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Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling

Neuropsychopharmacology (2024)Cite this article

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Abstract

Thalamic abnormalities have been repeatedly implicated in the pathophysiology of schizophrenia and other neurodevelopmental disorders. Uncovering the etiology of thalamic abnormalities and how they may contribute to illness phenotypes faces at least two obstacles. First, the typical developmental trajectories of thalamic nuclei and their association with cognition across the lifespan are largely unknown. Second, modest effect sizes indicate marked individual differences and pose a significant challenge to personalized medicine. To address these knowledge gaps, we characterized the development of thalamic nuclei volumes using normative models generated from the Human Connectome Project Lifespan datasets (5–100+ years), then applied them to an independent clinical cohort to determine the frequency of thalamic volume deviations in people with schizophrenia (17–61 years). Normative models revealed diverse non-linear age effects across the lifespan. Association nuclei exhibited negative age effects during youth but stabilized in adulthood until turning negative again with older age. Sensorimotor nuclei volumes remained relatively stable through youth and adulthood until also turning negative with older age. Up to 18% of individuals with schizophrenia exhibited abnormally small (i.e., below the 5th centile) mediodorsal and pulvinar volumes, and the degree of deviation, but not raw volumes, correlated with the severity of cognitive impairment. While case–control differences are robust, only a minority of patients demonstrate unusually small thalamic nuclei volumes. Normative modeling enables the identification of these individuals, which is a necessary step toward precision medicine.

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Fig. 1: Normative development of thalamic nuclei in the right hemisphere.
Fig. 2: Association between executive function and thalamic nuclei volumes across development.
Fig. 3: Proportions of individuals showing deviations from normative ranges in the Schizophrenia Cohort.
Fig. 4: Comparison of associations of SCIP total scores with centile scores and standardized raw volumes (Std Vols) in the schizophrenia group.

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Data availability

Data for normative modeling were derived from publicly available datasets. Individual-level data for the schizophrenia cohort are not publicly accessible as the participants of this study did not give written consent for their data to be shared publicly. Neuroimaging data storage and processing took place at the Vanderbilt University Institute of Imaging Science Center for Computational Imaging XNAT [78, 79].

Code availability

Neuroimaging processing pipelines were containerized using Singularity, built at Singularity Hub [80] (https://singularity-hub.org), and are accessible through GitHub (https://github.com/baxpr/thomasdocker). Code for statistical analyses is accessible through GitHub (https://github.com/Woodward-Lab/NormModThalamicNuclei).

References

  1. Schmitt LI, Wimmer RD, Nakajima M, Happ M, Mofakham S, Halassa MM. Thalamic amplification of cortical connectivity sustains attentional control. Nature. 2017;545:219–23.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  2. Saalmann YB, Pinsk MA, Wang L, Li X, Kastner S. The pulvinar regulates information transmission between cortical areas based on attention demands. Science. 2012;337:753–6.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  3. Sommer MA. The role of the thalamus in motor control. Curr Opin Neurobiol. 2003;13:663–70.

    Article  CAS  PubMed  Google Scholar 

  4. McCormick DA, Bal T. Sensory gating mechanisms of the thalamus. Curr Opin Neurobiol. 1994;4:550–6.

    Article  CAS  PubMed  Google Scholar 

  5. Adams P, Guillery RW, Sherman SM, Sillito AM, Guillery RW, Sherman SM. The thalamus as a monitor of motor outputs. Philos Trans R Soc Lond B Biol Sci. 2002;357:1809–21.

    Article  Google Scholar 

  6. Wimmer RD, Schmitt LI, Davidson TJ, Nakajima M, Deisseroth K, Halassa MM. Thalamic control of sensory selection in divided attention. Nature. 2015;526:705–9.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  7. Rikhye RV, Gilra A, Halassa MM. Thalamic regulation of switching between cortical representations enables cognitive flexibility. Nat Neurosci. 2018;21:1753–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhou H, Schafer RJ, Desimone R. Pulvinar-cortex interactions in vision and attention. Neuron. 2016;89:209–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mørch-Johnsen L, Jørgensen KN, Barth C, Nerland S, Bringslid IK, Wortinger LA, et al. Thalamic nuclei volumes in schizophrenia and bipolar spectrum disorders—associations with diagnosis and clinical characteristics. Schizophr Res. 2023;256:26–35.

