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Neurodevelopmental model of schizophrenia revisited: similarity in individual deviation and idiosyncrasy from the normative model of whole-brain white matter tracts and shared brain-cognition covariation with ADHD and ASD

Abstract

The neurodevelopmental model of schizophrenia is supported by multi-level impairments shared among schizophrenia and neurodevelopmental disorders. Despite schizophrenia and typical neurodevelopmental disorders, i.e., autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), as disorders of brain dysconnectivity, no study has ever elucidated whether whole-brain white matter (WM) tracts integrity alterations overlap or diverge between these three disorders. Moreover, whether the linked dimensions of cognition and brain metrics per the Research Domain Criteria framework cut across diagnostic boundaries remains unknown. We aimed to map deviations from normative ranges of whole-brain major WM tracts for individual patients to investigate the similarity and differences among schizophrenia (281 patients subgrouped into the first-episode, subchronic and chronic phases), ASD (175 patients), and ADHD (279 patients). Sex-specific WM tract normative development was modeled from diffusion spectrum imaging of 626 typically developing controls (5–40 years). There were three significant findings. First, the patterns of deviation and idiosyncrasy of WM tracts were similar between schizophrenia and ADHD alongside ASD, particularly at the earlier stages of schizophrenia relative to chronic stages. Second, using the WM deviation patterns as features, schizophrenia cannot be separated from neurodevelopmental disorders in the unsupervised machine learning algorithm. Lastly, the canonical correlation analysis showed schizophrenia, ADHD, and ASD shared linked cognitive dimensions driven by WM deviations. Together, our results provide new insights into the neurodevelopmental facet of schizophrenia and its brain basis. Individual’s WM deviations may contribute to diverse arrays of cognitive function along a continuum with phenotypic expressions from typical neurodevelopmental disorders to schizophrenia.

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Fig. 1: GFA Z-score profiles of patients with different stages of schizophrenia vs. neurodevelopmental disorders (the age-matching sample).
Fig. 2: Similarity and Dissimilarity between different stages of schizophrenia and neurodevelopmental disorders (the age-matching sample).
Fig. 3: Distribution of participants based on their Z-GFA deviation patterns.
Fig. 4: Canonical correlation analysis (CCA) between generalized fractional anisotropy (GFA) Z-scores of neural tracts and intellectual function using the Wechsler Scale-III.

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References

  1. Kahn RS, Sommer IE, Murray RM, Meyer-Lindenberg A, Weinberger DR, Cannon TD, et al. Schizophrenia. Nat Rev Dis Prim. 2015;1:15067.

    Article  PubMed  Google Scholar 

  2. Birnbaum R, Weinberger DR. Genetic insights into the neurodevelopmental origins of schizophrenia. Nat Rev Neurosci. 2017;18:727–40.

    Article  CAS  PubMed  Google Scholar 

  3. Pantelis C, Yücel M, Wood SJ, Velakoulis D, Sun D, Berger G, et al. Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. Schizophrenia Bull. 2005;31:672–96.

    Article  Google Scholar 

  4. Kochunov P, Hong LE. Neurodevelopmental and neurodegenerative models of schizophrenia: white matter at the center stage. Schizophr Bull. 2014;40:721–8.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Oliver LD, Moxon-Emre I, Lai MC, Grennan L, Voineskos AN, Ameis SH. Social Cognitive Performance in Schizophrenia Spectrum Disorders Compared With Autism Spectrum Disorder: A Systematic Review, Meta-analysis, and Meta-regression. JAMA Psychiatry. 2021;78:281–92.

  6. Arican I, Bass N, Neelam K, Wolfe K, McQuillin A, Giaroli G. Prevalence of attention deficit hyperactivity disorder symptoms in patients with schizophrenia. Acta Psychiatr Scand. 2019;139:89–96.

    Article  CAS  PubMed  Google Scholar 

  7. Keshavan MS, Diwadkar VA, Montrose DM, Rajarethinam R, Sweeney JA. Premorbid indicators and risk for schizophrenia: a selective review and update. Schizophrenia Res. 2005;79:45–57.

