer-Lindenberg A, Weinberger DR, Cannon TD et al (2015) Schizophrenia. Nat Rev Dis Primers 1:15067. https://doi.org/10.1038/nrdp.2015.67

    Article  PubMed  Google Scholar 

  5. Salazar de Pablo G, Estradé A, Cutroni M, Andlauer O, Fusar-Poli P (2021) Establishing a clinical service to prevent psychosis: what, how and when? Systematic review. Transl Psychiatry 11:43. https://doi.org/10.1038/s41398-020-01165-x

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fusar-Poli P, Sullivan SA, Shah JL, Uhlhaas PJ (2019) Improving the detection of individuals at clinical risk for psychosis in the community, primary and secondary care: an integrated evidence-based approach. Front Psychiatry 10:774. https://doi.org/10.3389/fpsyt.2019.00774

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mensi MM, Molteni S, Iorio M, Filosi E, Ballante E, Balottin U et al (2021) Prognostic accuracy of DSM-5 attenuated psychosis syndrome in adolescents: prospective real-world 5-year cohort study. Schizophr Bull 47:1663–1673. https://doi.org/10.1093/schbul/sbab041

    Article  PubMed  PubMed Central  Google Scholar 

  8. Addington J, Farris M, Devoe D, Metzak P (2020) Progression from being at-risk to psychosis: next steps. NPJ Schizophr 6:27. https://doi.org/10.1038/s41537-020-00117-0

    Article  PubMed  PubMed Central  Google Scholar 

  9. Shakeel MK, MacQueen G, Addington J, Metzak PD, Georgopoulos G, Bray S et al (2020) White matter connectivity in youth at risk for serious mental illness: a longitudinal analysis. Psychiatry Res Neuroimaging 302:111106. https://doi.org/10.1016/j.pscychresns.2020.111106

    Article  PubMed  Google Scholar 

  10. Catalan A, Salazar de Pablo G, Vaquerizo Serrano J, Mosillo P, Baldwin H, Fernández-Rivas A et al (2021) Annual research review: prevention of psychosis in adolescents - systematic review and meta-analysis of advances in detection, prognosis and intervention. J Child Psychol Psychiatry 62:657–673. https://doi.org/10.1111/jcpp.13322

    Article  PubMed  Google Scholar 

  11. Jung WH, Kim JS, Jang JH, Choi J-S, Jung MH, Park J-Y et al (2011) Cortical thickness reduction in individuals at ultra-high-risk for psychosis. Schizophr Bull 37:839–849. https://doi.org/10.1093/schbul/sbp151

    Article  PubMed  Google Scholar 

  12. Buechler R, Wotruba D, Michels L, Theodoridou A, Metzler S, Walitza S et al (2020) Cortical volume differences in subjects at risk for psychosis are driven by surface area. Schizophr Bull 46:1511–1519. https://doi.org/10.1093/schbul/sbaa066

    Article  PubMed  PubMed Central  Google Scholar 

  13. Klauser P, Zhou J, Lim JKW, Poh JS, Zheng H, Tng HY, Krishnan R et al (2015) Lack of evidence for regional brain volume or cortical thickness abnormalities in youths at clinical high risk for psychosis: findings from the longitudinal youth at risk study. Schizophr Bull 41:1285–1293. https://doi.org/10.1093/schbul/sbv012

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ziermans TB, Durston S, Sprong M, Nederveen H, van Haren NEM, Schnack HG et al (2009) No evidence for structural brain changes in young adolescents at ultra high risk for psychosis. Schizophr Res 112:1–6. https://doi.org/10.1016/j.schres.2009.04.013

    Article  PubMed  Google Scholar 

  15. von Hohenberg CC, Pasternak O, Kubicki M, Ballinger T, Vu M-A, Swisher T, Green K et al (2014) White matter microstructure in individuals at clinical high risk of psychosis: a whole-brain diffusion tensor imaging study. Schizophr Bull 40:895–903. https://doi.org/10.1093/schbul/sbt079

    Article  Google Scholar 

  16. Mittal VA, Dean DJ, Bernard JA, Orr JM, Pelletier-Baldelli A, Carol EE et al (2014) Neurological soft signs predict abnormal cerebellar-thalamic tract development and negative symptoms in adolescents at high risk for psychosis: a longitudinal perspective. Schizophr Bull 40:1204–1215. https://doi.org/10.1093/schbul/sbt199

    Article  PubMed  Google Scholar 

  17. Fannon D, Chitnis X, Doku V, Tennakoon L, O’Ceallaigh S, Soni W et al (2000) Features of structural brain abnormality detected in first-episode psychosis. Am J Psychiatry 157:1829–1834. https://doi.org/10.1176/appi.ajp.157.11.1829

