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A person-centered approach to the assessment of early life stress: Associations with the volume of stress-sensitive brain regions in early adolescence

Published online by Cambridge University Press:  02 May 2018

Lucy S. King ,
Kathryn L. Humphreys ,
M. Catalina Camacho  and
Ian H. Gotlib
Lucy S. King*
Affiliation:
Stanford University
Kathryn L. Humphreys
Affiliation:
Stanford University
M. Catalina Camacho
Affiliation:
University of Pittsburgh
Ian H. Gotlib
Affiliation:
Stanford University
*
Address correspondence and reprint requests to: Lucy S. King, Jordan Hall, 450 Serra Mall, Building 420, Department of Psychology, Stanford University, Stanford, CA 94305; E-mail: lucyking@stanford.edu.
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Abstract

Researchers are becoming increasingly interested in linking specific forms of early life stress (ELS) to specific neurobiological markers, including alterations in the morphology of stress-sensitive brain regions. We used a person-centered, multi-informant approach to investigate the associations of specific constellations of ELS with hippocampal and amygdala volume in a community sample of 211 9- to 13-year-old early adolescents. Further, we compared this approach to a cumulative risk model of ELS, in which ELS was quantified by the total number of stressors reported. Using latent class analysis, we identified three classes of ELS (labeled typical/low, family instability, and direct victimization) that were distinguished by experiences of family instability and victimization. Adolescents in the direct victimization class had significantly smaller hippocampal volume than did adolescents in the typical/low class; ELS classes were not significantly associated with amygdala volume. The cumulative risk model of ELS had a poorer fit than did the person-centered model; moreover, cumulative ELS was not significantly associated with hippocampal or amygdala volume. Our results underscore the utility of taking a person-centered approach to identify alterations in stress-sensitive brain regions based on constellations of ELS, and suggest victimization is specifically associated with hippocampal hypotrophy observed in early adolescence.

Type
Regular Articles
Information
Development and Psychopathology , Volume 31 , Issue 2 , May 2019 , pp. 643 - 655
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

We thank Alexandria Price, Holly Pham, Isabella Lazzareschi, Monica Ellwood-Lowe, Sophie Schouboe, and Madelaine Graber for their assistance in collection and management of data; Matthew Sacchet for his assistance with subcortical brain segmentation; and Natalie Colich for helpful conversations. This research was supported by NIH Grants R01-MH101495 (to I.H.G.) and F32-MH107129 (to K.L.H.), the Brain & Behavior Research Foundation (NARSAD Young Investigator Award to KLH [23819]), the Klingenstein Third Generation Foundation (Fellowship to K.L.H.), and the National Science Foundation (Graduate Research Fellowship to L.S.K.).

References

Asparouhov, T., & Muthén, B. (2013). Auxiliary variables in mixture modeling: 3-step approaches using Mplus. Mplus Web Notes, 15, 1–48. Retrieved from http://www.statmodel.com/examples/webnotes/webnote15.pdfGoogle Scholar
Ballard, E. D., Van Eck, K., Musci, R. J., Hart, S. R., Storr, C. L., Breslau, N., & Wilcox, H. C. (2015). Latent classes of childhood trauma exposure predict the development of behavioral health outcomes in adolescence and young adulthood. Psychological Medicine, 45, 3305–3316. doi:10.1017/S0033291715001300Google Scholar
Bates, D., Machler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 1–48.Google Scholar