    Article  Google Scholar 

  8. Faraone SV, Asherson P, Banaschewski T, Biederman J, Buitelaar JK, Ramos-Quiroga JA, et al. Attention-deficit/hyperactivity disorder. Nat Rev Dis Prim. 2015;1:15020.

    Article  PubMed  Google Scholar 

  9. Lord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T, et al. Autism spectrum disorder. Nat Rev Dis Prim. 2020;6:5.

    Article  PubMed  Google Scholar 

  10. Brainstorm C, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360:6395.

    Google Scholar 

  11. Gudmundsson OO, Walters GB, Ingason A, Johansson S, Zayats T, Athanasiu L, et al. Attention-deficit hyperactivity disorder shares copy number variant risk with schizophrenia and autism spectrum disorder. Transl Psychiatry. 2019;9:258.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Spronk M, Keane BP, Ito T, Kulkarni K, Ji JL, Anticevic A et al. A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction. Cereb Cortex. 2021;31:547–61.

  13. Waldman ID, Poore HE, Luningham JM, Yang J. Testing structural models of psychopathology at the genomic level. World Psychiatry. 2020;19:350–9.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Opel N, Goltermann J, Hermesdorf M, Berger K, Baune BT, Dannlowski U. Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium. Biol Psychiatry. 2020;88:678–86.

    Article  PubMed  Google Scholar 

  15. 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 

  16. 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.

  17. Tung YH, Lin HY, Chen CL, Shang CY, Yang LY, Hsu YC et al. Whole-brain white matter tracts deviation and idiosyncrasy from normative development in autism, ADHD and their unaffected siblings link with dimensions of psychopathology and cognition. Am J Psychiatry. 2021;178:730–43.

  18. Honey CJ, Kotter R, Breakspear M, Sporns O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci USA. 2007;104:10240–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Alexander-Bloch A, Giedd JN, Bullmore E. Imaging structural co-variance between human brain regions. Nat Rev Neurosci. 2013;14:322–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kochunov P, Hong LE, Dennis EL, Morey RA, Tate DF, Wilde EA et al. ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research. Hum Brain Mapp. 2022;43:194–206.

  21. Salthouse TA. Aging and measures of processing speed. Biol Psychol. 2000;54:35–54.

    Article  CAS  PubMed  Google Scholar 

  22. Bartzokis G, Lu PH, Tingus K, Mendez MF, Richard A, Peters DG, et al. Lifespan trajectory of myelin integrity and maximum motor speed. Neurobiol Aging. 2010;31:1554–62.

    Article  CAS  PubMed  Google Scholar 

  23. Kochunov P, Coyle T, Lancaster J, Robin DA, Hardies J, Kochunov V, et al. Processing speed is correlated with cerebral health markers in the frontal lobes as quantified by neuroimaging. Neuroimage. 2010;49:1190–9.

    Article  CAS  PubMed  Google Scholar 

  24. Penke L, Muñoz Maniega S, Murray C, Gow AJ, Hernández MC, Clayden JD, et al. A general factor of brain white matter integrity predicts information processing speed in healthy older people. J Neurosci. 2010;30:7569–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wright SN, Hong LE, Winkler AM, Chiappelli J, Nugent K, Muellerklein F, et al. Perfusion shift from white to gray matter may account for processing speed deficits in schizophrenia. Hum Brain Mapp. 2015;36:3793–804.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Pérez-Iglesias R, Tordesillas-Gutiérrez D, McGuire PK, Barker GJ, Roiz-Santiañez R, Mata I, et al. White matter integrity and cognitive impairment in first-episode psychosis. Am J Psychiatry. 2010;167:451–8.

    Article  PubMed  Google Scholar 

  27. Glahn DC, Kent JW Jr., Sprooten E, Diego VP, Winkler AM, Curran JE, et al. Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging. Proc Natl Acad Sci USA. 2013;110:19006–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Karbasforoushan H, Duffy B, Blackford JU, Woodward ND. Processing speed impairment in schizophrenia is mediated by white matter integrity. Psychol Med. 2015;45:109–20.

    Article  CAS  PubMed  Google Scholar 

  29. Turken A, Whitfield-Gabrieli S, Bammer R, Baldo JV, Dronkers NF, Gabrieli JD. Cognitive processing speed and the structure of white matter pathways: convergent evidence from normal variation and lesion studies. Neuroimage. 2008;42:1032–44.