    Article  PubMed  Google Scholar 

  10. Huang AS, Rogers BP, Sheffield JM, Jalbrzikowski ME, Anticevic A, Blackford JU, et al. Thalamic nuclei volumes in psychotic disorders and in youths with psychosis spectrum symptoms. Am J Psychiatry. 2020. https://doi.org/10.1176/appi.ajp.2020.19101099.

  11. Byne W, Buchsbaum MS, Kemether E, Hazlett EA, Shinwari A, Mitropoulou V, et al. Magnetic resonance imaging of the thalamic mediodorsal nucleus and pulvinar in schizophrenia and schizotypal personality disorder. Arch Gen Psychiatry. 2001;58:133–40.

    Article  CAS  PubMed  Google Scholar 

  12. Hazlett EA, Buchsbaum MS, Byne W, Wei TC, Spiegel-Cohen J, Geneve C, et al. Three-dimensional analysis with MRI and PET of the size, shape, and function of the thalamus in the schizophrenia spectrum. Am J Psychiatry. 1999;156:1190–9.

    Article  CAS  PubMed  Google Scholar 

  13. Huang AS, Rogers BP, Woodward ND. Disrupted modulation of thalamus activation and thalamocortical connectivity during dual-task performance in schizophrenia. Schizophr Res. 2019;210:270–7.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Giraldo-Chica M, Woodward ND. Review of thalamocortical resting-state fMRI studies in schizophrenia. Schizophr Res. 2017;180:58–63.

    Article  PubMed  Google Scholar 

  15. Perez-Rando M, Elvira UKA, García-Martí G, Gadea M, Aguilar EJ, Escarti MJ, et al. Alterations in the volume of thalamic nuclei in patients with schizophrenia and persistent auditory hallucinations. Neuroimage Clin. 2022;35:103070.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ivanov I, Bansal R, Hao X, Zhu H, Kellendonk C, Miller L, et al. Morphological abnormalities of the thalamus in youths with attention deficit hyperactivity disorder. Am J Psychiatry. 2010;167:397–408.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Clerkin SM, Schulz KP, Berwid OG, Fan J, Newcorn JH, Tang CY, et al. Thalamo-cortical activation and connectivity during response preparation in adults with persistent and remitted ADHD. Am J Psychiatry. 2013;170:1011–9.

    Article  PubMed  Google Scholar 

  18. Batty MJ, Palaniyappan L, Scerif G, Groom MJ, Liddle EB, Liddle PF, et al. Morphological abnormalities in prefrontal surface area and thalamic volume in attention deficit/hyperactivity disorder. Psychiatry Res. 2015;233:225–32.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fu C, Chen S, Qian A, Zhou R, Zhou J, Li J, et al. Larger thalamus correlated with inattentive severity in the inattentive subtype of ADHD without comorbidity. Psychiatry Res. 2021;304:114079.

    Article  PubMed  Google Scholar 

  20. Tsatsanis KD, Rourke BP, Klin A, Volkmar FR, Cicchetti D, Schultz RT. Reduced thalamic volume in high-functioning individuals with autism. Biol Psychiatry. 2003;53:121–9.

    Article  PubMed  Google Scholar 

  21. Nair A, Treiber JM, Shukla DK, Shih P, Müller R-A. Impaired thalamocortical connectivity in autism spectrum disorder: a study of functional and anatomical connectivity. Brain. 2013;136:1942–55.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Woodward ND, Giraldo-Chica M, Rogers B, Cascio CJ. Thalamocortical dysconnectivity in autism spectrum disorder: an analysis of the autism brain imaging data exchange. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2:76–84.

    PubMed  PubMed Central  Google Scholar 

  23. Cheon K-A, Kim Y-S, Oh S-H, Park S-Y, Yoon H-W, Herrington J, et al. Involvement of the anterior thalamic radiation in boys with high functioning autism spectrum disorders: a Diffusion Tensor Imaging study. Brain Res. 2011;1417:77–86.

    Article  CAS  PubMed  Google Scholar 

  24. Schuetze M, Park MTM, Cho IY, MacMaster FP, Chakravarty MM, Bray SL. Morphological alterations in the thalamus, striatum, and pallidum in autism spectrum disorder. Neuropsychopharmacology. 2016;41:2627–37.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Tamura R, Kitamura H, Endo T, Hasegawa N, Someya T. Reduced thalamic volume observed across different subgroups of autism spectrum disorders. Psychiatry Res. 2010;184:186–8.

    Article  PubMed  Google Scholar 

  26. Aggleton JP, Pralus A, Nelson AJD, Hornberger M. Thalamic pathology and memory loss in early Alzheimer’s disease: moving the focus from the medial temporal lobe to Papez circuit. Brain. 2016;139:1877–90.