Calem, M., Bromis, K., McGuire, P., Morgan, C., & Kempton, M. J. (2017). Meta-analysis of associations between childhood adversity and hippocampus and amygdala volume in non-clinical and general population samples. NeuroImage: Clinical, 14, 471–479. doi:10.1016/j.nicl.2017.02.016Google Scholar
Child Welfare Information Gateway. (2017). Child maltreatment 2015: Summary of key findings. Washington, DC: Author. Retrieved from https://www.childwelfare.gov/pubs/factsheets/canstats/Google Scholar
Chisholm, J. S., Quinlivan, J. A., Petersen, R. W., & Coall, D. A. (2005). Early stress predicts age at menarche and first birth, adult attachment, and expected lifespan. Human Nature, 16, 233–265. doi:10.1007/s12110-005-1009-0Google Scholar
Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Hoboken, NJ: Wiley.Google Scholar
Dong, M., Anda, R., & Felitti, V. (2004). The interrelatedness of multiple forms of childhood abuse, neglect, and household dysfunction. Child Abuse & Neglect, 28, 771–784. doi:10.1016/j.chiabu.2004.01.008Google Scholar
Dorn, L. D., & Biro, F. M. (2011). Puberty and its measurement: A decade in review. Journal of Research on Adolescence, 21, 180–195. doi:10.1111/j.1532-7795.2010.00722.xGoogle Scholar
Dunn, V. J., Abbott, R. A., Croudace, T. J., Wilkinson, P., Jones, P. B., Herbert, J., & Goodyer, I. M. (2011). Profiles of family-focused adverse experiences through childhood and early adolescence: The ROOTS project, a community investigation of adolescent mental health. BMC Psychiatry, 11, 109. doi:10.1186/1471-244X-11-109Google Scholar
Dziak, J. J., Lanza, S. T., & Tan, X. (2014). Effect size, statistical power and sample size requirements for the bootstrap likelihood ratio test in latent class analysis. Structural Equation Modeling, 21, 534–552. doi:10.1080/10705511.2014.919819Google Scholar
Edminston, E., Wang, F., Mazure, C., Guiney, J., Sinha, R., Mayes, L., & Blumberg, H. (2011). Corticostriatal-limbic gray matter morphology in adolescents with self-reported exposure to childhood maltreatment. Archives of Pediatrics and Adolescent Medicine, 165, 1069–1077. doi:10.1001/archpediatrics.2011.565Google Scholar
Eiland, L., Ramroop, J., Hill, M. N., Manley, J., & McEwen, B. S. (2012). Chronic juvenile stress produces corticolimbic dendritic architectural remodeling and modulates emotional behavior in male and female rats. Psychoneuroendocrinology, 37, 39–47. doi:10.1016/j.psyneuen.2011.04.015Google Scholar
Ellwood-Lowe, M. E., Sacchet, M. D., & Gotlib, I. H. (2016). The application of neuroimaging to social inequity and language disparity: A cautionary examination. Developmental Cognitive Neuroscience, 22, 1–8. doi:10.1016/j.dcn.2016.10.001Google Scholar
Evans, G. W., Li, D., & Whipple, S. S. (2013). Cumulative risk and child development. Psychological Bulletin, 139, 1342–1396. doi:10.1037/a0031808Google Scholar
Farah, M. J. (2017). The neuroscience of socioeconomic status: Correlates, causes, and consequences. Neuron, 96, 56–71. doi:10.1016/j.neuron.2017.08.034Google Scholar
Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz, A. M., Edwards, V., … Marks, J. S. (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14, 245–258. doi:10.1016/S0749-3797(98)00017-8Google Scholar
Finkelhor, D., Turner, H. A., Shattuck, A., & Hamby, S. L. (2013). Violence, abuse, and crime exposure in a national sample of children and youth. JAMA Pediatrics, 167, 614–621. doi:10.1542/peds.2009-0467Google Scholar
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., … Dale, A. M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33, 341–355. doi:10.1016/S0896-6273(02)00569-XGoogle Scholar
