Hostname: page-component-848d4c4894-x24gv Total loading time: 0 Render date: 2024-06-08T05:48:42.345Z Has data issue: false hasContentIssue false
  • English
  • Français

Associations between cortical thickness and anxious/depressive symptoms differ by the quality of early care

Published online by Cambridge University Press:  22 October 2021

Marta Korom Open the ORCID record for Marta Korom [Opens in a new window] ,
Nim Tottenham ,
Emilio A. Valadez  and
Mary Dozier Open the ORCID record for Mary Dozier [Opens in a new window]
Marta Korom*
Affiliation:
Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
Nim Tottenham
Affiliation:
Department of Psychology, Columbia University in the City of New York, New York, NY, USA
Emilio A. Valadez
Affiliation:
Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
Mary Dozier
Affiliation:
Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
*
Author for Correspondence: Marta Korom, Wolf Hall 108, 105 The Green, University of Delaware, Newark, DE 19716, USA; E-mail: mkorom@udel.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

A variety of childhood experiences can lead to anxious/depressed (A/D) symptoms. The aim of the present study was to explore the brain morphological (cortical thickness and surface area) correlates of A/D symptoms and the extent to which these phenotypes vary depending on the quality of the parenting context in which children develop. Structural magnetic resonance imaging (MRI) scans were acquired on 45 children with Child Protective Services (CPS) involvement due to risk of not receiving adequate care (high-risk group) and 25 children without CPS involvement (low-risk group) (rangeage = 8.08–12.14; Mage = 10.05) to assess cortical thickness (CT) and cortical surface area (SA). A/D symptoms were measured using the Child Behavioral Checklist. The association between A/D symptoms and CT, but not SA, differed by risk status such that high-risk children showed decreasing CT as A/D scores increased, whereas low-risk children showed increasing CT as A/D scores increased. This interaction was specific to CT in prefrontal, frontal, temporal, and parietal cortical regions. The groups had marginally different A/D scores, in the direction of higher risk being associated with lower A/D scores. Results suggest that CT correlates of A/D symptoms are differentially shaped by the quality of early caregiving experiences and should be distinguished between high- and low-risk children.

Keywords

anxious/depressive symptomscaregiving qualitycortical thicknessearly adversitypial surface area
Type
Regular Article
Information
Development and Psychopathology , Volume 35 , Issue 1 , February 2023 , pp. 73 - 84
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Achenbach, T. M., Dumenci, L., & Rescorla, L. A. (2003). DSM-oriented and empirically based approaches to constructing scales from the same item pools. Journal of Clinical Child & Adolescent Psychology, 32, 328–340.CrossRefGoogle ScholarPubMed
Achenbach, T. M., & Rescorla, L. A. (2001). Manual for the ASEBA school-Age forms & profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, & Families.Google Scholar
Afacan, O., Erem, B., Roby, D. P., Roth, N., Roth, A., Prabhu, S. P., & Warfield, S. K. (2016). Evaluation of motion and its effect on brain magnetic resonance image quality in children. Pediatric Radiology, 46, 1728–1735.CrossRefGoogle ScholarPubMed
Bas-Hoogendam, J. M., van Steenbergen, H., Tissier, R., Houwing-Duistermaat, J. J., Westenberg, P. M., & van der Wee, N. (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. doi:10.1016/j.ebiom.2018.08.048CrossRefGoogle ScholarPubMed
Beesdo, K., Knappe, S., & Pine, D. S. (2009). Anxiety and anxiety disorders in children and adolescents: Developmental issues and implications for DSM-V. Psychiatric Clinics of North America, 32, 483–524.CrossRefGoogle ScholarPubMed
