Skip to main content
SpringerLink
Log in

Age-dependent alterations in the coordinated development of subcortical regions in adolescents with social anxiety disorder

  • Original Contribution
  • Published:
European Child & Adolescent Psychiatry Aims and scope Submit manuscript

Abstract

Subcortical brain regions play essential roles in the pathology of social anxiety disorder (SAD). While adolescence is the peak period of SAD, the relationships between altered development of the subcortical regions during this period and SAD are still unclear. This study investigated the age-dependent alterations in structural co-variance among subcortical regions and between subcortical and cortical regions, aiming to reflect aberrant coordination during development in the adolescent with SAD. High-resolution T1-weighted images were obtained from 76 adolescents with SAD and 67 healthy controls (HC), ranging from 11 to 17.9 years. Symptom severity was evaluated with the Social Anxiety Scale for Children (SASC) and the Depression Self Rating Scale for Children (DSRS-C). Structural co-variance and sliding age-window analyses were used to detect age-dependent group differences in inter-regional coordination patterns among subcortical regions and between subcortical and cortical regions. The volume of the striatum significantly correlated with SAD symptom severity. The SAD group exhibited significantly enhanced structural co-variance among key regions of the striatum (putamen and caudate). While the co-variance decreased with age in healthy adolescents, the co-variance in SAD adolescents stayed high, leading to more apparent group differences in middle adolescence. Moreover, the striatum’s mean structural co-variance with cortical regions decreased with age in HC but increased with age in SAD. Adolescents with SAD suffer aberrant developmental coordination among the key regions of the striatum and between the striatum and cortical regions. The degree of incoordination is age-dependent, which may represent a neurodevelopmental trait of SAD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

Requests for access to the anonymized data can be sent to the corresponding author and reviewed by the IRB. The code and image-derived statistics are shared at: https://github.com/PHI-group/StableProjects.

References

  1. Mathew SJ, Coplan JD, Gorman JM (2001) Neurobiological mechanisms of social anxiety disorder. Am J Psychiatry 158:1558–1567. https://doi.org/10.1176/appi.ajp.158.10.1558

    Article  CAS  PubMed  Google Scholar 

  2. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596

  3. Richey JA, Rittenberg A, Hughes L et al (2014) Common and distinct neural features of social and non-social reward processing in autism and social anxiety disorder. Soc Cogn Affect Neurosci 9:367–377. https://doi.org/10.1093/scan/nss146

    Article  PubMed  Google Scholar 

  4. Todd BK (2007) Social anxiety spectrum and diminished positive experiences: theoretical synthesis and meta-analysis. Clin Psychol Rev. https://doi.org/10.1016/j.cpr.2006.12.003

    Article  Google Scholar 

  5. Beesdo K, Knappe S, Pine DS (2009) Anxiety and anxiety disorders in children and adolescents: developmental issues and implications for DSM-V. Psychiatr Clin North Am 32:483–524. https://doi.org/10.1016/j.psc.2009.06.002

    Article  PubMed  PubMed Central  Google Scholar 

  6. Xie S, Zhang X, Cheng W (2021) Adolescent anxiety disorders and the developing brain: comparing neuroimaging findings in adolescents and adults. Gen Psychiatry. https://doi.org/10.1136/gpsych-2020-100411

    Article  Google Scholar 

  7. Natu VS, Gomez J, Barnett M et al (2019) Apparent thinning of human visual cortex during childhood is associated with myelination. Proc Natl Acad Sci USA 116:20750–20759. https://doi.org/10.1073/pnas.1904931116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Heller AS, Cohen AO, Dreyfuss MFW et al (2016) Changes in cortico-subcortical and subcortico-subcortical connectivity impact cognitive control to emotional cues across development. Soc Cogn Affect Neurosci 11:1910–1918. https://doi.org/10.1093/scan/nsw097