    Article  PubMed  PubMed Central  Google Scholar 

  27. van de Mortel LA, Thomas RM, van Wingen GA. Alzheimer’s disease neuroimaging initiative. Grey matter loss at different stages of cognitive decline: a role for the thalamus in developing Alzheimer’s disease. J Alzheimers Dis. 2021;83:705–20.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Štepán-Buksakowska I, Szabó N, Horínek D, Tóth E, Hort J, Warner J, et al. Cortical and subcortical atrophy in Alzheimer’s disease: parallel atrophy of thalamus and hippocampus. Alzheimer Dis Assoc Disord. 2014;28:65.

    Article  PubMed  Google Scholar 

  29. Zhu Q-Y, Bi S-W, Yao X-T, Ni Z-Y, Li Y, Chen B-Y, et al. Disruption of thalamic connectivity in Alzheimer’s disease: a diffusion tensor imaging study. Metab Brain Dis. 2015;30:1295–308.

    Article  CAS  PubMed  Google Scholar 

  30. Forno G, Saranathan M, Contador J, Guillen N, Falgàs N, Tort-Merino A, et al. Thalamic nuclei changes in early and late onset Alzheimer’s disease. Curr Res Neurobiol. 2023;4:100084.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wu Y, Wu X, Gao L, Yan Y, Geng Z, Zhou S, et al. Abnormal functional connectivity of thalamic subdivisions in Alzheimer’s disease: a functional magnetic resonance imaging study. Neuroscience. 2022;496:73–82.

    Article  CAS  PubMed  Google Scholar 

  32. Sheffield JM, Huang AS, Rogers BP, Giraldo-Chica M, Landman BA, Blackford JU, et al. Thalamocortical anatomical connectivity in schizophrenia and psychotic bipolar disorder. Schizophr Bull. 2020. https://doi.org/10.1093/schbul/sbaa022.

  33. Giraldo-Chica M, Rogers BP, Damon SM, Landman BA, Woodward ND. Prefrontal-thalamic anatomical connectivity and executive cognitive function in schizophrenia. Biol Psychiatry. 2018;83:509–17.

    Article  PubMed  Google Scholar 

  34. Woodward ND, Heckers S. Mapping thalamocortical functional connectivity in chronic and early stages of psychotic disorders. Biol Psychiatry. 2016;79:1016–25.

    Article  PubMed  Google Scholar 

  35. Coupé P, Catheline G, Lanuza E, Manjón JV.Alzheimer’s Disease Neuroimaging Initiative Towards a unified analysis of brain maturation and aging across the entire lifespan: an MRI analysis. Hum Brain Mapp. 2017;38:5501–18.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Narvacan K, Treit S, Camicioli R, Martin W, Beaulieu C. Evolution of deep gray matter volume across the human lifespan. Hum Brain Mapp. 2017;38:3771–90.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Fair DA, Liston C, et al. Neurodevelopment of the association cortices: patterns, mechanisms, and implications for psychopathology. Neuron 2021;109:2820–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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  CAS  PubMed  PubMed Central  Google Scholar 

  39. Danka Mohammed CP, Khalil R. Postnatal development of visual cortical function in the mammalian brain. Front Syst Neurosci. 2020;14:29.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Luna B. Developmental changes in cognitive control through adolescence. Adv Child Dev Behav. 2009;37:233–78.

  41. Huang AS, Rogers BP, Sheffield JM, Vandekar S, Anticevic A, Woodward ND. Characterizing effects of age, sex and psychosis symptoms on thalamocortical functional connectivity in youth. Neuroimage. 2021;243:118562.

    Article  PubMed  Google Scholar 

  42. Steiner L, Federspiel A, Slavova N, Wiest R, Grunt S, Steinlin M, et al. Functional topography of the thalamo-cortical system during development and its relation to cognition. Neuroimage. 2020;223:117361.

    Article  PubMed  Google Scholar 

  43. Avery SN, Huang AS, Sheffield JM, Rogers BP, Vandekar S, Anticevic A, et al. Development of thalamocortical structural connectivity in typically developing and psychosis spectrum youths. Biol Psychiatry Cogn Neurosci Neuroimaging. 2022;7:782–792.

  44. Choi EY, Tian L, Su JH, Radovan MT, Tourdias T, Tran TT, et al. Thalamic nuclei atrophy at high and heterogenous rates during cognitively unimpaired human aging. Neuroimage. 2022;262:119584.