Bennett, L., & Diamond, C. (1996). Chemical and anatomical plasticity of brain. 1964 [classical article]. The Journal of Neuropsychiatry and Clinical Neurosciences, 8, 459–470.Google ScholarPubMed
Bennett, E. L., Rosenzweig, M. R., Diamond, M. C., Morimoto, H., & Hebert, M. (1974). Effects of successive environments on brain measures. Physiology and Behavior, 12, 621–631.CrossRefGoogle ScholarPubMed
Biederman, J., Monuteaux, M. C., Greene, R. W., Braaten, E., Doyle, A. E., & Faraone, S. V. (2001). Long-term stability of the child behavior checklist in a clinical sample of youth with attention deficit hyperactivity disorder. Journal of Clinical Child Psychology, 30, 492–502.CrossRefGoogle Scholar
Blackmon, K., Barr, W. B., Carlson, C., Devinsky, O., DuBois, J., Pogash, D., … Thesen, T. (2011). Structural evidence for involvement of a left amygdala-orbitofrontal network in subclinical anxiety. Psychiatry Research: Neuroimaging, 194, 296–303.CrossRefGoogle ScholarPubMed
Bois, C., Ronan, L., Levita, L., Whalley, H. C., Giles, S., McIntosh, A. M., … Lawrie, S. M. (2015). Cortical surface area differentiates familial high risk individuals who go on to develop schizophrenia. Biological Psychiatry, 78, 413–420. doi:10.1016/j.biopsych.2014.12.030CrossRefGoogle ScholarPubMed
Brito, N. H., & Noble, K. G. (2014). Socioeconomic status and structural brain development. Frontiers in Neuroscience, 8, 276. doi:10.3389/fnins.2014.00276CrossRefGoogle ScholarPubMed
Busso, D. S., McLaughlin, K. A., Brueck, S., Peverill, M., Gold, A. L., & Sheridan, M. A. (2017). Child abuse, neural structure, and adolescent psychopathology: A longitudinal study. Journal of the American Academy of Child and Adolescent Psychiatry, 56(4), 321–328.e1.CrossRefGoogle ScholarPubMed
Callaghan, B. L., & Tottenham, N. (2016a). The neuro-environmental loop of plasticity: A cross-species analysis of parental effects on emotion circuitry development following typical and adverse caregiving. Neuropsychopharmacology, 41, 163–176.CrossRefGoogle ScholarPubMed
Callaghan, B. L., & Tottenham, N. (2016b). The stress acceleration hypothesis: Effects of early-life adversity on emotion circuits and behavior. Current Opinion in Behavioral Sciences, 7, 76–81.CrossRefGoogle ScholarPubMed
Chad-Friedman, E., Botdorf, M., Riggins, T., & Dougherty, L. R. (2021). Early childhood cumulative risk is associated with decreased global brain measures, cortical thickness, and cognitive functioning in school-age children. Developmental Psychobiology, 63, 192–205. doi:10.1002/dev.21956CrossRefGoogle ScholarPubMed
Choi, J., Jeong, B., Polcari, A., Rohan, M. L., & Teicher, M. H. (2012). Reduced fractional anisotropy in the visual limbic pathway of young adults witnessing domestic violence in childhood. NeuroImage, 59, 1071–1079.CrossRefGoogle ScholarPubMed
Cicchetti, D. (2016). Socioemotional, personality, and biological development: Illustrations from a multilevel developmental psychopathology perspective on child maltreatment. Annual Review of Psychology, 67, 187–211. doi: 10.1146/annurev-psych-122414-033259CrossRefGoogle ScholarPubMed
Cicchetti, D., & Curtis, W. J. (2006). The developing brain and neural plasticity: Implications for normality, psychopathology, and resilience: Developmental neurosciences. In Cicchetti, D. & Cohen, D. J. (Eds.), Developmental psychopathology: Developmental neuroscience (2nd ed., Vol. 2, pp. 1–64). Hoboken, NJ: John Wiley & Sons, Inc.Google Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600.CrossRefGoogle Scholar
Cicchetti, D., Rogosch, F. A., Gunnar, M. R., & Toth, S. L. (2010). The differential impacts of early physical and sexual abuse and internalizing problems on daytime cortisol rhythm in school-aged children. Child Development, 81, 252–269.CrossRefGoogle ScholarPubMed