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tottenham N, Galván A (2016) Stress and the adolescent brain: amygdala-prefrontal cortex circuitry and ventral striatum as developmental targets. Neurosci Biobehav Rev 70:217–227. https://doi.org/10.1016/j.neubiorev.2016.07.030

    Article  PubMed  PubMed Central  Google Scholar 

  10. Spear LP (2000) The adolescent brain and age-related behavioral manifestations. Neurosci Biobehav Rev 24:417–463. https://doi.org/10.1016/S0149-7634(00)00014-2

    Article  CAS  PubMed  Google Scholar 

  11. Caouette JD, Guyer AE (2014) Gaining insight into adolescent vulnerability for social anxiety from developmental cognitive neuroscience. Dev Cogn Neurosci 8:65–76. https://doi.org/10.1016/j.dcn.2013.10.003

    Article  PubMed  Google Scholar 

  12. Etkin A, Wager TD (2007) Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. Am J Psychiatry 164:1476–1488. https://doi.org/10.1176/appi.ajp.2007.07030504

    Article  PubMed  PubMed Central  Google Scholar 

  13. Phelps EA, LeDoux JE (2005) Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 48:175–187. https://doi.org/10.1016/j.neuron.2005.09.025

    Article  CAS  PubMed  Google Scholar 

  14. Schultz J, Willems T, Gädeke M et al (2019) A human subcortical network underlying social avoidance revealed by risky economic choices. Elife 8:e45249. https://doi.org/10.7554/eLife.45249

    Article  PubMed  PubMed Central  Google Scholar 

  15. Miskovic V, Schmidt LA (2012) Social fearfulness in the human brain. Neurosci Biobehav Rev 36:459–478. https://doi.org/10.1016/j.neubiorev.2011.08.002

    Article  PubMed  Google Scholar 

  16. Shin LM, Liberzon I (2010) The neurocircuitry of fear, stress, and anxiety disorders. Neuropsychopharmacology 35:169–191. https://doi.org/10.1038/npp.2009.83

    Article  PubMed  Google Scholar 

  17. Tovote P, Fadok JP, Lüthi A (2015) Neuronal circuits for fear and anxiety. Nat Rev Neurosci 16:317–331. https://doi.org/10.1038/nrn3945

    Article  CAS  PubMed  Google Scholar 

  18. de Carvalho MR, Rozenthal M, Nardi AE (2010) The fear circuitry in panic disorder and its modulation by cognitive-behaviour therapy interventions. World J Biol Psychiatry 11:188–198. https://doi.org/10.1080/15622970903178176

    Article  PubMed  Google Scholar 

  19. Freitas-Ferrari MC, Hallak JEC, Trzesniak C et al (2010) Neuroimaging in social anxiety disorder: a systematic review of the literature. Prog Neuropsychopharmacol Biol Psychiatry 34:565–580. https://doi.org/10.1016/j.pnpbp.2010.02.028

    Article  PubMed  Google Scholar 

  20. Brühl AB, Delsignore A, Komossa K et al (2014) Neuroimaging in social anxiety disorder—a meta-analytic review resulting in a new neurofunctional model. Neurosci Biobehav Rev 47:260–280. https://doi.org/10.1016/j.neubiorev.2014.08.003

    Article  PubMed  Google Scholar 

  21. Marchand WR (2010) Cortico-basal ganglia circuitry: a review of key research and implications for functional connectivity studies of mood and anxiety disorders. Brain Struct Funct 215:73–96. https://doi.org/10.1007/s00429-010-0280-y

    Article  PubMed  Google Scholar 

  22. Ruff CC, Fehr E (2014) The neurobiology of rewards and values in social decision making. Nat Rev Neurosci 15:549–562. https://doi.org/10.1038/nrn3776

    Article  CAS  PubMed  Google Scholar 

  23. Becker MPI, Simon D, Miltner WHR et al (2017) Altered activation of the ventral striatum under performance-related observation in social anxiety disorder. Psychol Med 47:2502–2512. https://doi.org/10.1017/S0033291717001076

