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Individualized cortical thickness asymmetry in autism spectrum disorder and schizophrenia

Molecular Psychiatry (2025)Cite this article

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Abstract

Cortical thickness asymmetry has been proposed as a latent biomarker for autism spectrum disorder (ASD) and schizophrenia (SZ). However, the degree of abnormal asymmetry at the individual level in ASD and SZ remains unclear. To investigate this, we employed a normative modeling approach. Normative ranges for the whole brain and regional (160 cortical parcels) cortical thickness asymmetry index (AI) were established using a training set of healthy subjects (n = 4904, 45.15% male, age range: 6–95 years), controlling for age, sex, image quality, and scanner. We calculated z-scores to quantify individual deviations from the normative median in a test set consisting of healthy controls (HCtest, n = 526, 40% male), participants with ASD (n = 135, 83% male), and SZ (n = 287, 81% male). Regional deviance was assessed by counting the number of individuals with significant deviations below (infra-normal, z-score ≤ −1.96) or above (supra-normal, z-score ≥ 1.96) the normative median in each parcel. We also evaluated individual deviance by counting the number of regions with significant deviations for each participant. A multivariate approach was employed to determine whether regional deviance could separate the three groups. There were no differences for deviance of whole brain AI between any of the groups. Distributions of individual deviances overlapped across all 160 regions, with one superior temporal region in which SZ individuals showed a higher proportion of supra-normal AI values compared to HCtest (HCtest = 1.14%, SZ = 5.92%, χ2 = 15.45, PFDR < 0.05, ω = 0.14). The SZ group had a higher average number of regions with significant deviations than HCtest (infra-normal: z = 4.21, p < 0.01; supra-normal: z = 4.33, p < 0.01) but this group difference had limited predictive diagnostic accuracy at the individual level (Area Under the Curve≅60%). The multivariate analysis showed no association between regional deviance and diagnosis. Results were consistent when using a different parcellation, alternative asymmetry calculations, analysis restricted to males, and after controlling for handedness and IQ. Normative modelling revealed little to no evidence of atypical individualized cortical thickness asymmetry in ASD and SZ. The results of this study challenge the utility of cortical thickness asymmetry as a biomarker for ASD and SZ.

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Fig. 1: Characterizing regional-level deviance in the Asymmetry Index (AI).
Fig. 2: The distributions of the normative modeling based z-scores for the whole brain AI in HCtest and each condition.
Fig. 3: Distribution of the number of regions with infra- or supra-normal deviance per individual.
Fig. 4: The optimum and three cluster results of k-medoid clustering applied to the 2D embedding of z-scores for regional cortical thickness AI, generated using tSNE (A1, A2).

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Data availability

In addition to non-publicly available datasets, the following publicly available datasets were used: Aomic (id1000, piop1 and piop2) available at https://openneuro.org/datasets/ds003097, https://openneuro.org/datasets/ds002785 and https://openneuro.org/datasets/ds002790; camcan available at https://camcan-archive.mrc-cbu.cam.ac.uk; dlbs available at https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html; ixi available at http://brain-development.org/ixidataset; narratives available at https://openneuro.org/datasets/ds002345; oasis3 available at www.oasis-brains.org; rockland available at https://rocklandsample.org/; sald available at https://fcon_1000.projects.nitrc.org/indi/retro/sald.html; ABIDE-I and II available at https://fcon_1000.projects.nitrc.org/indi/abide/; MITASD available at https://openneuro.org/datasets/ds000212/versions/1.0.0; WASHASD available at https://openneuro.org/datasets/ds002522/versions/1.0.0; BGS available at http://schizconnect.org/; COBRE available at http://schizconnect.org/.

Code availability

All code used to perform the analyses can be found at https://github.com/iamjoostjanssen/NormModel_AI_SZ_ASD.

References

  1. Zhou D, Lebel C, Evans A, Beaulieu C. Cortical thickness asymmetry from childhood to older adulthood. Neuroimage. 2013;83:66–74.

    Article  PubMed  Google Scholar 

  2. Toga AW, Thompson PM. Mapping brain asymmetry. Nat Rev Neurosci. 2003;4:37–48.

    Article  PubMed  CAS  Google Scholar 

  3. Corballis MC. Left brain, right brain: facts and fantasies. PLoS Biol. 2014;12:e1001767.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Postema MC, van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, et al. Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets. Nat Commun. 2019;10:4958.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Sha Z, Schijven D, Francks C. Patterns of brain asymmetry associated with polygenic risks for autism and schizophrenia implicate language and executive functions but not brain masculinization. Mol Psychiatry. 2021;26:7652–60.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Floris DL, Lai MC, Auer T, Lombardo MV, Ecker C, Chakrabarti B, et al. Atypically rightward cerebral asymmetry in male adults with autism stratifies individuals with and without language delay. Hum Brain Mapp. 2016;37:230–53.