Cicchetti, D., & Toth, S. L. (2015). Child maltreatment. In Lerner, R. M. & Lamb, M. E. (Eds.), Handbook of child psychology and developmental science (Vol. 3, pp. 513–563). Hoboken, NJ: John Wiley & Sons, Inc.Google Scholar
Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. NeuroImage, 9, 179–194.CrossRefGoogle ScholarPubMed
De Bellis, M. D., Keshavan, M. S., Shifflett, H., Iyengar, S., Dahl, R. E., Axelson, D. A., … Ryan, N. D. (2002). Superior temporal gyrus volumes in pediatric generalized anxiety disorder. Biological Psychiatry, 51, 553–562.CrossRefGoogle ScholarPubMed
De Brito, S. A., Viding, E., Sebastian, C. L., Kelly, P. A., Mechelli, A., Maris, H., & McCrory, E. J. (2013). Reduced orbitofrontal and temporal grey matter in a community sample of maltreated children. Journal of Child Psychology and Psychiatry and Allied Disciplines, 54, 105–112.CrossRefGoogle Scholar
Derogatis, L. R. (1975). Brief symptom inventory. Baltimore: Clinical Psychometric Research.Google Scholar
Dobbing, J., & Sands, J. (1973). Quantitative growth and development of human brain. Archives of Disease in Childhood, 48, 757–767.CrossRefGoogle ScholarPubMed
Drevets, W. C., Price, J. L., & Furey, M. L. (2008). Brain structural and functional abnormalities in mood disorders: Implications for neurocircuitry models of depression. Brain Structure and Function, 213, 93–118.CrossRefGoogle ScholarPubMed
Dubois-Comtois, K., Moss, E., Cyr, C., & Pascuzzo, K. (2013). Behavior problems in middle childhood: The predictive role of maternal distress, child attachment, and mother-child interactions. Journal of Abnormal Child Psychology, 41, 1311–1324.CrossRefGoogle ScholarPubMed
Ducharme, S., Albaugh, M. D., Hudziak, J. J., Botteron, K. N., Nguyen, T. V., Truong, C., … Karama, S. (2014). Anxious/depressed symptoms are linked to right ventromedial prefrontal cortical thickness maturation in healthy children and young adults. Cerebral Cortex, 24, 2941–2950.CrossRefGoogle ScholarPubMed
Feurer, C., Suor, J. H., Jimmy, J., Klumpp, H., Monk, C. S., Phan, K. L., & Burkhouse, K. L. (2020). Differences in cortical thinning across development among individuals with and without anxiety disorders. Depression and Anxiety, 38, 372–381. doi:10.1002/da.23096CrossRefGoogle ScholarPubMed
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97, 11050–11055.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Fischl, B., Van Der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D. H., … Dale, A. M. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 11–22.CrossRefGoogle ScholarPubMed
Frizzo, G. B., Pedrini, J. R., Souza, D. S., Bandeira, D. R., & Borsa, J. C. (2015). Reliability of child behavior checklist and teacher's report form in a sample of Brazilian children. Universitas Psychologica, 14, 149–156. doi:10.11144/Javeriana.upsy14-1.rcbcGoogle Scholar
Gee, D. G., Humphreys, K. L., Flannery, J., Goff, B., Telzer, E. H., Shapiro, M., … Tottenham, N. (2013). A developmental shift from positive to negative connectivity in human amygdala-prefrontal circuitry. Journal of Neuroscience, 33, 4584–4593.CrossRefGoogle ScholarPubMed
Gogolla, N. (2017). The insular cortex. Current Biology, 27, R580–R586. doi:10.1016/j.cub.2017.05.010CrossRefGoogle ScholarPubMed
Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., … Thompson, P. M. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences, 101, 8174–8179.CrossRefGoogle ScholarPubMed
Gold, A. L., Sheridan, M. A., Peverill, M., Busso, D. S., Lambert, H. K., Alves, S., … McLaughlin, K. A. (2016). Childhood abuse and reduced cortical thickness in brain regions involved in emotional processing. Journal of Child Psychology and Psychiatry, 57, 1154–1164.CrossRefGoogle ScholarPubMed