    Article  PubMed  Google Scholar 

  30. Alloza C, Cox SR, Duff B, Semple SI, Bastin ME, Whalley HC, et al. Information processing speed mediates the relationship between white matter and general intelligence in schizophrenia. Psychiatry Res Neuroimaging. 2016;254:26–33.

    Article  PubMed  Google Scholar 

  31. Hong SB, Zalesky A, Fornito A, Park S, Yang YH, Park MH, et al. Connectomic disturbances in attention-deficit/hyperactivity disorder: a whole-brain tractography analysis. Biol Psychiatry. 2014;76:656–63.

    Article  PubMed  Google Scholar 

  32. Fuelscher I, Hyde C, Anderson V, Silk TJ. White matter tract signatures of fiber density and morphology in ADHD. Cortex. 2021;138:329–40.

    Article  PubMed  Google Scholar 

  33. Barnea-Goraly N, Lotspeich LJ, Reiss AL. Similar white matter aberrations in children with autism and their unaffected siblings: a diffusion tensor imaging study using tract-based spatial statistics. Arch Gen Psychiatry. 2010;67:1052–60.

    Article  PubMed  Google Scholar 

  34. Jou RJ, Reed HE, Kaiser MD, Voos AC, Volkmar FR, Pelphrey KA. White Matter Abnormalities in Autism and Unaffected Siblings. J Neuropsychiatry Clin Neurosci. 2016;28:49–55.

    Article  PubMed  Google Scholar 

  35. Di X, Azeez A, Li X, Haque E, Biswal BB. Disrupted focal white matter integrity in autism spectrum disorder: A voxel-based meta-analysis of diffusion tensor imaging studies. Prog Neuro-Psychopharmacol Biol Psychiatry. 2018;82:242–8.

    Article  Google Scholar 

  36. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. Am J Psychiatry. 2010;167:748–51.

    Article  PubMed  Google Scholar 

  37. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. 4th ed edn. Washington DC:American Psychiatric Association;1994.

  38. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. 5th ed edn. Arlinton, VA:American Psychiatric Association;2013.

  39. Gau SSF, Chou MC, Lee JC, Wong CC, Chou WJ, Chen MF, et al. Behavioral problems and parenting style among Taiwanese children with autism and their siblings. Psychiatry Clin Neurosci. 2010;64:70–8.

    Article  PubMed  Google Scholar 

  40. Gau SS, Chong MY, Chen TH, Cheng AT. A 3-year panel study of mental disorders among adolescents in Taiwan. Am J Psychiatry. 2005;162:1344–50.

    Article  PubMed  Google Scholar 

  41. Chen YL, Shen LJ, Gau SS. The Mandarin version of the Kiddie-Schedule for Affective Disorders and Schizophrenia-Epidemiological version for DSM-5 - A psychometric study. J Formos Med Assoc. 2017;116:671–8. Epub 2017 Jul 1011

    Article  PubMed  Google Scholar 

  42. Lin YJ, Yang LK, Gau SS. Psychiatric comorbidities of adults with early- and late-onset attention-deficit/hyperactivity disorder. Aust N. Z J Psychiatry. 2016;50:548–56. Epub 0004867415602015 Oct 0004867415609412.

    Article  PubMed  Google Scholar 

  43. Lin HY, Cocchi L, Zalesky A, Lv J, Perry A, Tseng WI, et al. Brain-behavior patterns define a dimensional biotype in medication-naive adults with attention-deficit hyperactivity disorder. Psychol Med. 2018;48:2399–408. Epub 0033291718002018 Feb 0033291718000027.

    Article  PubMed  Google Scholar 

  44. Newton R, Rouleau A, Nylander AG, Loze JY, Resemann HK, Steeves S, et al. Diverse definitions of the early course of schizophrenia-a targeted literature review. NPJ Schizophr. 2018;4:21.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Wechsler D. Wechsler Adult Intelligence Scale - Third Edition (WAIS-III). San Antonio, TX:Psychological Corporation;1997.