    Article  CAS  PubMed  Google Scholar 

  24. Crane NA, Chang F, Kinney KL et al (2021) Individual differences in striatal and amygdala response to emotional faces are related to symptom severity in social anxiety disorder. NeuroImage Clin 30

  25. Taber-Thomas BC, Morales S, Hillary FG et al (2016) Altered topography of intrinsic functional connectivity in childhood risk for social anxiety. Depress Anxiety 33:995–1004. https://doi.org/10.1002/da.22508

    Article  PubMed  PubMed Central  Google Scholar 

  26. Guyer AE, Choate VR, Detloff A et al (2012) Striatal functional alteration during incentive anticipation in pediatric anxiety disorders. Am J Psychiatry 169:205–212. https://doi.org/10.1176/appi.ajp.2011.11010006

    Article  PubMed  PubMed Central  Google Scholar 

  27. Carlton CN, Sullivan-Toole H, Ghane M et al (2020) Reward circuitry and motivational deficits in social anxiety disorder: what can be learned from mouse models? Front Neurosci 14:154. https://doi.org/10.3389/fnins.2020.00154

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zhu H, Qiu C, Meng Y et al (2017) Altered topological properties of brain networks in social anxiety disorder: a resting-state functional MRI study. Sci Rep 7:43089. https://doi.org/10.1038/srep43089

    Article  PubMed  PubMed Central  Google Scholar 

  29. Schneier FR, Abi-Dargham A, Martinez D et al (2009) Dopamine transporters, D2 receptors, and dopamine release in generalized social anxiety disorder. Depress Anxiety 26:411–418. https://doi.org/10.1002/da.20543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. van der Wee NJ, van Veen JF, Stevens H et al (2008) Increased serotonin and dopamine transporter binding in psychotropic medication-naive patients with generalized social anxiety disorder shown by 123I-beta-(4-iodophenyl)-tropane SPECT. J Nucl Med 49:757–763. https://doi.org/10.2967/jnumed.107.045518

    Article  PubMed  Google Scholar 

  31. Bas-Hoogendam JM, van Steenbergen H, Tissier RLM et al (2018) Subcortical brain volumes, cortical thickness and cortical surface area in families genetically enriched for social anxiety disorder—a multiplex multigenerational neuroimaging study. EBioMedicine 36:410–428. https://doi.org/10.1016/j.ebiom.2018.08.048

    Article  PubMed  PubMed Central  Google Scholar 

  32. Monique E, Daniel SP, Michael H (2006) Triadic model of the neurobiology of motivated behavior in adolescence. Psychol Med 36:299–312. https://doi.org/10.1017/S0033291705005891

    Article  Google Scholar 

  33. Ernst M (2014) The triadic model perspective for the study of adolescent motivated behavior. Brain Cogn 89:104–111. https://doi.org/10.1016/j.bandc.2014.01.006

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ernst M, Fudge JL (2009) A developmental neurobiological model of motivated behavior: anatomy, connectivity and ontogeny of the triadic nodes. Neurosci Biobehav Rev 33:367–382. https://doi.org/10.1016/j.neubiorev.2008.10.009

    Article  PubMed  Google Scholar 

  35. Burgoyne RD, Graham ME, Cambray-Deakin M (1993) Neurotrophic effects of NMDA receptor activation on developing cerebellar granule cells. J Neurocytol 22:689–695. https://doi.org/10.1007/BF01181314

    Article  CAS  PubMed  Google Scholar 

  36. Bullmore ET, Frangou S, Murray RM (1997) The dysplastic net hypothesis: an integration of developmental and dysconnectivity theories of schizophrenia. Schizophr Res 28:143–156. https://doi.org/10.1016/s0920-9964(97)00114-x