Gold, A. L., Steuber, E. R., White, L. K., Pacheco, J., Sachs, J. F., Pagliaccio, D., … Pine, D. S. (2017). Cortical thickness and subcortical gray matter volume in pediatric anxiety disorders. Neuropsychopharmacology, 42, 2423–2433.CrossRefGoogle ScholarPubMed
Gorka, A. X., Hanson, J. L., Radtke, S. R., & Hariri, A. R. (2014). Reduced hippocampal and medial prefrontal gray matter mediate the association between reported childhood maltreatment and trait anxiety in adulthood and predict sensitivity to future life stress. Biology of Mood and Anxiety Disorders, 4, 1–10.CrossRefGoogle ScholarPubMed
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., … Amlien, I. K. … Enhancing NeuroImaging Genetics through Meta-Analysis Consortium (ENIGMA) – Genetics working group (2020). The genetic architecture of the human cerebral cortex. Science, 367, eaay6690. doi:10.1126/science.aay6690CrossRefGoogle ScholarPubMed
Hagler, D. J., Saygin, A. P., & Sereno, M. I. (2006). Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. NeuroImage, 33, 1093–1103.CrossRefGoogle ScholarPubMed
Hanson, J. L., Adluru, N., Chung, M. K., Alexander, A. L., Davidson, R. J., & Pollak, S. D. (2013). Early neglect is associated with alterations in white matter integrity and cognitive functioning. Child Development, 84, 1566–1578.CrossRefGoogle ScholarPubMed
Hegarty, C. E., Foland-Ross, L. C., Narr, K. L., Townsend, J. D., Bookheimer, S. Y., Thompson, P. M., & Altshuler, L. L. (2012). Anterior cingulate activation relates to local cortical thickness. NeuroReport, 23, 420–424.CrossRefGoogle ScholarPubMed
Herringa, R. J., Birn, R. M., Ruttle, P. L., Burghy, C. A., Stodola, D. E., Davidson, R. J., & Essex, M. J. (2013). Childhood maltreatment is associated with altered fear circuitry and increased internalizing symptoms by late adolescence. Proceedings of the National Academy of Sciences of the United States of America, 110, 19119–19124.CrossRefGoogle ScholarPubMed
Herringa, R. J., Burghy, C. A., Stodola, D. E., Fox, M. E., Davidson, R. J., & Essex, M. J. (2016). Enhanced prefrontal-amygdala connectivity following childhood adversity as a protective mechanism against internalizing in adolescence. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 1, 326–334. doi:10.1016/j.bpsc.2016.03.003CrossRefGoogle ScholarPubMed
Herzberg, M. P., Hodel, A. S., Cowell, R. A., Hunt, R. H., Gunnar, M. R., & Thomas, K. M. (2018). Risk taking, decision-making, and brain volume in youth adopted internationally from institutional care. Neuropsychologia, 119, 262–270. doi:10.1016/j.neuropsychologia.2018.08.022CrossRefGoogle ScholarPubMed
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.2014.10.020CrossRefGoogle ScholarPubMed
Hofmann, S. G., Sawyer, A. T., Witt, A. A., & Oh, D. (2010). The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. Journal of Consulting and Clinical Psychology, 78, 169–183.CrossRefGoogle Scholar
Huang, H., Gundapuneedi, T., & Rao, U. (2012). White matter disruptions in adolescents exposed to childhood maltreatment and vulnerability to psychopathology. Neuropsychopharmacology, 37, 2693–2701.CrossRefGoogle ScholarPubMed
Hussey, J. M., Marshall, J. M., English, D. J., Knight, E. D., Lau, A. S., Dubowitz, H., & Kotch, J. B. (2005). Defining maltreatment according to substantiation: Distinction without a difference? Child Abuse & Neglect, 29, 479–492. doi:10.1016/j.chiabu.2003.12.005CrossRefGoogle ScholarPubMed
Jedd, K., Hunt, R. H., Cicchetti, D., Hunt, E., Cowell, R. A., Rogosch, F. A., … Thomas, K. M. (2015). Long-term consequences of childhood maltreatment: Altered amygdala functional connectivity. Development and Psychopathology, 27, 1577–1589.CrossRefGoogle ScholarPubMed
Jensen, S. K., Dickie, E. W., Schwartz, D. H., Evans, C. J., Dumontheil, I., Paus, T., & Barker, E. D. (2015). Effect of early adversity and childhood internalizing symptoms on brain structure in young men. JAMA Pediatrics, 169, 938–946. doi:10.1001/jamapediatrics.2015.1486CrossRefGoogle ScholarPubMed