    Article  PubMed  Google Scholar 

  45. Remiszewski N, Bryant JE, Rutherford SE, Marquand AF, Nelson E, Askar I, et al. Contrasting case-control and normative reference approaches to capture clinically relevant structural brain abnormalities in patients with first-episode psychosis who are antipsychotic naive. JAMA Psychiatry. 2022;79:1133–1138.

  46. 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 

  47. 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;26:3512–3523.

  48. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K, et al. The WU-Minn Human Connectome Project: an overview. Neuroimage. 2013;80:62–79.

    Article  PubMed  Google Scholar 

  49. Somerville LH, Bookheimer SY, Buckner RL, Burgess GC, Curtiss SW, Dapretto M, et al. The lifespan human connectome project in development: a large-scale study of brain connectivity development in 5–21 year olds. Neuroimage. 2018;183:456–68.

    Article  PubMed  Google Scholar 

  50. Bookheimer SY, Salat DH, Terpstra M, Ances BM, Barch DM, Buckner RL, et al. The Lifespan Human Connectome Project in aging: an overview. Neuroimage.2019;185:335–48.

    Article  PubMed  Google Scholar 

  51. Gershon RC, Cella D, Fox NA, Havlik RJ, Hendrie HC, Wagster MV. Assessment of neurological and behavioural function: the NIH Toolbox. Lancet Neurol. 2010;9:138–9.

    Article  PubMed  Google Scholar 

  52. Mungas D, Heaton R, Tulsky D, Zelazo PD, Slotkin J, Blitz D, et al. Factor structure, convergent validity, and discriminant validity of the NIH Toolbox Cognitive Health Battery (NIHTB-CHB) in adults. J Int Neuropsychol Soc. 2014;20:579–87.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Purdon SE. The Screen for Cognitive Impairment in Psychiatry (SCIP): instructions and three alternate forms. Edmonton, Alberta: PNL Inc.; 2005.

  54. Rojo E, Pino O, Guilera G, Gómez-Benito J, Purdon SE, Crespo-Facorro B, et al. Neurocognitive diagnosis and cut-off scores of the Screen for Cognitive Impairment in Psychiatry (SCIP-S). Schizophr Res. 2010;116:243–51.

    Article  PubMed  Google Scholar 

  55. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 2013;80:105–24.

    Article  PubMed  Google Scholar 

  56. Su JH, Thomas FT, Kasoff WS, Tourdias T, Choi EY, Rutt BK, et al. Thalamus Optimized Multi Atlas Segmentation (THOMAS): fast, fully automated segmentation of thalamic nuclei from structural MRI. Neuroimage 2019;194:272–82.

    Article  PubMed  Google Scholar 

  57. Mikis Stasinopoulos D, Rigby RA. Generalized Additive Models for Location Scale and Shape (GAMLSS) in R. J Stat Softw. 2008;23:1–46.

    Google Scholar 

  58. Ferguson BR, Gao W-J. Development of thalamocortical connections between the mediodorsal thalamus and the prefrontal cortex and its implication in cognition. Front Hum Neurosci. 2014;8:1027.

    PubMed  Google Scholar 

  59. Abitz M, Nielsen RD, Jones EG, Laursen H, Graem N, Pakkenberg B. Excess of neurons in the human newborn mediodorsal thalamus compared with that of the adult. Cereb Cortex. 2007;17:2573–8.

    Article  PubMed  Google Scholar 

  60. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2:861–3.

    Article  CAS  PubMed  Google Scholar 

  61. Ostby Y, Tamnes CK, Fjell AM, Westlye LT, Due-Tønnessen P, Walhovd KB. Heterogeneity in subcortical brain development: a structural magnetic resonance imaging study of brain maturation from 8 to 30 years. J Neurosci. 2009;29:11772–82.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Brain Development Cooperative Group. Total and regional brain volumes in a population-based normative sample from 4 to 18 years: the NIH MRI study of normal brain development. Cereb Cortex. 2012;22:1–12.

  63. Brown TT, Kuperman JM, Chung Y, Erhart M, McCabe C, Hagler DJ Jr, et al. Neuroanatomical assessment of biological maturity. Curr Biol. 2012;22:1693–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Walhovd KB, Fjell AM, Reinvang I, Lundervold A, Dale AM, Eilertsen DE, et al. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging. 2005;26:1261–70.

    Article  PubMed  Google Scholar 

  65. Walhovd KB, Westlye LT, Amlien I, Espeseth T, Reinvang I, Raz N, et al. Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiol Aging. 2011;32:916–32.