Ghosh, S. S., Kakunoori, S., Augustinack, J., Nieto-Castanon, A., Kovelman, I., Gaab, N., … Fischl, B. (2010). Evaluating the validity of volume-based and surface-based brain image registration for developmental cognitive neuroscience studies in children 4 to 11 years of age. NeuroImage, 53, 85–93. doi:10.1016/j.neuroimage.2010.05.075Google Scholar
Ghosh-Ippen, C., Ford, J., Racusin, R., Acker, M., Bosquet, K., Rogers, C., & Edwards, J. (2002). Trauma Events Screening Inventory—Parent Report Revised. Washington, DC: US Department of Verterans Affairs, National Center for PTSD.Google Scholar
Gogtay, N., Nugent, T. F., Herman, D. H., Ordonez, A., Greenstein, D., Hayashi, K. M., … Rapoport, J. L. (2006). Dynamic mapping of normal human hippocampal development. Hippocampus, 16, 664–672.Google Scholar
Green, J. G., Mclaughlin, K. A., Berglund, P. A., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M., & Kessler, R. C. (2010). Childhood adversities and adult psychiatric disorders in the National Comorbidity Survey Replication I. Archives of General Psychiatry, 67, 113–123. doi:10.1001/archgenpsychiatry.2009.187Google Scholar
Hagan, M. J., Sulik, M. J., & Lieberman, A. F. (2016). Traumatic life events and psychopathology in a high risk, ethnically diverse sample of young children: A person-centered approach. Journal of Abnormal Child Psychology, 44. doi:10.1007/s10802-015-0078-8Google Scholar
Hanson, J. L., Chandra, A., Wolfe, B. L., & Pollak, S. D. (2011). Association between income and the hippocampus. PLOS ONE, 6. doi:10.1371/journal.pone.0018712Google Scholar
Hanson, J. L., Nacewicz, B. M., Sutterer, M. J., Cayo, A., Schaefer, S. M., Rudolph, K., … Davidson, R. (2015). Behavior problems after early life stress: Contributions of the hippocampus and amygdala. Biological Psychiatry, 77, 314–323.Google Scholar
Hodel, A. S., Hunt, R. H., Cowell, R. A., van den Heuvel, S. E., Gunnar, M. R., & Thomas, K. M. (2015). Duration of early adversity and structural brain development in post-institutionalized adolescents. NeuroImage, 105, 112–119. doi:10.1016/j.neuroimage.2014.10.020Google Scholar
Humphreys, K. L., Kircanski, K., Colich, N. L., & Gotlib, I. H. (2016). Attentional avoidance of fearful facial expressions following early life stress is associated with impaired social functioning. Journal of Child Psychology and Psychiatry, 57, 1174–1182. doi:10.1111/jcpp.12607Google Scholar
Humphreys, K. L., & Zeanah, C. H. (2015). Deviations from the expectable environment in early childhood and emerging psychopathology. Neuropsychopharmacology, 40, 154–170. doi:10.1038/npp.2014.165Google Scholar
King, L. S., Colich, N. L., LeMoult, J., Humphreys, K. L., Ordaz, S. J., Price, A. N., & Gotlib, I. H. (2017). The impact of the severity of early life stress on diurnal cortisol: The role of puberty. Psychoneuroendocrinology, 77, 68–74. doi:10.1016/j.psyneuen.2016.11.024Google Scholar
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2016). Tests in linear mixed effects models. Journal of Stastical Software, 82, 1–26. doi:10.18637/jss.v082.i13Google Scholar
Lanza, S. T., & Cooper, B. R. (2016). Latent class analysis for developmental research. Child Development Perspectives, 10, 59–64. doi:10.1111/cdep.12163Google Scholar
Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14, 157–168. doi:10.1007/s11121-011-0201-1.LatentGoogle Scholar
Luby, J. L., Belden, A., Botteron, K., Marrus, N., Harms, M. P., Babb, C., … Barch, D. (2013). The effects of poverty on childhood brain development: The mediating effect of caregiving and stressful life events. JAMA Pediatrics, 167, 1135–1142. doi:10.1001/jamapediatrics.2013.3139Google Scholar
Luby, J. L., Belden, A., Harms, M. P., Tillman, R., & Barch, D. M. (2016). Preschool is a sensitive period for the influence of maternal support on the trajectory of hippocampal development. Proceedings of the National Academy of Sciences, 113, 1542–1547. doi:10.1073/pnas.1601443113Google Scholar
Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10, 434–445. doi:10.1038/nrn2639Google Scholar
Lupien, S. J., Parent, S., Evans, A. C., Tremblay, R. E., Zelazo, P. D., Corbo, V., … Seguin, J. R. (2011). Larger amygdala but no change in hippocampal volume in 10-year-old children exposed to maternal depressive symptomatology since birth. Proceedings of the National Academy of Sciences, 108, 14324–14329. doi:10.1073/pnas.1105371108Google Scholar
McChesney, G. C., Adamson, G., & Shevlin, M. (2015). A latent class analysis of trauma based on a nationally representative sample of US adolescents. Social Psychiatry and Psychiatric Epidemiology, 50, 1207–1217. doi:10.1016/j.psychres.2009.11.010Google Scholar
McEwen, B. S., Eiland, L., Hunter, R. G., & Miller, M. M. (2012). Stress and anxiety: Structural plasticity and epigenetic regulation as a consequence of stress. Neuropharmacology, 62, 3–12. doi:10.1016/j.neuropharm.2011.07.014Google Scholar
McLaughlin, K. A., & Sheridan, M. A. (2016). Beyond cumulative risk: A dimensional approach to childhood adversity. Current Directions in Psychological Science, 25, 239–245. doi:10.1177/0963721416655883Google Scholar
Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110, 40–48. doi:10.1037/0021-843X.110.1.40Google Scholar
Mills, K. L., Goddings, A.-L., Herting, M. M., Meuwese, R., Blakemore, S.-J., Crone, E. A., … Tamnes, C. K. (2016). Structural brain development between childhood and adulthood: Convergence across four longitudinal samples. NeuroImage, 141, 273–281. doi:10.1016/j.neuroimage.2016.07.044Google Scholar
Morris, N. M., & Udry, J. R. (1980). Validation of a self-administered instrument to assess stage of adolescent development. Journal of Youth and Adolescence, 9, 271–280. doi:10.1007/BF02088471Google Scholar
Muthén, L. K., & Muthén, B. O. (2012). Mplus user's guide (7th ed.). Los Angeles: Author.Google Scholar
Noble, K., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., … Sowell, E. R. (2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18, 773. doi:10.1038/nn.3983Google Scholar
Noble, K., Houston, S. M., Kan, E., & Sowell, E. R. (2012). Neural correlates of socioeconomic status in the developing human brain. Developmental Science, 15, 516–527. doi:10.1111/j.1467-7687.2012.01147.xGoogle Scholar
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A monte carlo simulation study. Structural Equation Modeling, 14, 535–569. doi:10.1080/10705510701575396Google Scholar
Peper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S., & Hulshoff Pol, H. E. (2007). Genetic influences on human brain structure: A review of brain imaging studies in twins. Human Brain Mapping, 28, 464–473. doi:10.1002/hbm.20398Google Scholar
Plomin, R., DeFries, J. C., & Loehlin, J. C. (1977). Genotype-environment interaction and correlation in the analysis of human behavior. Psychological Bulletin, 84, 309–322.Google Scholar
Rao, U., Chen, L. A., Bidesi, A. S., Shad, M. U., Thomas, M. A., & Hammen, C. L. (2010). Hippocampal changes associated with early-life adversity and vulnerability to depression. Biological Psychiatry, 67, 357–364. doi:10.1016/j.biopsych.2009.10.017Google Scholar
R Core Team. (2015). R: A language and environment for statistical computing. Vienna, Ausria: R Foundation for Statistical Computing.Google Scholar
Reuben, A., Moffitt, T. E., Caspi, A., Belsky, D. W., Harrington, H., Schroeder, F., … Danese, A. (2016). Lest we forget: Comparing retrospective and prospective assessments of adverse childhood experiences in the prediction of adult health. Journal of Child Psychology and Psychiatry, 57, 1103–1112.Google Scholar