Keiley, M. K., Howe, T. R., Dodge, K. A., Bates, J. E., & Petti, G. S. (2001). The timing of child physical maltreatment: A cross-domain growth analysis of impact on adolescent externalizing and internalizing problems. Development and Psychopathology, 13, 891–912.CrossRefGoogle ScholarPubMed
Kelly, P. A., Viding, E., Wallace, G. L., Schaer, M., De Brito, S. A., Robustelli, B., & Mccrory, E. J. (2013). Cortical thickness, surface area, and gyrification abnormalities in children exposed to maltreatment: Neural markers of vulnerability? Biological Psychiatry, 74, 845–852.CrossRefGoogle ScholarPubMed
Kendall, P. C. (2012). Anxiety disorders in youth. In Kendall, P. C. (Ed.), Child and adolescent therapy: Cognitive-behavioral procedures (pp. 143–189). New York, NY, US: Guilford Press.Google Scholar
Kim, P., Evans, G. W., Angstadt, M., Ho, S. S., Sripada, C. S., Swain, J. E., … Phan, K. L. (2013). Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood. Proceedings of the National Academy of Sciences of the United States of America, 110, 18442–18447. doi:10.1073/pnas.1308240110CrossRefGoogle ScholarPubMed
Kim, J. J., & Yoon, K. S. (1998). Stress: Metaplastic effects in the hippocampus. Trends in Neuroscience, 21, 505–509.CrossRefGoogle ScholarPubMed
Klapwijk, E. T., van de Kamp, F., van der Meulen, M., Peters, S., & Wierenga, L. M. (2019). Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data. NeuroImage, 189, 116–129.CrossRefGoogle ScholarPubMed
Kok, R., Thijssen, S., Bakermans-Kranenburg, M. J., Jaddoe, V. W., Verhulst, F. C., White, T., … Tiemeier, H. (2015). Normal variation in early parental sensitivity predicts child structural brain development. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 824–831.e1. doi:10.1016/j.jaac.2015.07.009CrossRefGoogle ScholarPubMed
Kurth, F., Zilles, K., Fox, P. T., Laird, A. R., & Eickhoff, S. B. (2010). A link between the systems: Functional differentiation and integration within the human insula revealed by meta-analysis. Brain Structure and Function, 214, 519–534. doi: 10.1007/s00429-010-0255-zCrossRefGoogle ScholarPubMed
La Buissonnière-Ariza, V., Séguin, J. R., Nassim, M., Boivin, M., Pine, D. S., Lepore, F., … Maheu, F. S. (2019). Chronic harsh parenting and anxiety associations with fear circuitry function in healthy adolescents: A preliminary study. Biological Psychology, 145, 198–210.CrossRefGoogle ScholarPubMed
Lange, N. (2012). Total and regional brain volumes in a population-based normative sample from 4 to 18 years: The NIH MRI study of normal brain development. Cerebral Cortex, 22, 1–12.Google Scholar
Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neuroscience and Biobehavioral Reviews, 30, 718–729.CrossRefGoogle ScholarPubMed
LeWinn, K. Z., Sheridan, M. A., Keyes, K. M., Hamilton, A., & McLaughlin, K. A. (2017). Sample composition alters associations between age and brain structure. Nature Communications, 8, 874.CrossRefGoogle ScholarPubMed
Li, G., Wang, L., Yap, P.-T., Wang, F., Wu, Z., Meng, Y., … Shen, D. (2019). Computational neuroanatomy of baby brains: A review. NeuroImage, 185, 906–925.CrossRefGoogle ScholarPubMed
Liberzon, I., Ma, S. T., Okada, G., Ho, S. S., Swain, J. E., & Evans, G. W. (2015). Childhood poverty and recruitment of adult emotion regulatory neurocircuitry. Social Cognitive and Affective Neuroscience, 10, 1596–1606. doi:10.1093/scan/nsv045CrossRefGoogle ScholarPubMed
Lim, L., Hart, H., Mehta, M., Worker, A., Simmons, A., Mirza, K., & Rubia, K. (2018). Grey matter volume and thickness abnormalities in young people with a history of childhood abuse. Psychological Medicine, 48(6), 1034–1046. doi:10.1017/S0033291717002392.CrossRefGoogle ScholarPubMed