    Article  PubMed  Google Scholar 

  66. Fjell AM, Westlye LT, Grydeland H, Amlien I, Espeseth T, Reinvang I, et al. Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiol Aging. 2013;34:2239–47.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Potvin O, Mouiha A, Dieumegarde L, Duchesne S.Alzheimer’s Disease Neuroimaging Initiative ormative data for subcortical regional volumes over the lifetime of the adult human brain. Neuroimage. 2016;137:9–20.

    Article  PubMed  Google Scholar 

  68. Tullo S, Patel R, Devenyi GA, Salaciak A, Bedford SA, Farzin S, et al. MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Hum Brain Mapp. 2019;40:5269–88.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Wang Y, Xu Q, Luo J, Hu M, Zuo C. Effects of age and sex on subcortical volumes. Front Aging Neurosci. 2019;11:259.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Kharitonova M, Martin RE, Gabrieli JDE, Sheridan MA. Cortical gray-matter thinning is associated with age-related improvements on executive function tasks. Dev Cogn Neurosci. 2013;6:61–71.

    Article  PubMed  PubMed Central  Google Scholar 

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

  72. Woodward ND, Heckers S. Brain structure in neuropsychologically defined subgroups of schizophrenia and psychotic bipolar disorder. Schizophr Bull. 2015;41:1349–59.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Hickey TL. Postnatal development of the human lateral geniculate nucleus: relationship to a critical period for the visual system. Science. 1977;198:836–8.

    Article  CAS  PubMed  ADS  Google Scholar 

  74. Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, et al. The normative modeling framework for computational psychiatry. Nat Protoc. 2022;17:1711–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, et al. Normative modeling of brain morphometry across the lifespan using CentileBrain: algorithm benchmarking and model optimization. Lancet Digital Health. 2024;6:e211–e221.

  76. Weinberger DR, Radulescu E. Finding the elusive psychiatric ‘lesion’ with 21st-century neuroanatomy: a note of caution. AJP. 2016;173:27–33.

    Article  Google Scholar 

  77. Weinberger DR, Radulescu E. Structural magnetic resonance imaging all over again. JAMA Psychiatry. 2021;78:11–12.

    Article  PubMed  Google Scholar 

  78. Harrigan RL, Yvernault BC, Boyd BD, Damon SM, Gibney KD, Conrad BN, et al. Vanderbilt University Institute of imaging science center for computational imaging XNAT: a multimodal data archive and processing environment. Neuroimage. 2016;124:1097–101.

    Article  PubMed  Google Scholar 

  79. Huo Y, Blaber J, Damon SM, Boyd BD, Bao S, Parvathaneni P, et al. Towards portable large-scale image processing with high-performance computing. J Digit Imaging. 2018;31:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Sochat VV, Prybol CJ, Kurtzer GM. Enhancing reproducibility in scientific computing: metrics and registry for Singularity containers. PLoS One. 2017;12:e0188511.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN.

Funding

This work was supported by NIMH grants R01 MH102266, R01 MH115000, K24 MH126280, P50 MH132642-5980 (awarded to NDW), NIMH grant R01 MH123563 (awarded to SV), NIMH grant R01 MH070560 (awarded to SH); the Charlotte and Donald Test Fund and the Vanderbilt Institute for Clinical and Translational Research (through grant 1-UL-1-TR000445 from the National Center for Research Resources/NIH).

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Authors and Affiliations

  1. Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

    Anna S. Huang, Stephan Heckers & Neil D. Woodward

  2. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

    Kaidi Kang & Simon Vandekar

  3. Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, TN, USA

    Baxter P. Rogers

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  1. Anna S. Huang
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Contributions

Anna S. Huang: Conceptualization, Data Curation, Analysis, Data Interpretation, Methodology, Project Administration, Writing–Original Draft; Kaidi Kang: Analysis, Methods, Writing—Review and Edit; Baxter P. Rogers: Analysis, Methods, Writing–Review and Edit; Simon Vandekar: Analysis, Methods, Validation, Writing–Review and Edit; Stephan Heckers: Funding, Data Acquisition, Writing–Review and Edit; Neil D. Woodward: Conceptualization, Funding, Project Administration, Data Interpretation, Resources, Supervision, Writing–Original Draft, Review and Edit.

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Correspondence to Anna S. Huang.

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Huang, A.S., Kang, K., Vandekar, S. et al. Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling. Neuropsychopharmacol. (2024). https://doi.org/10.1038/s41386-024-01837-y

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