  46. Wechsler D. Wechsler Intelligence Scale for Children - Third Edition (WISC-III). San Antonio, TX:Psychological Corporation;1991.

  47. Lin YJ, Gau SS. Developmental changes of neuropsychological functioning in individuals with and without childhood ADHD from early adolescence to young adulthood: a 7-year follow-up study. Psychol Med. 2019;49:940–51. Epub 0033291718002018 Jun 0033291718001526.

    Article  PubMed  Google Scholar 

  48. Luciana M. Practitioner Review: Computerized assessment of neuropsychological function in children: clinical and research applications of the Cambridge Neuropsychological Testing Automated Battery (CANTAB). J Child Psychol Psychiatry. 2003;44:649–63.

    Article  PubMed  Google Scholar 

  49. Seng GJ, Tseng WL, Chiu YN, Tsai WC, Wu YY, Gau SS. Executive functions in youths with autism spectrum disorder and their unaffected siblings. Psychol Med. 2020;30:1–10.

    Google Scholar 

  50. Reese TG, Heid O, Weisskoff RM, Wedeen VJ. Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo. Magn Reson Med. 2002;49:177–82.

    Article  Google Scholar 

  51. Hsu Y-C, Hsu C-H, Tseng, YI W-. A large deformation diffeomorphic metric mapping solution for diffusion spectrum imaging datasets. NeuroImage. 2012;63:818–34.

    Article  PubMed  Google Scholar 

  52. Yeh F-C, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng W-YI. Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy. PLOS ONE. 2013;8:e80713.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Avram AV, Sarlls JE, Barnett AS, Ozarslan E, Thomas C, Irfanoglu MO, et al. Clinical feasibility of using mean apparent propagator (MAP) MRI to characterize brain tissue microstructure. Neuroimage. 2016;127:422–34.

    Article  PubMed  Google Scholar 

  54. Snedecor GW, Cochran WG. Statistical Methods. 8th edn. Ames, Iowa:Iowa State University Press;1989.

  55. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivar Behav Res. 2011;46:399–424.

    Article  Google Scholar 

  56. Tibshirani R, Walther G, Hastie T. Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc: Ser B (Stat Methodol). 2001;63:411–23.

    Article  Google Scholar 

  57. Smith SM, Nichols TE, Vidaurre D, Winkler AM, Behrens TEJ, Glasser MF, et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci. 2015;18:1565–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Alexander AL, Hurley SA, Samsonov AA, Adluru N, Hosseinbor AP, Mossahebi P, et al. Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connectivity. 2011;1:423–46.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Rommelse N, Buitelaar JK, Hartman CA. Structural brain imaging correlates of ASD and ADHD across the lifespan: a hypothesis-generating review on developmental ASD-ADHD subtypes. J Neural Transm (Vienna). 2017;124:259–71.

    Article  PubMed  Google Scholar 

  60. Thapar A, Riglin L. The importance of a developmental perspective in Psychiatry: what do recent genetic-epidemiological findings show? Mol Psychiatry. 2020;25:1631–9.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Gogtay N, Vyas NS, Testa R, Wood SJ, Pantelis C. Age of onset of schizophrenia: perspectives from structural neuroimaging studies. Schizophr Bull. 2011;37:504–13.

    Article  PubMed  PubMed Central  Google Scholar 

  62. McGorry PD, Nelson B, Goldstone S, Yung AR. Clinical staging: a heuristic and practical strategy for new research and better health and social outcomes for psychotic and related mood disorders. Can J Psychiatry. 2010;55:486–97.

    Article  PubMed  Google Scholar 

  63. Di Biase MA, Cropley VL, Baune BT, Olver J, Amminger GP, Phassouliotis C, et al. White matter connectivity disruptions in early and chronic schizophrenia. Psychol Med. 2017;47:2797–810.

    Article  PubMed  Google Scholar 

  64. Owen JP, Marco EJ, Desai S, Fourie E, Harris J, Hill SS, et al. Abnormal white matter microstructure in children with sensory processing disorders. Neuroimage Clin. 2013;2:844–53.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Kallankari H, Saunavaara V, Parkkola R, Haataja L, Hallman M, Kaukola T. Diffusion tensor imaging in frontostriatal tracts is associated with executive functioning in very preterm children at 9 years of age. Pediatr Radiol. 2021;51:112–8

  66. Shen KK, Welton T, Lyon M, McCorkindale AN, Sutherland GT, Burnham S, et al. Structural core of the executive control network: A high angular resolution diffusion MRI study. Hum Brain Mapp. 2020;41:1226–36.