Lim, L., Radua, J., & Rubia, K. (2014). Gray matter abnormalities in childhood maltreatment: A voxelwise meta analysis. American Journal of Psychiatry, 171, 854–863.CrossRefGoogle Scholar
Lupien, S. J., Leon, M. D., Santi, S. D., Convit, A., Tarshish, C., Thakur, M., … Meaney, M. J. (1998). Cortisol levels during human aging predict hippocampal atrophy and memory deficits. Nature Neuroscience, 1, 69–73.CrossRefGoogle ScholarPubMed
Marsh, R., Gerber, A. J., & Peterson, B. S. (2008). Neuroimaging studies of normal brain development and their relevance for understanding childhood neuropsychiatric disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 1233–1251.CrossRefGoogle ScholarPubMed
Masten, A. S., & Cicchetti, D. (2010). Developmental cascades. Development and Psychopathology, 22, 491–495. doi:10.1017/S0954579410000222CrossRefGoogle ScholarPubMed
Mattison, R. E., & Spitznagel, E. L. (1999). Long-term stability of child behavior checklist profile types in a child psychiatric clinic population. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 700–707. doi:10.1097/00004583-199906000-00017CrossRefGoogle Scholar
McElroy, E., Belsky, J., Carragher, N., Fearon, P., & Patalay, P. (2018). Developmental stability of general and specific factors of psychopathology from early childhood to adolescence: Dynamic mutualism or p-differentiation? Journal of Child Psychology and Psychiatry, and Allied Disciplines, 59, 667–675. doi:10.1111/jcpp.12849CrossRefGoogle ScholarPubMed
McEwen, B. S., Nasca, C., & Gray, J. D. (2016). Stress effects on neuronal structure: Hippocampus, amygdala, and prefrontal cortex. Neuropsychopharmacology, 41, 3–23.CrossRefGoogle ScholarPubMed
McLaughlin, K. A., Peverill, M., Gold, A. L., Alves, S., & Sheridan, M. A. (2015). Child maltreatment and neural systems underlying emotion regulation. Journal of the American Academy of Child and Adolescent Psychiatry, 54(9), 753–762. doi:10.1016/j.jaac.2015.06.010.CrossRefGoogle ScholarPubMed
McLaughlin, K. A., Sheridan, M. A., Winter, W., Fox, N. A., Zeanah, C. H., & Nelson, C. A. (2014). Widespread reductions in cortical thickness following severe early-life deprivation: A neurodevelopmental pathway to attention-deficit/hyperactivity disorder. Biological Psychiatry, 76, 629–638.CrossRefGoogle ScholarPubMed
McLaughlin, K. A., Weissman, D., & Bitrán, D. (2019). Childhood adversity and neural development: A systematic review. Annual Review of Developmental Psychology, 1, 277–312.CrossRefGoogle ScholarPubMed
Mehta, M. A., Golembo, N. I., Nosarti, C., Colvert, E., Mota, A., Williams, S. C. R., … Sonuga-Barke, E. J. S. (2009). Amygdala, hippocampal and corpus callosum size following severe early institutional deprivation: The English and Romanian adoptees study pilot. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 50, 943–951.CrossRefGoogle ScholarPubMed
Miller, G. A., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110, 40–48.CrossRefGoogle ScholarPubMed
Miller, D. J., Duka, T., Stimpson, C. D., Schapiro, S. J., Baze, W. B., McArthur, M. J., … Sherwood, C. C. (2012). Prolonged myelination in human neocortical evolution. Proceedings of the National Academy of Sciences, 109, 16480–16485.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Molet, J., Maras, P. M., Kinney-Lang, E., Harris, N. G., Rashid, F., Ivy, A. S., … Baram, T. Z. (2016). MRI uncovers disrupted hippocampal microstructure that underlies memory impairments after early-life adversity. Hippocampus, 26, 1618–1632.CrossRefGoogle ScholarPubMed
Nanni, V., Uher, R., & Danese, A. (2012). Childhood maltreatment predicts unfavorable course of illness and treatment outcome in depression: A meta-analysis. American Journal of Psychiatry, 169, 141–151. doi:10.1176/appi.ajp.2011.11020335CrossRefGoogle ScholarPubMed