    Article  CAS  PubMed  Google Scholar 

  18. Saito J, Hori M, Nemoto T, Katagiri N, Shimoji K, Ito S et al (2017) Longitudinal study examining abnormal white matter integrity using a tract-specific analysis in individuals with a high risk for psychosis. Psychiatry Clin Neurosci 71:530–541. https://doi.org/10.1111/pcn.12515

    Article  CAS  PubMed  Google Scholar 

  19. Niznikiewicz MA (2019) Neurobiological approaches to the study of clinical and genetic high risk for developing psychosis. Psychiatry Res 277:17–22. https://doi.org/10.1016/j.psychres.2019.02.009

    Article  PubMed  Google Scholar 

  20. Zipursky RB, Reilly TJ, Murray RM (2013) The myth of schizophrenia as a progressive brain disease. Schizophr Bull 39:1363–1372. https://doi.org/10.1093/schbul/sbs135

    Article  PubMed  Google Scholar 

  21. Hartberg CB, Sundet K, Rimol LM, Haukvik UK, Lange EH, Nesvåg R et al (2011) Brain cortical thickness and surface area correlates of neurocognitive performance in patients with schizophrenia, bipolar disorder, and healthy adults. J Int Neuropsychol Soc 17:1080–1093. https://doi.org/10.1017/s1355617711001081

    Article  CAS  PubMed  Google Scholar 

  22. Molteni S, Filosi E, Mensi MM, Spada G, Zandrini C, Ferro F et al (2019) Predictors of outcomes in adolescents with clinical high risk for psychosis, other psychiatric symptoms, and psychosis: a longitudinal protocol study. Front Psychiatry 10:787. https://doi.org/10.3389/fpsyt.2019.00787

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kaufman J, Birmaher B, Axelson D, Perepletchikova F, Brent D, Ryan N (2016) Schedule for affective disorders and schizophrenia for school aged children (6-18 years): Kiddie-SADS - Lifetime version (K-SADS-PL DSM-5). In: Advanced Center for Intervention and Services Research (ACISR) for Early Onset Mood and Anxiety Disorders Western Psychiatric Institute and Clinic; Child and Adolescent Research and Education (CARE) Program. Yale University

  24. Kaufman J, Birmaher B, Rao U, Ryan N (2019) K-SADS-PL DSM-5. Intervista diagnostica per la valutazione dei disturbi psicopatologici in bambini e adolescenti. Ed. Centro Studi Erickson, Trento

  25. First MB, Williams JBW, Benjamin LS, Spitzer RL (2015) Structured clinical interview for DSM-5 personality disorders SCID-5-PD. American Psychiatric Association, Arlington

  26. First MB, Williams JBW, Smith Benjamin L, Spitzer RL (2017) SCID-5-PD: Intervista clinica strutturata per i disturbi di personalità del DSM-5. Raffaello Cortina Editore, Milano

  27. Fusar-Poli P, Hobson R, Raduelli M, Balottin U (2012) Reliability and validity of the Comprehensive Assessment of the At Risk Mental State, Italian version (CAARMS-I). Curr Pharm Des 18:386–391. https://doi.org/10.2174/138161212799316118

    Article  CAS  PubMed  Google Scholar 

  28. Yung AR, Yuen HP, McGorry PD, Phillips LJ, Kelly D, Dell’Olio M et al (2005) Mapping the onset of psychosis: the comprehensive assessment of at-risk mental states. Aust N Z J Psychiatry 39:964–971. https://doi.org/10.1080/j.1440-1614.2005.01714.x

    Article  PubMed  Google Scholar 

  29. Hollingshead AB (1975) Four factor index of social status. Yale Journal of Sociology, vol. 8, 2011, New Haven, pp 21–51. https://sociology.yale.edu/sites/default/files/files/yjs_fall_2011.pdf

  30. Bradley RH, Corwyn RF (2002) Socioeconomic status and child development. Annu Rev Psychol 53:371–399. https://doi.org/10.1146/annurev.psych.53.100901.135233

    Article  PubMed  Google Scholar 

  31. Guy W (1976) ECDEU assessment manual for psychopharmacology. U.S. Dept. of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse, and Mental Health Administration, National Institute of Mental Health, Psychopharmacology Research Branch, Division of Extramural Research Programs. Rockville, MD

  32. Wechsler D (2012) WISC-IV Wechsler Intelligence Scale for children IV ed. In: Nuovo modello teorico, nuovi subtest, nuovi punteggi, nuove norme: il perfezionamento dell’eccellenza. Giunti Psychometrics