    Article  CAS  PubMed  Google Scholar 

  37. Mechelli A, Friston KJ, Frackowiak RS et al (2005) Structural covariance in the human cortex. J Neurosci 25:8303–8310. https://doi.org/10.1523/JNEUROSCI.0357-05.2005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14:322–336. https://doi.org/10.1038/nrn3465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Liu Z, Hu Y, Zhang Y et al (2021) Altered gray matter volume and structural co-variance in adolescents with social anxiety disorder: evidence for a delayed and unsynchronized development of the fronto-limbic system. Psychol Med 51:1742–1751. https://doi.org/10.1017/S0033291720000495

    Article  PubMed  Google Scholar 

  40. Alexander-Bloch A, Raznahan A, Bullmore E et al (2013) The convergence of maturational change and structural covariance in human cortical networks. J Neurosci 33:2889–2899. https://doi.org/10.1523/JNEUROSCI.3554-12.2013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Duan X, Wang R, Xiao J et al (2020) Subcortical structural covariance in young children with autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 99:109874. https://doi.org/10.1016/j.pnpbp.2020.109874

    Article  PubMed  Google Scholar 

  42. Wannan CMJ, Cropley VL, Chakravarty MM et al (2019) Evidence for network-based cortical thickness reductions in Schizophrenia. Am J Psychiatry 176:552–563. https://doi.org/10.1176/appi.ajp.2019.18040380

    Article  PubMed  Google Scholar 

  43. Liu Y, Liu J, Wang Y (2011) Reliability and validity of Chinese version of the Mini International Neuropsychiatric Interview for Children and Adolescents (child version). Chin Ment Health J 25:8–13

    Google Scholar 

  44. Yu L (2010) Reliability and validity of Chinese version of the Mini International Neuropsychiatric Interview for Children and Adolescents (Parent Version)

  45. Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9:179–194. https://doi.org/10.1006/nimg.1998.0395

    Article  CAS  PubMed  Google Scholar 

  46. Fischl B, Sereno MI, Dale AM (1999) Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195–207. https://doi.org/10.1006/nimg.1998.0396

    Article  CAS  PubMed  Google Scholar 

  47. Desikan RS, Ségonne F, Fischl B 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 

  48. Fischl B, Salat DH, Busa E et al (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33:341–355. https://doi.org/10.1016/S0896-6273(02)00569-X

    Article  CAS  PubMed  Google Scholar 

  49. Gaser C, Dahnke R, Thompson PM et al (2022) CAT—a computational anatomy toolbox for the analysis of structural MRI data. bioRxiv 2022.06.11.495736. https://doi.org/10.1101/2022.06.11.495736

  50. La Greca AM, Stone WL (1993) Social anxiety scale for children-revised: factor structure and concurrent validity. J Clin Child Psychol 22:17–27. https://doi.org/10.1207/s15374424jccp2201_2

    Article  Google Scholar 

  51. Birleson P, Hudson I, Buchanan DG et al (1987) Clinical evaluation of a self-rating scale for depressive disorder in childhood (depression Self-Rating Scale). J Child Psychol Psychiatry 28:43–60. https://doi.org/10.1111/j.1469-7610.1987.tb00651.x

    Article  CAS  PubMed  Google Scholar 

  52. Hardin AP, Hackell JM (2017) Age limit of pediatrics. Pediatrics 140:e20172151. https://doi.org/10.1542/peds.2017-2151

    Article  PubMed  Google Scholar 

  53. Vijayakumar N, Ball G, Seal ML et al (2021) The development of structural covariance networks during the transition from childhood to adolescence. Sci Rep 11:9451. https://doi.org/10.1038/s41598-021-88918-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Váša F, Seidlitz J, Romero-Garcia R et al (2018) Adolescent tuning of association cortex in human structural brain networks. Cereb Cortex 28:281–294. https://doi.org/10.1093/cercor/bhx249

    Article  PubMed  Google Scholar 

  55. Oldham S, Murawski C, Fornito A et al (2018) The anticipation and outcome phases of reward and loss processing: a neuroimaging meta-analysis of the monetary incentive delay task. Hum Brain Mapp 39:3398–3418. https://doi.org/10.1002/hbm.24184