    Article  PubMed  Google Scholar 

  7. Li Q, Zhao W, Palaniyappan L, Guo S. Atypical hemispheric lateralization of brain function and structure in autism: a comprehensive meta-analysis study. Psychol Med. 2023;53:1–12.

  8. Lindell AK, Hudry K. Atypicalities in cortical structure, handedness, and functional lateralization for language in autism spectrum disorders. Neuropsychol Rev. 2013;23:257–70.

    Article  PubMed  Google Scholar 

  9. Brugger SP, Howes OD. Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry. 2017;74:1104–11.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kong XZ, Postema MC, Guadalupe T, de Kovel C, Boedhoe PSW, Hoogman M, et al. Mapping brain asymmetry in health and disease through the ENIGMA consortium. Hum Brain Mapp. 2022 Jan;43:167–81.

    Article  PubMed  Google Scholar 

  11. Schijven D, Postema MC, Fukunaga M, Matsumoto J, Miura K, de Zwarte SMC, et al. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium. Proc Natl Acad Sci USA. 2023;120:e2213880120.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Crow TJ. Schizophrenia as failure of hemispheric dominance for language. Trends Neurosci. 1997;20:339–43.

    PubMed  CAS  Google Scholar 

  13. Marquand AF, Kia SM, Zabihi M, Wolfers T, Buitelaar JK, Beckmann CF. Conceptualizing mental disorders as deviations from normative functioning. Mol Psychiatry. 2019;24:1415–24.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bethlehem RaI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, et al. Brain charts for the human lifespan. Nature. 2022;604:525–33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Segal A, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, et al. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders. Nat Neurosci. 2023;26:1613–29.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Bedford SA, Ortiz-Rosa A, Schabdach JM, Costantino M, Tullo S, Piercy T, et al. The impact of quality control on cortical morphometry comparisons in autism. Imaging Neurosci Camb Mass. 2023;1:1–21.

    Google Scholar 

  17. Chopra S, Segal A, Oldham S, Holmes A, Sabaroedin K, Orchard ER, et al. Network-based spreading of gray matter changes across different stages of psychosis. JAMA Psychiatry. 2023;80:1246–57.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Janssen J, Díaz-Caneja CM, Alloza C, Schippers A, de Hoyos L, Santonja J, et al. Dissimilarity in sulcal width patterns in the cortex can be used to identify patients with schizophrenia with extreme deficits in cognitive performance. Schizophr Bull. 2021;47:552–61.

    Article  PubMed  Google Scholar 

  19. Gaser C, Dahnke R, Thompson PM, Kurth F, Luders E. The Alzheimer’s Disease Neuroimaging Initiative null. CAT: a computational anatomy toolbox for the analysis of structural MRI data. Gigascience. 2024;13:giae049.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wolfers T, Doan NT, Kaufmann T, Alnæs D, Moberget T, Agartz I, et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry. 2018;75:1146–55.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Fischl B. FreeSurfer. Neuroimage. 2012;62:774–81.

    Article  PubMed  Google Scholar 

  22. Rosen AFG, Roalf DR, Ruparel K, Blake J, Seelaus K, Villa LP, et al. Quantitative assessment of structural image quality. Neuroimage. 2018;169:407–18.

    Article  PubMed  Google Scholar 

  23. Rutherford S, Barkema P, Tso IF, Sripada C, Beckmann CF, Ruhe HG, et al. Evidence for embracing normative modeling. eLife. 2023;12:e85082.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Romero-Garcia R, Atienza M, Cantero JL. Predictors of coupling between structural and functional cortical networks in normal aging. Hum Brain Mapp. 2014;35:2724–40.

    Article  PubMed  Google Scholar 

  25. Roe JM, Vidal-Pineiro D, Amlien IK, Pan M, Sneve MH, Thiebaut de Schotten M, et al. Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex. eLife. 2023;12:e84685.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Greve DN, Van der Haegen L, Cai Q, Stufflebeam S, Sabuncu MR, Fischl B, et al. A surface-based analysis of language lateralization and cortical asymmetry. J Cogn Neurosci. 2013;25:1477–92.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bahar N, Cler GJ, Krishnan S, Asaridou SS, Smith HJ, Willis HE, et al. Differences in cortical surface area in developmental language disorder. Neurobiol Lang Camb Mass. 2024;5:288–314.