  33. Wechsler D (1997) Wechsler adult intelligence scale - revised. Giunti Psychometrics

  34. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021

    Article  PubMed  Google Scholar 

  35. Fusar-Poli P, Radua J, McGuire P, Borgwardt S (2012) Neuroanatomical maps of psychosis onset: voxel-wise meta-analysis of antipsychotic-naÏve VBM studies. Schizophr Bull 38(6):1297–1307. https://doi.org/10.1093/schbul/sbr134

  36. Cannon TD, Chung Y, He G, Sun D, Jacobson A, van Erp TGM et al (2015) Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol Psychiatry 77:147–157. https://doi.org/10.1016/j.biopsych.2014.05.023

    Article  PubMed  Google Scholar 

  37. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155. https://doi.org/10.1002/hbm.10062

    Article  PubMed  PubMed Central  Google Scholar 

  38. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051

    Article  PubMed  Google Scholar 

  39. R core team (2021) R: A language and environment for statistical computing. R Found Stat Comput, Vienna, Austria. https://www.r-project.org. Accessed 10 Jun 2022

  40. Janssen J, Reig S, Alemán Y, Schnack H, Udias JM, Parellada M et al (2009) Gyral and sulcal cortical thinning in adolescents with first episode early-onset psychosis. Biol Psychiatry 66:1047–1054. https://doi.org/10.1016/j.biopsych.2009.07.021

    Article  PubMed  Google Scholar 

  41. Iwashiro N, Suga M, Takano Y, Inoue H, Natsubori T, Satomura Y et al (2012) 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 137:124–131. https://doi.org/10.1016/j.schres.2012.02.024

    Article  PubMed  Google Scholar 

  42. Zhu Y, Nakatani H, Yassin W, Maikusa N, Okada N, Kunimatsu A et al (2022) 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 48:563–574. https://doi.org/10.1093/schbul/sbac030

    Article  PubMed  PubMed Central  Google Scholar 

  43. Del Re EC, Stone WS, Bouix S, Seitz J, Zeng V, Guliano A et al (2021) Baseline cortical thickness reductions in clinical high risk for psychosis: brain regions associated with conversion to psychosis versus non-conversion as assessed at one-year follow-up in the Shanghai-At-Risk-for-Psychosis (SHARP) study. Schizophr Bull 47:562–574. https://doi.org/10.1093/schbul/sbaa127

    Article  PubMed  Google Scholar 

  44. Yasuda Y, Okada N, Nemoto K, Fukunaga M, Yamamori H, Ohi K et al (2020) Brain morphological and functional features in cognitive subgroups of schizophrenia. Psychiatry Clin Neurosci 74:191–203. https://doi.org/10.1111/pcn.12963

    Article  PubMed  Google Scholar 

  45. Ding Y, Ou Y, Pan P, Shan X, Chen J, Liu F et al (2019) Brain structural abnormalities as potential markers for detecting individuals with ultra-high risk for psychosis: a systematic review and meta-analysis. Schizophr Res 209:22–31. https://doi.org/10.1016/j.schres.2019.05.015

    Article  PubMed  Google Scholar 

  46. Dukart J, Smieskova R, Harrisberger F, Lenz C, Schmidt A, Walter A et al (2017) Age-related brain structural alterations as an intermediate phenotype of psychosis. J Psychiatry Neurosci 42:307–319. https://doi.org/10.1503/jpn.160179

    Article  PubMed  PubMed Central  Google Scholar 

  47. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H et al (2007) Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 27:2349–2356. https://doi.org/10.1523/jneurosci.5587-06.2007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Guo S, Palaniyappan L, Liddle PF, Feng J (2016) Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a MRI-derived cortical thickness study. Psychol Med 46:2201–2214. https://doi.org/10.1017/s0033291716000994

    Article  CAS  PubMed  Google Scholar 

  49. Palaniyappan L, Das T, Dempster K (2017) The neurobiology of transition to psychosis: clearing the cache. J Psychiatry Neurosci 42:294–299. https://doi.org/10.1503/jpn.170137

    Article  PubMed  PubMed Central  Google Scholar 

  50. Borgwardt SJ, McGuire PK, Aston J, Gschwandtner U, Pflüger MO, Stieglitz R-D et al (2008) Reductions in frontal, temporal and parietal volume associated with the onset of psychosis. Schizophr Res 106:108–114. https://doi.org/10.1016/j.schres.2008.08.007

    Article  PubMed  Google Scholar 

  51. Smigielski L, Stämpfli P, Wotruba D, Buechler R, Sommer S, Gerstenberg M et al (2022) White matter microstructure and the clinical risk for psychosis: a diffusion tensor imaging study of individuals with basic symptoms and at ultra-high risk. NeuroImage Clin 35:103067. https://doi.org/10.1016/j.nicl.2022.103067