    Article  PubMed  PubMed Central  Google Scholar 

  56. Raznahan A, Shaw PW, Lerch JP et al (2014) Longitudinal four-dimensional mapping of subcortical anatomy in human development. Proc Natl Acad Sci 111:1592–1597. https://doi.org/10.1073/pnas.1316911111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Telzer EH (2016) Dopaminergic reward sensitivity can promote adolescent health: a new perspective on the mechanism of ventral striatum activation. Dev Cogn Neurosci 17:57–67. https://doi.org/10.1016/j.dcn.2015.10.010

    Article  PubMed  Google Scholar 

  58. Larsen B, Luna B (2015) In vivo evidence of neurophysiological maturation of the human adolescent striatum. Dev Cogn Neurosci 12:74–85. https://doi.org/10.1016/j.dcn.2014.12.003

    Article  PubMed  Google Scholar 

  59. Bhanji JP, Delgado MR (2014) The social brain and reward: social information processing in the human striatum. Wiley Interdiscip Rev Cogn Sci 5:61–73. https://doi.org/10.1002/wcs.1266

    Article  PubMed  Google Scholar 

  60. Hanson JL, Hariri AR, Williamson DE (2015) Blunted ventral striatum development in adolescence reflects emotional neglect and predicts depressive symptoms. Biol Psychiatry 78:598–605. https://doi.org/10.1016/j.biopsych.2015.05.010

    Article  PubMed  PubMed Central  Google Scholar 

  61. Zhang Y, Zhu C, Chen H et al (2015) Frequency-dependent alterations in the amplitude of low-frequency fluctuations in social anxiety disorder. J Affect Disord 174:329–335. https://doi.org/10.1016/j.jad.2014.12.001

    Article  PubMed  Google Scholar 

  62. Wager TD, Davidson ML, Hughes BL et al (2008) Prefrontal-subcortical pathways mediating successful emotion regulation. Neuron 59:1037–1050. https://doi.org/10.1016/j.neuron.2008.09.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. van den Bos W, Cohen MX, Kahnt T et al (2012) Striatum-medial prefrontal cortex connectivity predicts developmental changes in reinforcement learning. Cereb Cortex 22:1247–1255. https://doi.org/10.1093/cercor/bhr198

    Article  PubMed  PubMed Central  Google Scholar 

  64. Bas-Hoogendam JM, van Steenbergen H, Nienke Pannekoek J et al (2017) Voxel-based morphometry multi-center mega-analysis of brain structure in social anxiety disorder. NeuroImage Clin 16:678–688. https://doi.org/10.1016/j.nicl.2017.08.001

    Article  PubMed  PubMed Central  Google Scholar 

  65. Wang X, Cheng B, Wang S et al (2021) Distinct grey matter volume alterations in adult patients with panic disorder and social anxiety disorder: a systematic review and voxel-based morphometry meta-analysis. J Affect Disord 281:805–823. https://doi.org/10.1016/j.jad.2020.11.057

    Article  PubMed  Google Scholar 

  66. Zhao Y, Chen L, Zhang W et al (2017) Gray matter abnormalities in non-comorbid medication-naive patients with major depressive disorder or social anxiety disorder. EBioMedicine 21:228–235. https://doi.org/10.1016/j.ebiom.2017.06.013

    Article  PubMed  PubMed Central  Google Scholar 

  67. Wang X, Cheng B, Luo Q et al (2018) Gray matter structural alterations in social anxiety disorder: a voxel-based meta-analysis. Front Psychiatry. https://doi.org/10.3389/fpsyt.2018.00449

    Article  PubMed  PubMed Central  Google Scholar 

  68. Østby Y, Tamnes CK, Fjell AM et al (2009) Heterogeneity in subcortical brain development: a structural magnetic resonance imaging study of brain maturation from 8 to 30 years. J Neurosci 29:11772–11782. https://doi.org/10.1523/jneuroSCI.1242-09.2009