Neil, A. L., & Christensen, H. (2009). Efficacy and effectiveness of school-based prevention and early intervention programs for anxiety. Clinical Psychology Review, 29, 208–215. doi:10.1016/j.cpr.2009.01.002CrossRefGoogle ScholarPubMed
Newman, L., Sivaratnam, C., & Komiti, A. (2015). Attachment and early brain development – neuroprotective interventions in infant–caregiver therapy. Translational Developmental Psychiatry, 3, 28647.CrossRefGoogle Scholar
Newman, E., Thompson, W. K., Bartsch, H., Hagler, D. J., Chen, C. H., Brown, T. T., … Jernigan, T. L. (2016). Anxiety is related to indices of cortical maturation in typically developing children and adolescents. Brain Structure and Function, 221, 3013–3025. doi:10.1007/s00429-015-1085-9CrossRefGoogle ScholarPubMed
Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Jernigan, T. L., Prom-Wormley, E., Neale, M., … Kremen, W. S. (2009). Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex, 19, 2728–2735. doi:10.1093/cercor/bhp026CrossRefGoogle ScholarPubMed
Paus, T., Keshavan, M., & Giedd, J. N. (2008). Why do many psychiatric disorders emerge during adolescence? Nature Reviews Neuroscience, 9, 947–957.CrossRefGoogle ScholarPubMed
Prasad, K. M., Goradia, D., Eack, S., Rajagopalan, M., Nutche, J., Magge, T., … Keshavan, M. S. (2010). Cortical surface characteristics among offspring of schizophrenia subjects. Schizophrenia Research, 116, 143–151. doi:10.1016/j.schres.2009.11.003CrossRefGoogle ScholarPubMed
Qi, H., Ning, Y., Li, J., Guo, S., Chi, M., Gao, M., … Wu, K. (2014). Gray matter volume abnormalities in depressive patients with and without anxiety disorders. Medicine, 93, e345. doi:10.1097/MD.0000000000000345CrossRefGoogle ScholarPubMed
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL https://www.R-project.org/Google Scholar
Rifkin-Graboi, A., Kong, L., Sim, L. W., Sanmugam, S., Broekman, B. F., Chen, H., … Qiu, A. (2015). Maternal sensitivity, infant limbic structure volume and functional connectivity: A preliminary study. Translational Psychiatry, 5, e668. doi:10.1038/tp.2015.133CrossRefGoogle ScholarPubMed
Rosso, I. M., Makris, N., Britton, J. C., Price, L. M., Gold, A. L., Zai, D., … Rauch, S. L. (2010). Anxiety sensitivity correlates with two indices of right anterior insula structure in specific animal phobia. Depression and Anxiety, 27, 1104–1110. doi:10.1002/da.20765CrossRefGoogle ScholarPubMed
Sapolsky, R. M. (1996). Stress, glucocorticoids, and damage to the nervous system: The current state of confusion. Stress, 1, 1–19.CrossRefGoogle Scholar
Schwartz, C. E., Kunwar, P. S., Greve, D. N., Kagan, J., Snidman, N. C., & Bloch, R. B. (2012). A phenotype of early infancy predicts reactivity of the amygdala in male adults. Molecular Psychiatry, 17, 1042–1050.CrossRefGoogle ScholarPubMed
Seligman, L. D., & Ollendick, T. H. (2011). Cognitive-behavioral therapy for anxiety disorders in youth. Child and Adolescent Psychiatric Clinics of North America, 20, 217–238. doi:10.1016/j.chc.2011.01.003CrossRefGoogle ScholarPubMed
Semple, R. J., & Lee, J. (2007). Mindfulness-based cognitive therapy for anxious children: A manual for treating childhood anxiety. Oakland, CA: New Harbinger Publications.Google Scholar
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E., Glasser, M. F., … Miller, K. L. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18, 1565–1567. doi:10.1038/nn.4125CrossRefGoogle ScholarPubMed
Stiles, J., & Jernigan, T. L. (2010). The basics of brain development. Neuropsychology Review, 20, 327–348.CrossRefGoogle ScholarPubMed
Strawn, J. R., Hamm, L., Fitzgerald, D. A., Fitzgerald, K. D., Monk, C. S., & Phan, K. L. (2015). Neurostructural abnormalities in pediatric anxiety disorders. Journal of Anxiety Disorders, 32, 81–88.CrossRefGoogle ScholarPubMed
