Accelerated cortical thinning within structural brain networks is associated with irritability in youth


Irritability is an important dimension of psychopathology that spans multiple clinical diagnostic categories, yet its relationship to patterns of brain development remains sparsely explored. Here, we examined how transdiagnostic symptoms of irritability relate to the development of structural brain networks. All participants (n = 137, 83 females) completed structural brain imaging with 3 Tesla MRI at two timepoints (mean age at follow-up: 21.1 years, mean inter-scan interval: 5.2 years). Irritability at follow-up was assessed using the Affective Reactivity Index, and cortical thickness was quantified using Advanced Normalization Tools software. Structural covariance networks were delineated using non-negative matrix factorization, a multivariate analysis technique. Both cross-sectional and longitudinal associations with irritability at follow-up were evaluated using generalized additive models with penalized splines. The False Discovery Rate (q < 0.05) was used to correct for multiple comparisons. Cross-sectional analysis of follow-up data revealed that 11 of the 24 covariance networks were associated with irritability, with higher levels of irritability being associated with thinner cortex. Longitudinal analyses further revealed that accelerated cortical thinning within nine networks was related to irritability at follow-up. Effects were particularly prominent in brain regions implicated in emotion regulation, including the orbitofrontal, lateral temporal, and medial temporal cortex. Collectively, these findings suggest that irritability is associated with widespread reductions in cortical thickness and accelerated cortical thinning, particularly within the frontal and temporal cortex. Aberrant structural maturation of regions important for emotional regulation may in part underlie symptoms of irritability.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1
Fig. 2
Fig. 3


  1. 1.

    Leibenluft E. Severe mood dysregulation, irritability, and the diagnostic boundaries of bipolar disorder in youths. Am J Psychiatry. 2011;168:129–42.

  2. 2.

    Stringaris A, et al. Adult outcomes of youth irritability: a 20-year prospective community-based study. Am J Psychiatry. 2009;166:1048–54.

  3. 3.

    Stringaris A, et al. Youth meeting symptom and impairment criteria for mania-like episodes lasting less than four days: an epidemiological enquiry. J Child Psychol Psychiatry. 2010;51:31–8.

  4. 4.

    Biederman J, et al. Further evidence of unique developmental phenotypic correlates of pediatric bipolar disorder: findings from a large sample of clinically referred preadolescent children assessed over the last 7 years. J Affect Disord. 2004;82(Suppl 1):S45–58.

  5. 5.

    Stringaris A, et al. Pediatric bipolar disorder versus severe mood dysregulation: risk for manic episodes on follow-up. J Am Acad Child Adolesc Psychiatry. 2010;49:397–405.

  6. 6.

    Leibenluft E, et al. Chronic versus episodic irritability in youth: a community-based, longitudinal study of clinical and diagnostic associations. J Child Adolesc Psychopharmacol. 2006;16:456–66.

  7. 7.

    Leibenluft E, et al. Defining clinical phenotypes of juvenile mania. Am J Psychiatry. 2003;160:430–7.

  8. 8.

    Research Domain Criteria (RDoC). Toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167:748–51.

  9. 9.

    Casey BJ, Oliveri ME, Insel T. A neurodevelopmental perspective on the Research Domain Criteria (RDoC) framework. Biol Psychiatry. 2014;76:350–3.

  10. 10.

    Brotman MA, et al. Prevalence, clinical correlates, and longitudinal course of severe mood dysregulation in children. Biol Psychiatry. 2006;60:991–7.

  11. 11.

    Dougherty LR, et al. Preschool irritability predicts child psychopathology, functional impairment, and service use at age nine. J Child Psychol Psychiatry. 2015;56:999–1007.

  12. 12.

    Belden AC, Thomson NR, Luby JL. Temper tantrums in healthy versus depressed and disruptive preschoolers: defining tantrum behaviors associated with clinical problems. J Pediatrics. 2008;152:117–22.

  13. 13.

    Keenan K, Wakschlag LS. More than the terrible twos: the nature and severity of behavior problems in clinic-referred preschool children. J Abnorm Child Psychol. 2000;28:33–46.

  14. 14.

    S. Wakschlag L, et al. Clinical implications of a dimensional approach: the normal:abnormal spectrum of early irritability. J Am Acad Child Adolesc Psychiatry 2015;54:626–34.