    Article  PubMed  PubMed Central  Google Scholar 

  69. Low LK, Cheng H-J (2006) Axon pruning: an essential step underlying the developmental plasticity of neuronal connections. Philos Trans R Soc B Biol Sci 361:1531–1544. https://doi.org/10.1098/rstb.2006.1883

    Article  CAS  Google Scholar 

  70. Albaugh MD, Nguyen T-V, Ducharme S et al (2017) Age-related volumetric change of limbic structures and subclinical anxious/depressed symptomatology in typically developing children and adolescents. Biol Psychol 124:133–140. https://doi.org/10.1016/j.biopsycho.2017.02.002

    Article  PubMed  Google Scholar 

  71. Foell J, Palumbo IM, Yancey JR et al (2019) Biobehavioral threat sensitivity and amygdala volume: a twin neuroimaging study. Neuroimage 186:14–21. https://doi.org/10.1016/j.neuroimage.2018.10.065

    Article  PubMed  Google Scholar 

  72. Lee KH, Yoo JH, Lee J et al (2020) The indirect effect of peer problems on adolescent depression through nucleus accumbens volume alteration. Sci Rep 10:12870. https://doi.org/10.1038/s41598-020-69769-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Heshmati M, Russo SJ (2015) Anhedonia and the brain reward circuitry in depression. Curr Behav Neurosci Rep 2:146–153. https://doi.org/10.1007/s40473-015-0044-3

    Article  PubMed  PubMed Central  Google Scholar 

  74. Zhang Y, Liu W, Lebowitz ER et al (2020) Abnormal asymmetry of thalamic volume moderates stress from parents and anxiety symptoms in children and adolescents with social anxiety disorder. Neuropharmacology 180:108301. https://doi.org/10.1016/j.neuropharm.2020.108301

    Article  CAS  PubMed  Google Scholar 

  75. Boyes A, McLoughlin LT, Anderson H et al (2022) Basal ganglia correlates of wellbeing in early adolescence. Brain Res 1774:147710. https://doi.org/10.1016/j.brainres.2021.147710

    Article  CAS  PubMed  Google Scholar 

  76. Rubin RD, Watson PD, Duff MC et al (2014) The role of the hippocampus in flexible cognition and social behavior. Front Hum Neurosci 8:742

    Article  PubMed  PubMed Central  Google Scholar 

  77. Felix-Ortiz AC, Tye KM (2014) Amygdala inputs to the ventral hippocampus bidirectionally modulate social behavior. J Neurosci 34:586–595. https://doi.org/10.1523/JNEUROSCI.4257-13.2014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Ashar YK, Clark J, Gunning FM et al (2021) Brain markers predicting response to cognitive-behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al 2015. Transl Psychiatry. 11: 260. https://doi.org/10.1038/s41398-021-01366-y

  79. Whitfield-Gabrieli S, Ghosh SS, Nieto-Castanon A et al (2016) Brain connectomics predict response to treatment in social anxiety disorder. Mol Psychiatry 21:680–685. https://doi.org/10.1038/mp.2015.109

    Article  CAS  PubMed  Google Scholar 

  80. Craske MG, Meuret AE, Ritz T et al (2016) Treatment for anhedonia: a neuroscience driven approach. Depress Anxiety 33:927–938. https://doi.org/10.1002/da.22490

    Article  PubMed  Google Scholar 

  81. Bystritsky A, Khalsa SS, Cameron ME et al (2013) Current diagnosis and treatment of anxiety disorders. Pharm Ther 38:30–57

    Google Scholar 

  82. Gorwood P (2008) Neurobiological mechanisms of anhedonia. Dialogues Clin Neurosci 10:291–299

    Article  PubMed  PubMed Central  Google Scholar 

  83. Strege MV, Swain D, Bochicchio L et al (2018) A pilot study of the effects of mindfulness-based cognitive therapy on positive affect and social anxiety symptoms. Front Psychol 9:866