    Article  PubMed  PubMed Central  Google Scholar 

  52. Kristensen TD, Glenthøj LB, Ragahava JM, Syeda W, Mandl RCW, Wenneberg C et al (2021) Changes in negative symptoms are linked to white matter changes in superior longitudinal fasciculus in individuals at ultra-high risk for psychosis. Schizophr Res 237:192–201. https://doi.org/10.1016/j.schres.2021.09.014

    Article  PubMed  Google Scholar 

  53. Hoptman MJ, Nierenberg J, Bertisch HC, Catalano D, Ardekani BA, Branch CA, Delisi LE (2008) A DTI study of white matter microstructure in individuals at high genetic risk for schizophrenia. Schizophr Res 106:115–124. https://doi.org/10.1016/j.schres.2008.07.023

    Article  PubMed  Google Scholar 

  54. DeLisi LE, Szulc KU, Bertisch H, Majcher M, Brown K, Bappal A et al (2006) Early detection of schizophrenia by diffusion weighted imaging. Psychiatry Res 148:61–66. https://doi.org/10.1016/j.pscychresns.2006.04.010

    Article  PubMed  PubMed Central  Google Scholar 

  55. Carletti F, Woolley JB, Bhattacharyya S, Perez-Iglesias R, Fusar Poli P, Valmaggia L et al (2012) Alterations in white matter evident before the onset of psychosis. Schizophr Bull 38:1170–1179. https://doi.org/10.1093/schbul/sbs053

    Article  PubMed  PubMed Central  Google Scholar 

  56. Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C et al (2018) Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry 23:1261–1269. https://doi.org/10.1038/mp.2017.170

    Article  CAS  PubMed  Google Scholar 

  57. Drakesmith M, Dutt A, Fonville L, Zammit S, Reichenberg A, Evans CJ et al (2016) Volumetric, relaxometric and diffusometric correlates of psychotic experiences in a non-clinical sample of young adults. NeuroImage Clin 12:550–558. https://doi.org/10.1016/j.nicl.2016.09.002

    Article  PubMed  PubMed Central  Google Scholar 

  58. Peters BD, Karlsgodt KH (2015) White matter development in the early stages of psychosis. Schizophr Res 161:61–69. https://doi.org/10.1016/j.schres.2014.05.021

    Article  PubMed  Google Scholar 

  59. Sato J, Vandewouw MM, Bando N, Branson HM, O’Connor DL, Unger SL, Taylor MJ (2021) White matter alterations and cognitive outcomes in children born very low birth weight. Neuroimage Clin 32:102843. https://doi.org/10.1016/j.nicl.2021.102843

    Article  PubMed  PubMed Central  Google Scholar 

  60. Darki F, Klingberg T (2015) The role of fronto-parietal and fronto-striatal networks in the development of working memory: a longitudinal study. Cereb Cortex 25:1587–1595. https://doi.org/10.1093/cercor/bht352

    Article  PubMed  Google Scholar 

  61. Borghesani PR, Madhyastha TM, Aylward EH, Reiter MA, Swarny BR, Schaie KW, Willis SL (2013) The association between higher order abilities, processing speed, and age are variably mediated by white matter integrity during typical aging. Neuropsychologia 51:1435–1444. https://doi.org/10.1016/j.neuropsychologia.2013.03.005

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank the patients and their families for their collaboration.

Funding

We thank the Italian Ministry of Health for the financial support by RC 2017–2019 and 2020–2021.

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Contributions

Conception and study design: Martina Maria Mensi, Umberto Balottin, Renato Borgatti and Anna Pichiecchio; data collection or acquisition: Laura Mazzocchi, Arianna Vecchio, Alexandra Paredes and Matteo Paoletti; statistical analysis: Laura Mazzocchi and Elena Ballante; interpretation of results: Luca Melazzini, Laura Mazzocchi, Arianna Vecchio, Alexandra Paredes, Martina Maria Mensi, Elena Ballante, Matteo Paoletti and Anna Pichiecchio; original draft preparation, review and editing: Luca Melazzini, Laura Mazzocchi, Arianna Vecchio, Alexandra Paredes, Matteo Paoletti, Renato Borgatti and Anna Pichiecchio. All authors read and approved the final version of the manuscript and agree to be accountable for the integrity and accuracy of all aspects of the work.

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Correspondence to Laura Mazzocchi.

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This study was approved by the Ethics Committee of the IRCCS Mondino Foundation and was conducted in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Luca Melazzini and Laura Mazzocchi are co-first authors.

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Melazzini, L., Mazzocchi, L., Vecchio, A. et al. Magnetic resonance advanced imaging analysis in adolescents: cortical thickness study to identify attenuated psychosis syndrome. Neuroradiology 65, 1447–1458 (2023). https://doi.org/10.1007/s00234-023-03200-2

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