    Article  PubMed  Google Scholar 

  67. Humpston CS, Broome MR. Thinking, believing, and hallucinating self in schizophrenia. Lancet Psychiatry. 2020;7:638–46.

    Article  PubMed  Google Scholar 

  68. Heinz A, Murray GK, Schlagenhauf F, Sterzer P, Grace AA, Waltz JA. Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia. Schizophr Bull. 2019;45:1092–1100.

    Article  PubMed  Google Scholar 

  69. Chini M, Hanganu-Opatz IL. Prefrontal Cortex Development in Health and Disease: Lessons from Rodents and Humans. Trends Neurosci. 2021;44:227–40.

  70. Griffa A, Baumann PS, Klauser P, Mullier E, Cleusix M, Jenni R, et al. Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging. Transl Psychiatry. 2019;9:62.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Morris-Rosendahl DJ, Crocq MA. Neurodevelopmental disorders-the history and future of a diagnostic concept. Dialogues Clin Neurosci. 2020;22:65–72.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The study was approved by the Research Ethics Committee of National Taiwan University Hospital, Taipei, Taiwan (Approval numbers: 200903062 R, 201003087 R, and 201201006RIB, corresponding to ClinicalTrials.gov number, NCT00916851, NCT01247610, and NCT01582256, respectively). Written informed consent and child assent were obtained from the participants or their parents. This work was supported by grants from the Ministry of Science and Technology, Taiwan (NSC96–3112-B-002–033, NSC97–3112-B-002–009, NSC98–3112-B-002–004, NSC99–2627-B-002–015, NSC100–2627-B-002–014, NSC99–2321-B-002–037, NSC100–2321-B-002–015, NSC101–2627-B-002–002, NSC 101–2314-B-002–136-MY3, NSC101–2321-B-002–079), the National Health Research Institute, Taiwan (NHRI-EX97–9407PC, NHRI-EX98–9407PC, NHRI-EX100–10008PI, NHRI-EX101–10008PI, NHRI-EX102–10008PI, NHRI-EX103–10008PI, NHRI-EX104–10404PI, NHRI-EX105–10404PI, NHRI-EX106–10404PI), National Taiwan University Hospital (NTUH101-S1910) and Chen-Yung Foundation to SSG for the data collection on ADHD, ASD, and TDC. For data collection of schizophrenia, WYT, HGH, and CML obtained grants from the Ministry of Science and Technology (MOST106–2314-B-002–242-MY3, NSC 100–2321-B-002–016 & NSC 99–2321-B-002–038, MOST 104–2314-B-002–068), respectively. HYL receives stipend support from the Azrieli Foundation through the Azrieli Adult Neurodevelopmental Centre at CAMH. The authors are grateful to all the participants and their parents for their participation and research assistants for data collection.

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SSG initiated and designed the study. YLC, HYL, YHT, and SSG formulated overarching research goals and aims and interpreted the data. SSG collected ASD, ADHD (CYS, too), and control samples. CML, HGH, and TJH collected schizophrenia and control samples. YHT, HYL, CLC, and WYT performed neuroimage analysis. YHT, HYL, CSW, and YLC performed statistical analyses. YLC and YHT drafted the first paper, rigorously edited and reviewed by HYL and SSG. All the authors approved the final version to be published and agreed to be accountable for the integrity and accuracy of all aspects of the work.

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Correspondence to Wen-Yih Isaac Tseng, Chih-Min Liu or Susan Shur-Fen Gau.

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Chien, YL., Lin, HY., Tung, YH. et al. Neurodevelopmental model of schizophrenia revisited: similarity in individual deviation and idiosyncrasy from the normative model of whole-brain white matter tracts and shared brain-cognition covariation with ADHD and ASD. Mol Psychiatry 27, 3262–3271 (2022). https://doi.org/10.1038/s41380-022-01636-1

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