    Article  Google Scholar 

  28. Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex N Y N 1991. 2018;28:3095–114.

    Google Scholar 

  29. Kruggel F, Solodkin A. Heritability of structural patterning in the human cerebral cortex. Neuroimage. 2020;221:117169.

    Article  PubMed  Google Scholar 

  30. Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:1–26.

    Article  Google Scholar 

  31. Fraza CJ, Dinga R, Beckmann CF, Marquand AF. Warped Bayesian linear regression for normative modelling of big data. Neuroimage. 2021;245:118715.

    Article  PubMed  Google Scholar 

  32. Bethlehem RAI, Seidlitz J, Romero-Garcia R, Trakoshis S, Dumas G, Lombardo MV. A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder. Commun Biol. 2020;3:486.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lv J, Di Biase M, Cash RFH, Cocchi L, Cropley VL, Klauser P, et al. Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort. Mol Psychiatry. 2021;26:3512–23.

    Article  PubMed  Google Scholar 

  34. ENIGMA Clinical High Risk for Psychosis Working Group, Haas SS, Ge R, Agartz I, Amminger GP, Andreassen OA, et al. Normative modeling of brain morphometry in clinical high risk for psychosis. JAMA Psychiatry. 2024;81:77–88.

    Article  Google Scholar 

  35. Huang AS, Kang K, Vandekar S, Rogers BP, Heckers S, Woodward ND Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling. Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol. 2024;49:1518–27.

  36. Hothorn T, Hornik K, van de Wiel MA, Zeileis A. Implementing a class of permutation tests: the coin package. J Stat Softw. 2008;28:1–23.

    Article  Google Scholar 

  37. Van Der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res. 2014;15:3221–45.

    Google Scholar 

  38. Hennig C, Liao TF. How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification. J R Stat Soc Ser C Appl Stat. 2013;62:309–69.

    Article  Google Scholar 

  39. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011;8:665–70.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 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;84:644–54.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zabihi M, Oldehinkel M, Wolfers T, Frouin V, Goyard D, Loth E, et al. Dissecting the heterogeneous cortical anatomy of autism spectrum disorder using normative models. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:567–78.

    PubMed  PubMed Central  Google Scholar 

  43. Floris DL, Wolfers T, Zabihi M, Holz NE, Zwiers MP, Charman T, et al. Atypical brain asymmetry in Autism-A candidate for clinically meaningful stratification. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6:802–12.

    PubMed  Google Scholar 

  44. Cullen AE, De Brito SA, Gregory SL, Murray RM, Williams SCR, Hodgins S, et al. Temporal lobe volume abnormalities precede the prodrome: a study of children presenting antecedents of schizophrenia. Schizophr Bull. 2013;39:1318–27.

    Article  PubMed  Google Scholar 

  45. Crow TJ. Temporal lobe asymmetries as the key to the etiology of schizophrenia. Schizophr Bull. 1990;16:433–43.

    Article  PubMed  CAS  Google Scholar 

  46. Winter NR, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K, et al. A systematic evaluation of machine learning-based biomarkers for major depressive disorder. JAMA Psychiatry. 2024;81:386–95.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Porter A, Fei S, Damme KSF, Nusslock R, Gratton C, Mittal VA. A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis. Mol Psychiatry. 2023;28:3278–92.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Palaniyappan L, Liddle PF. Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J Psychiatry Neurosci JPN. 2012;37:17–27.

    Article  PubMed  Google Scholar 

  49. Wheeler AL, Voineskos AN. A review of structural neuroimaging in schizophrenia: from connectivity to connectomics. Front Hum Neurosci. 2014;8:653.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hong SJ, Valk SL, Di Martino A, Milham MP, Bernhardt BC. Multidimensional neuroanatomical subtyping of autism spectrum disorder. Cereb Cortex N Y N 1991. 2018;28:3578–88.