  15. 15.

    Pagliaccio D, et al. Irritability trajectories, cortical thickness, and clinical outcomes in a sample enriched for preschool depression. J Am Acad Child Adolesc Psychiatry. 2018;57:336.e6

  16. 16.

    Adleman NE, et al. Cross-sectional and longitudinal abnormalities in brain structure in children with severe mood dysregulation or bipolar disorder. J Child Psychol Psychiatry. 2012;53:1149–56.

  17. 17.

    Deveney CM, et al. Neural mechanisms of frustration in chronically irritable children. Am J Psychiatry. 2013;170:1186–94.

  18. 18.

    Tseng W-L, et al. Brain mechanisms of attention orienting following frustration: associations with irritability and age in youths. Am J Psychiatry. 2018;176:67–76.

  19. 19.

    Wiggins JL, et al. Neural correlates of irritability in disruptive mood dysregulation and bipolar disorders. Am J Psychiatry. 2016;173:722–30.

  20. 20.

    Deveney CM, et al. Neural recruitment during failed motor inhibition differentiates youths with bipolar disorder and severe mood dysregulation. Biol Psychol. 2012;89:148–55.

  21. 21.

    Singh MK, et al. Neural correlates of response inhibition in pediatric bipolar disorder. J Child Adolesc Psychopharmacol. 2010;20:15–24.

  22. 22.

    Li Y, et al. The neural substrates of cognitive flexibility are related to individual differences in preschool irritability: a fNIRS investigation. Dev Cogn Neurosci. 2017;25:138–44.

  23. 23.

    Perlman SB, et al. fNIRS evidence of prefrontal regulation of frustration in early childhood. Neuroimage. 2014;85(Pt 1):326–34.

  24. 24.

    Kringelbach ML. The human orbitofrontal cortex: linking reward to hedonic experience. Nat Rev Neurosci. 2005;6:691.

  25. 25.

    Goddard GV. Functions of the amygdala. Psychol. Bull. 1964;62:89–109.

  26. 26.

    Fulwiler CE, King JA, Zhang N. Amygdala-orbitofrontal resting-state functional connectivity is associated with trait anger. Neuroreport. 2012;23:606–10.

  27. 27.

    Paus T, Keshavan M, Giedd JN. Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci. 2008;9:947–57.

  28. 28.

    Giedd JN, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2:861.

  29. 29.

    Sotiras A, Resnick SM, Davatzikos C. Finding imaging patterns of structural covariance via non-negative matrix factorization. Neuroimage. 2015;108:1–16.

  30. 30.

    Sotiras A, et al. Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion. Proc Natl Acad Sci USA. 2017;114:3527–32.

  31. 31.

    Satterthwaite TD, et al. The Philadelphia Neurodevelopmental Cohort: a publicly available resource for the study of normal and abnormal brain development in youth. NeuroImage. 2016;124:1115–9.

  32. 32.

    Satterthwaite TD, et al. Neuroimaging of the Philadelphia neurodevelopmental cohort. Neuroimage. 2014;86:544–53.

  33. 33.

    Calkins ME, et al. The psychosis spectrum in a young U.S. community sample: findings from the Philadelphia Neurodevelopmental Cohort. World Psychiatry. 2014;13:296–305.

  34. 34.

    Kaufman J, et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry. 1997;36:980–8.

  35. 35.

    Calkins ME, et al. The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. J Child Psychol Psychiatry. 2015;56:1356–69.

  36. 36.

    Calkins ME, et al. Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow-up. World Psychiatry. 2017;16:62–76.

  37. 37.

    First, M.B. and M. Gibbon, The Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II), in Comprehensive handbook of psychological assessment, Vol. 2: Personality assessment. 2004, John Wiley & Sons Inc: Hoboken, NJ, US. p. 134–143.

  38. 38.

    Stringaris A, et al. The Affective Reactivity Index: a concise irritability scale for clinical and research settings. J Child Psychol Psychiatry. 2012;53:1109–17.

  39. 39.

    Mulraney MA, Melvin GA, Tonge BJ. Psychometric properties of the affective reactivity index in Australian adults and adolescents. Psychol Assess. 2014;26:148–55.

  40. 40.

    Beck AT, Steer RA, Brown GK. Beck depression inventory-II, San Antonio, Vol. 78; 1996. p. 490–8.

