[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Download PDF
Figure 1.
Association Between Individually Estimated Slopes Over Time for Global Volume of Cortical Gray Matter as a Function of P-SA-MDD Severity Score
Association Between Individually Estimated Slopes Over Time for Global Volume of Cortical Gray Matter as a Function of P-SA-MDD Severity Score

Individual slopes were significantly correlated with P-SA-MDD severity score (–0.156; P = .03). MLM indicates multilevel linear model; P-SA-MDD, depression symptom number score that spanned the preschool-age through school-age period.

Figure 2.
Association Between Individually Estimated Slopes Over Time for Global Thickness of Cortical Gray Matter as a Function of P-SA-MDD Severity Score
Association Between Individually Estimated Slopes Over Time for Global Thickness of Cortical Gray Matter as a Function of P-SA-MDD Severity Score

Individual slopes were significantly correlated with P-SA-MDD severity score (–0.189; P = .008). MLM indicates multilevel linear model; P-SA-MDD, depression symptom number score that spanned the preschool-age through school-age period.

Table 1.  
Characteristics of the Samplea
Characteristics of the Samplea
Table 2.  
Multilevel Mixed Models of Global Volume, Thickness, and Surface Area of Cortical Gray Matter
Multilevel Mixed Models of Global Volume, Thickness, and Surface Area of Cortical Gray Matter
1.
Giedd  JN, Raznahan  A, Alexander-Bloch  A, Schmitt  E, Gogtay  N, Rapoport  JL.  Child psychiatry branch of the National Institute of Mental Health longitudinal structural magnetic resonance imaging study of human brain development. Neuropsychopharmacology.2015;40(1):43-49.PubMedArticle
2.
Gilmore  JH, Schmitt  JE, Knickmeyer  RC,  et al.  Genetic and environmental contributions to neonatal brain structure: a twin study. Hum Brain Mapp. 2010;31(8):1174-1182.PubMed
3.
Giedd  JN, Rapoport  JL.  Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron. 2010;67(5):728-734.PubMedArticle
4.
Sowell  ER, Levitt  J, Thompson  PM,  et al.  Brain abnormalities in early-onset schizophrenia spectrum disorder observed with statistical parametric mapping of structural magnetic resonance images. Am J Psychiatry. 2000;157(9):1475-1484.PubMedArticle
5.
Paus  T.  Mapping brain maturation and cognitive development during adolescence. Trends Cogn Sci. 2005;9(2):60-68.PubMedArticle
6.
Lenroot  RK, Schmitt  JE, Ordaz  SJ,  et al.  Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Hum Brain Mapp. 2009;30(1):163-174.PubMedArticle
7.
Frangou  S, Chitins  X, Williams  SC.  Mapping IQ and gray matter density in healthy young people. Neuroimage. 2004;23(3):800-805.PubMedArticle
8.
van Eijndhoven  P, van Wingen  G, Katzenbauer  M,  et al.  Paralimbic cortical thickness in first-episode depression: evidence for trait-related differences in mood regulation. Am J Psychiatry. 2013;170(12):1477-1486.PubMedArticle
9.
Peterson  BS, Warner  V, Bansal  R,  et al.  Cortical thinning in persons at increased familial risk for major depression. Proc Natl Acad Sci U S A. 2009;106(15):6273-6278.PubMedArticle
10.
Gogtay  N, Rapoport  JL.  Childhood-onset schizophrenia: insights from neuroimaging studies. J Am Acad Child Adolesc Psychiatry. 2008;47(10):1120-1124.PubMedArticle
11.
Paus  T, Keshavan  M, Giedd  JN.  Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci. 2008;9(12):947-957.PubMed
12.
Zatorre  RJ, Fields  RD, Johansen-Berg  H.  Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15(4):528-536.PubMedArticle
13.
Gogtay  N, Thompson  PM.  Mapping gray matter development: implications for typical development and vulnerability to psychopathology. Brain Cogn. 2010;72(1):6-15.PubMedArticle
14.
Shaw  P, Eckstrand  K, Sharp  W,  et al.  Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci U S A. 2007;104(49):19649-19654.PubMedArticle