Strawn, J. R., John, W. C., Dominick, K. C., Swartz, M. S., Wehry, A. M., Patino, L. R., … DelBello, M. P. (2014). Cortical surface anatomy in pediatric patients with generalized anxiety disorder. Journal of Anxiety Disorders, 28, 717–723.CrossRefGoogle ScholarPubMed
Strawn, J. R., Wehry, A. M., Chu, W. J., Adler, C. M., Eliassen, J. C., Cerullo, M. A., … DelBello, M. P. (2013). Neuroanatomic abnormalities in adolescents with generalized anxiety disorder: A voxel-based morphometry study. Depression and Anxiety, 30, 842–848.CrossRefGoogle ScholarPubMed
Taylor, B. K., Eastman, J. A., Frenzel, M. R., Embury, C. M., Wang, Y. P., Stephen, J. M., … Wilson, T. W. (2020). Subclinical anxiety and posttraumatic stress influence cortical thinning during adolescence. Journal of the American Academy of Child and Adolescent Psychiatry, S0890-8567(20)32216-4. Advance online publication. doi:10.1016/j.jaac.2020.11.020Google ScholarPubMed
Tierney, A. L., & Nelson, C. A. (2009). Brain development and the role of experience in the early years. Zero to Three, 30, 9–13.Google ScholarPubMed
Tozzi, L., Garczarek, L., Janowitz, D., Stein, D. J., Wittfeld, K., Dobrowolny, H., … for the ENIGMA-MDD Consortium. (2020). Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: Mega-analytic findings from a large multi-site cohort. Psychological Medicine, 50(6), 1020–1031. doi:10.1017/S003329171900093X.CrossRefGoogle ScholarPubMed
Valadez, E. A., Tottenham, N., Tabachnick, A. R., & Dozier, M. (2020). Early parenting intervention effects on brain responses to maternal cues among high-risk children. The American Journal of Psychiatry, 177, 818–826. doi:10.1176/appi.ajp.2020.20010011CrossRefGoogle ScholarPubMed
VanTieghem, M., Korom, M., Flannery, J., Choy, T., Caldera, C., Humphreys, K. L., … Tottenham, N. (2021). Longitudinal changes in amygdala, hippocampus and cortisol development following early caregiving adversity. Developmental Cognitive Neuroscience, 48, 100916. doi:10.1016/j.dcn.2021.100916CrossRefGoogle ScholarPubMed
Verhulst, F. C., & Van der Ende, J. (1995). The eight-year stability of problem behavior in an epidemiologic sample. Pediatric Research, 38, 612–617. doi:10.1203/00006450-199510000-00023CrossRefGoogle Scholar
Vidal-Pineiro, D., Parker, N., Shin, J., French, L., Jackowski, A. P., … Mowinckel, A. M. (2020). Cellular correlates of cortical thinning throughout the lifespan. Scientific Reports, 10, 21803.CrossRefGoogle ScholarPubMed
Vuoksimaa, E., Panizzon, M. S., Chen, C. H., Fiecas, M., Eyler, L. T., Fennema-Notestine, C., … Kremen, W. S. (2015). The genetic association between neocortical volume and general cognitive ability is driven by global surface area rather than thickness. Cerebral Cortex, 25, 2127–2137. doi:10.1093/cercor/bhu018CrossRefGoogle ScholarPubMed
Wiik, K. L., Loman, M. M., Van Ryzin, M. J., Armstrong, J. M., Essex, M. J., Pollak, S. D., & Gunnar, M. R. (2011). Behavioral and emotional symptoms of post-institutionalized children in middle childhood. Journal of Child Psychology and Psychiatry, 52, 56–63.CrossRefGoogle ScholarPubMed
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., Fox, P. T., … Glahn, D. C. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage, 53, 1135–1146. doi:10.1016/j.neuroimage.2009.12.028CrossRefGoogle ScholarPubMed
Winkler, A. M., Sabuncu, M. R., Yeo, B. T., Fischl, B., Greve, D. N., Kochunov, P., … Glahn, D. C. (2012). Measuring and comparing brain cortical surface area and other areal quantities. NeuroImage, 61, 1428–1443. doi:10.1016/j.neuroimage.2012.03.026CrossRefGoogle ScholarPubMed
Zhang, X., Luo, Q., Wang, S., Qiu, L., Pan, N., Kuang, W., … Gong, Q. (2020). Dissociations in cortical thickness and surface area in non-comorbid never-treated patients with social anxiety disorder. EBioMedicine, 58, 102910. doi:10.1016/j.ebiom.2020.102910CrossRefGoogle ScholarPubMed