  41. 41.

    Birmaher B, et al. The screen for child anxiety related emotional disorders (SCARED): Scale construction and psychometric characteristics. J Am Acad Child Adolesc Psychiatry. 1997;36:545–53.

  42. 42.

    Swanson JM, et al. Categorical and dimensional definitions and evaluations of symptoms of ADHD: history of the SNAP and the SWAN Rating Scales. Int J Educ psychological Assess. 2012;10:51. p

  43. 43.

    Miller TJ, et al. Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophrenia Bull. 2003;29:703–15.

  44. 44.

    Tustison NJ, et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage. 2014;99:166–79.

  45. 45.

    Ciric R, et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage. 2017;154:174–87.

  46. 46.

    Tustison NJ, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29:1310–20.

  47. 47.

    Avants BB, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage. 2011;54:2033–44.

  48. 48.

    Klein A, et al. Evaluation of volume-based and surface-based brain image registration methods. Neuroimage. 2010;51:214–20.

  49. 49.

    Das SR, et al. Registration based cortical thickness measurement. Neuroimage. 2009;45:867–79.

  50. 50.

    Rosen AFG, et al. Quantitative assessment of structural image quality. NeuroImage. 2018;169:407–18.

  51. 51.

    Eklund A, Nichols TE, Knutsson H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci USA. 2016;113:7900–5.

  52. 52.

    Yang Z, Oja E. Linear and nonlinear projective nonnegative matrix factorization. IEEE Trans Neural Netw. 2010;21:734–49.

  53. 53.

    Boutsidis C, Gallopoulos E. Gallopoulos, E. Svd based initialization: a head start for nonnegative matrix factorization. Pattern Recognit. 2008;41:1350–62. vol. 1350–1362:1350–62

  54. 54.

    Van Essen DC, et al. An integrated software suite for surface-based analyses of cerebral cortex. J Am Med Inf Assoc. 2001;8:443–59. p

  55. 55.

    Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.

  56. 56.

    Fortin J-P, et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage. 2018;167:104–20.

  57. 57.

    Fortin J-P, et al. Harmonization of multi-site diffusion tensor imaging data. NeuroImage. 2017;161:149–70.

  58. 58.

    Yu M, et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum Brain Mapp. 2018;39:4213–27.

  59. 59.

    R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2018.

  60. 60.

    Lenroot RK, et al. Sexual dimorphism of brain developmental trajectories during childhood and adolescence. Neuroimage. 2007;36:1065–73.

  61. 61.

    Wood SN, Augustin NH. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol Model. 2002;157:157–77.

  62. 62.

    Cannon TD, et al. Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol psychiatry. 2015;77:147–57.

  63. 63.

    Dennis EL, et al. Irritability and brain volume in adolescents: cross-sectional and longitudinal associations. Soc Cogn Affect Neurosci 2019.

  64. 64.

    Winston JS, O’Doherty J, Dolan RJ. Common and distinct neural responses during direct and incidental processing of multiple facial emotions. NeuroImage. 2003;20:84–97.

  65. 65.

    Brotman MA, et al. Amygdala activation during emotion processing of neutral faces in children with severe mood dysregulation versus ADHD or bipolar disorder. Am J psychiatry. 2010;167:61–9.

  66. 66.

    Guyer AE, et al. Specificity of facial expression labeling deficits in childhood psychopathology. J Child Psychol Psychiatry. 2007;48:863–71.

  67. 67.

    Kircanski K, et al. A latent variable approach to differentiating neural mechanisms of irritability and anxiety in youth. JAMA Psychiatry. 2018;75:631–9.

  68. 68.

    Gold AL, et al. Comparing brain morphometry across multiple childhood psychiatric disorders. J Am Acad Child Adolesc Psychiatry. 2016;55:1027–.e3.

  69. 69.

    Mueller K, et al. Commentary: Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Front Hum Neurosci. 2017;11:345–345.

Download references

Author information

Correspondence to Theodore D. Satterthwaite.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The preliminary findings from this study were presented at the 2018 Society of Biology Psychiatry Annual Meeting.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jirsaraie, R.J., Kaczkurkin, A.N., Rush, S. et al. Accelerated cortical thinning within structural brain networks is associated with irritability in youth. Neuropsychopharmacol. 44, 2254–2262 (2019).

Download citation