    Article  PubMed  PubMed Central  Google Scholar 

  84. Bas-Hoogendam JM, Groenewold NA, Aghajani M et al (2022) ENIGMA-anxiety working group: rationale for and organization of large-scale neuroimaging studies of anxiety disorders. Hum Brain Mapp 43:83–112. https://doi.org/10.1002/hbm.25100

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (81971682, 81571756); Shanghai Science and Technology Commission (20Y11906700); Project of Shanghai Children’s Health Service Capacity Construction (GDEK201702); Clinical Research Project of Shanghai Mental Health Center (CRC2018DSJ01-5; CRC2019ZD04); Natural Science Foundation of Shanghai (20ZR1472800); Shanghai Municipal Commission of Education (Gaofeng Clinical Medicine-20171929); Shanghai Science and Technology Commission (18JC1420305); Shanghai Municipal Health Commission (2019ZB0201; 2018BR17); Shanghai Clinical Research Center for Mental Health (19MC1911100); and Research Funds from Shanghai Mental Health Center (13dz2260500, 2018-YJ-02, 2018-YJ-05).

Funding

2030 Science and Technology Innovation Key R&D Program, China (2022ZD0209101); National Natural Science Foundation of China (81971682); Shanghai Science and Technology Commission (20ZR1472800, 20Y11906700, 18JC1420305); Project of Shanghai Children’s Health Service Capacity Construction (GDEK201702); Clinical Research Project of Shanghai Mental Health Center  (CRC2018DSJ01-5); Shanghai Municipal Commission of Education (Gaofeng Clinical Medicine-20171929); Shanghai Municipal Health Commission (2019ZB0201); Shanghai Clinical Research Center for Mental Health (19MC1911100); Research Funds from Shanghai Mental Health Center (13dz2260500).

Author information

Author notes

  1. Jingjing Liu and Shuqi Xie have contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 South Wanping Road, Shanghai, 200013, China

    Jingjing Liu, Shuqi Xie, Yang Hu, Yue Ding, Xiaochen Zhang, Lei Zhang, Yinzhi Kang, Shuyu Jin, Yufeng Xia, Zhishan Hu & Zhi Yang

  2. Department of Child and Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 South Wanping Road, Shanghai, 200013, China

    Wenjing Liu, Changminghao Ma, Zhen Liu & Wenhong Cheng

  3. Institute of Psychological and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, China

    Zhi Yang

  4. Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China

    Zhi Yang

Authors
  1. Jingjing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuqi Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yue Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaochen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wenjing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Changminghao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yinzhi Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shuyu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yufeng Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zhishan Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Wenhong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Zhi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Zhi Yang and Wenhong Cheng conceived this work. Wenhong Cheng, Wenjing Liu, Zhen Liu, and Changminghao Ma recruited participants. Jingjing Liu, Shuqi Xie, Shuyu Jin, Yang Hu, Lei Zhang completed data acquisition. Yue Ding, Yufeng Xia, Xiaochen Zhang, Yinzhi Kang, Zhishan Hu, Wenhong Cheng and Zhi Yang provided critical comments related to the interpretation of the study findings. Shuqi Xie helped to check the content of the article. Jingjing Liu completed data analysis and led on drafting the manuscript with subsequent iterations refined by Zhi Yang. All authors have approved the final version for publication.

Corresponding authors

Correspondence to Wenhong Cheng or Zhi Yang.

Ethics declarations

Conflict of interests

The authors declare no conflict of interests.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1673 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Xie, S., Hu, Y. et al. Age-dependent alterations in the coordinated development of subcortical regions in adolescents with social anxiety disorder. Eur Child Adolesc Psychiatry 33, 51–64 (2024). https://doi.org/10.1007/s00787-022-02118-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00787-022-02118-z

Keywords

Search

Navigation