Ribbe, D. (1996). Psychometric review of Traumatic Event Screening Instrument for Children (TESI-C). In Stamm, B. H. (Ed.), Measurement of stress, trauma, and adaptation (pp. 386–387). Lutherville, MD: Sidran Press.Google Scholar
Rutter, M. (2007). Proceeding from observed correlation to causal inference: The use of natural experiments. Perspectives on Psychological Science, 2, 377–395. doi:10.1111/j.1745-6916.2007.00050.xGoogle Scholar
Sheridan, M. A., Fox, N. A., Zeanah, C. H., McLaughlin, K. A., & Nelson, C. A. (2012). Variation in neural development as a result of exposure to institutionalization early in childhood. Proceedings of the National Academy of Sciences of the USA, 109, 12927–12932. doi:10.1073/pnas.1200041109Google Scholar
Sheridan, M. A., & McLaughlin, K. A. (2014). Dimensions of early experience and neural development: Deprivation and threat. Trends in Cognitive Sciences, 18, 580–585. doi:10.1016/j.tics.2014.09.001Google Scholar
Somerville, L. H. (2016). Searching for signatures of brain maturity: What are we searching for? Neuron, 92, 1164–1167. doi:10.1016/j.neuron.2016.10.059Google Scholar
St. Clair, M. C., Croudace, T., Dunn, V. J., Jones, P. B., Herbert, J., & Goodyer, I. M. (2015). Childhood adversity subtypes and depressive symptoms in early and late adolescence. Development and Psychopathology, 27, 885–899. doi:10.1017/S0954579414000625Google Scholar
Teicher, M. H., & Samson, J. A. (2016). Annual Research Review: Enduring neurobiological effects of childhood abuse and neglect. Journal of Child Psychology and Psychiatry, 57, 241–266. doi:10.1111/jcpp.12507Google Scholar
Tottenham, N., Hare, T. A., Quinn, B. T., McCarry, T. W., Nurse, M., Gilhooly, T., … Casey, B. J. (2010). Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation. Developmental Science, 13, 46–61. doi:10.1111/j.1467-7687.2009.00852.xGoogle Scholar
Tottenham, N., & Sheridan, M. A. (2010). A review of adversity, the amygdala and the hippocampus: A consideration of developmental timing. Frontiers in Human Neuroscience, 3, 68. doi:10.3389/neuro.09.068.2009Google Scholar
Uematsu, A., Matsui, M., Tanaka, C., Takahashi, T., Noguchi, K., Suzuki, M., & Nishijo, H. (2012). Developmental trajectories of amygdala and hippocampus from infancy to early adulthood in healthy individuals. PLOS ONE, 7, 1–10. doi:10.1371/journal.pone.0046970Google Scholar
Vachon, D. D., Krueger, R. F., Rogosch, F. A., & Cicchetti, D. (2015). Assessment of the harmful psychiatric and behavioral effects of different forms of child maltreatment. JAMA Psychiatry, 72, 1135–1142. doi:10.1001/jamapsychiatry.2015.1792Google Scholar
von Eye, A. (2010). Developing the person-oriented approach: Theory and methods of analysis. Development and Psychopathology, 22, 277. doi:10.1017/S0954579410000052Google Scholar
Vyas, A., Jadhav, S., & Chattarji, S. (2006). Prolonged behavioral stress enhances synaptic connectivity in the basolateral amygdala. Neuroscience, 143, 387–393. doi:10.1016/j.neuroscience.2006.08.003Google Scholar
Vyas, A., Mitra, R., Shankaranarayana Rao, B. S., & Chattarji, S. (2002). Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons. Neuroscience, 22, 6810–6818.Google Scholar
Yu, Q., Daugherty, A. M., Anderson, D. M., Nishimura, M., Brush, D., Hardwick, A., … Ofen, N. (2017). Socioeconomic status and hippocampal volume in children and young adults. Developmental Science. Advance online publication. doi:10.1111/desc.12561Google Scholar
Zeanah, C. H., & Sonuga-Barke, E. J. S. (2016). Editorial: The effects of early trauma and deprivation on human development—From measuring cumulative risk to characterizing specific mechanisms. Journal of Child Psychology and Psychiatry, 57, 1099–1102. doi:10.1111/jcpp.12642Google Scholar