    Google Scholar 

  51. Chen YC, Arnatkevičiūtė A, McTavish E, Pang JC, Chopra S, Suo C, et al. The individuality of shape asymmetries of the human cerebral cortex. eLife. 2022;11:e75056.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Chen YC, Tiego J, Segal A, Chopra S, Holmes A, Suo C, et al. A multiscale characterization of cortical shape asymmetries in early psychosis. Brain Commun. 2024;6:fcae015.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Supported by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (ISCIII), CIBER -Consorcio Centro de Investigación Biomédica en Red- (CB/07/09/0023), co-financed by the European Union, ERDF Funds from the European Commission, “A way of making Europe”, (PI16/02012, PI17/01249, PI17/00997, PI19/01024, PI20/00721, PI22/01824, PI22/01621, PI23/00625), financed by the European Union - NextGenerationEU (PMP21/00051), Madrid Regional Government (S2022/BMD-7216 AGES 3-CM), European Union Seventh Framework Program, European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking: Project PRISM-2 (Grant agreement No.101034377), Project COllaborative Network for European Clinical Trials For Children “c4c” (Grant agreement No 777389) Horizon Europe, the National Institute of Mental Health of the National Institutes of Health under Award Number 1U01MH124639-01 (Project ProNET), Award Number 5P50MH115846-03 (Project FEP-CAUSAL) and Award Number 1R01MH128971-01A1 (Project SZ-aging), Fundación Familia Alonso, and Fundación Alicia Koplowitz. The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. Any views expressed are those of the author(s) and not necessarily those of the funders (IHI-JU2). The authors thank Yasser Alemán-Goméz, Alberto Fernández Pena, Zimbo Boudewijns, and Joyce van Baaren for code and technical assistance.

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Author notes

  1. These authors contributed equally: Marta Martín Echave, Hugo G. Schnack.

Authors and Affiliations

  1. Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain

    Marta Martín Echave, Covadonga M. Díaz-Caneja, Laura Pina-Camacho, Celso Arango & Joost Janssen

  2. Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo G. Schnack, Elizabeth E. L. Buimer, Wiepke Cahn & Hilleke E. Hulshoff Pol

  3. Department of Languages, Literature, and Communication, Faculty of Humanities, Utrecht University, Utrecht, The Netherlands

    Hugo G. Schnack

  4. Ciber del Área de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain

    Covadonga M. Díaz-Caneja, Laura Pina-Camacho, Celso Arango & Joost Janssen

  5. School of Medicine, Universidad Complutense, Madrid, Spain

    Covadonga M. Díaz-Caneja, Laura Pina-Camacho & Celso Arango

  6. Department of Psychology, Universidad de la Laguna, Tenerife, Spain

    Niels Janssen

  7. Institute of Biomedical Technologies, Universidad de La Laguna, Tenerife, Spain

    Niels Janssen

  8. Institute of Neurosciences, Universidad de la Laguna, Santa Cruz de Tenerife, Spain

    Niels Janssen

  9. CIBM Center for Biomedical Imaging, Lausanne, Switzerland

    Pedro M. Gordaliza

  10. Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland

    Pedro M. Gordaliza

  11. Department of Neurosurgery, Medisch Spectrum Twente, Enschede, The Netherlands

    Kuan H. Kho

  12. Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede, the Netherlands

    Kuan H. Kho

  13. Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Centre, Sophia Children’s Hospital, Rotterdam, The Netherlands

    Neeltje E. M. van Haren

  14. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    René S. Kahn

  15. Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands

    Hilleke E. Hulshoff Pol

Authors
  1. Marta Martín Echave
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Contributions

Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work: Joost Janssen, Marta Martín Echave, Hugo G. Schnack, Covadonga M. Díaz-Caneja. Drafting the work or revising it critically for important intellectual content: Joost Janssen, Marta Martín Echave, Hugo G. Schnack, Pedro M. Gordaliza, Neeltje E.M. van Haren, Celso Arango. Final approval of the version to be published: Joost Janssen, Marta Martín Echave, Covadonga M. Díaz-Caneja, Niels Janssen, Pedro M. Gordaliza, Elizabeth E.L. Buimer, Neeltje E.M. van Haren, Wiepke Cahn, Celso Arango, René S. Kahn, Hilleke E. Hulshoff Pol, Hugo G. Schnack. Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: Joost Janssen, Marta Martín Echave, Hugo G. Schnack.

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Correspondence to Joost Janssen.

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No authors declare any competing financial interests in relation to the work described. Dr. Díaz-Caneja has received honoraria from Angelini and Viatris. Dr. Arango has been a consultant to or has received honoraria or grants from Acadia, Angelini, Gedeon Richter, Janssen-Cilag, Lundbeck, Otsuka, Roche, Sage, Servier, Shire, Schering-Plough, Sumitomo Dainippon Pharma, Sunovion, and Takeda. Dr. Cahn has received unrestricted research grants from or served as an independent symposium speaker or consultant for Eli Lilly, Bristol-Myers Squibb, Lundbeck, Sanofi-Aventis, Janssen-Cilag, AstraZeneca, and Schering-Plough. The other authors report no financial relationships with commercial interests.

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Martín Echave, M., Schnack, H.G., Díaz-Caneja, C.M. et al. Individualized cortical thickness asymmetry in autism spectrum disorder and schizophrenia. Mol Psychiatry (2025). https://doi.org/10.1038/s41380-025-03359-5

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