15.
Greven  CU, Bralten  J, Mennes  M,  et al.  Developmentally stable whole-brain volume reductions and developmentally sensitive caudate and putamen volume alterations in those with attention-deficit/hyperactivity disorder and their unaffected siblings. JAMA Psychiatry. 2015;72(5):490-499.PubMedArticle
16.
Gogtay  N.  Cortical brain development in schizophrenia: insights from neuroimaging studies in childhood-onset schizophrenia. Schizophr Bull. 2008;34(1):30-36.PubMedArticle
17.
Rapoport  JL, Inoff-Germain  G.  Update on childhood-onset schizophrenia. Curr Psychiatry Rep. 2000;2(5):410-415.PubMedArticle
18.
Bora  E, Fornito  A, Pantelis  C, Yücel  M.  Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies. J Affect Disord. 2012;138(1-2):9-18.PubMedArticle
19.
Grieve  SM, Korgaonkar  MS, Koslow  SH, Gordon  E, Williams  LM.  Widespread reductions in gray matter volume in depression. Neuroimage Clin. 2013;3:332-339.PubMedArticle
20.
Gibb  B. Depression in childhood. In: Gotlib  I, Hammen  CL, ed. Handbook of Depression.3rd ed. New York, NY: Guilford Publications; 2014.
21.
Whittle  S, Lichter  R, Dennison  M,  et al.  Structural brain development and depression onset during adolescence: a prospective longitudinal study. Am J Psychiatry. 2014;171(5):564-571.PubMedArticle
22.
Belden  AC, Barch  DM, Oakberg  TJ,  et al.  Anterior insula volume and guilt: neurobehavioral markers of recurrence after early childhood major depressive disorder. JAMA Psychiatry. 2015;72(1):40-48.PubMedArticle
23.
Barch  DM, Gaffrey  MS, Botteron  KN, Belden  AC, Luby  JL.  Functional brain activation to emotionally valenced faces in school-aged children with a history of preschool-onset major depression. Biol Psychiatry. 2012;72(12):1035-1042.PubMedArticle
24.
Gaffrey  MS, Barch  DM, Singer  J, Shenoy  R, Luby  JL.  Disrupted amygdala reactivity in depressed 4- to 6-year-old children. J Am Acad Child Adolesc Psychiatry. 2013;52(7):737-746.PubMedArticle
25.
Marrus  N, Belden  A, Nishino  T,  et al.  Ventromedial prefrontal cortex thinning in preschool-onset depression. J Affect Disord. 2015;180:79-86.PubMedArticle
26.
Luby  JL, Gaffrey  MS, Tillman  R, April  LM, Belden  AC.  Trajectories of preschool disorders to full DSM depression at school age and early adolescence: continuity of preschool depression. Am J Psychiatry. 2014;171(7):768-776.PubMedArticle
27.
Amlien  IK, Fjell  AM, Tamnes  CK,  et al.  Organizing principles of human cortical development—thickness and area from 4 to 30 years: insights from comparative primate neuroanatomy [published online September 21, 2014]. Cereb Cortex. doi:10.1093/cercor/bhu214.
28.
Cai  DC, Fonteijn  H, Guadalupe  T,  et al.  A genome-wide search for quantitative trait loci affecting the cortical surface area and thickness of Heschl’s gyrus. Genes Brain Behav. 2014;13(7):675-685.PubMedArticle
29.
McKay  DR, Knowles  EE, Winkler  AA,  et al.  Influence of age, sex and genetic factors on the human brain. Brain Imaging Behav. 2014;8(2):143-152.PubMedArticle
30.
Sowell  ER, Thompson  PM, Holmes  CJ, Jernigan  TL, Toga  AW.  In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nat Neurosci. 1999;2(10):859-861.PubMedArticle
31.
Sowell  ER, Thompson  PM, Tessner  KD, Toga  AW.  Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. J Neurosci. 2001;21(22):8819-8829.PubMed
32.
Egger  HL, Ascher  AA. Preschool Age Psychiatric Assessment (PAPA). Durham, NC: Duke University Medical Center; 1999.
33.
Angold  A, Costello  EJ.  The Child and Adolescent Psychiatric Assessment (CAPA). J Am Acad Child Adolesc Psychiatry. 2000;39(1):39-48.PubMedArticle
34.
Luby  JL, Si  X, Belden  AC, Tandon  M, Spitznagel  E.  Preschool depression: homotypic continuity and course over 24 months. Arch Gen Psychiatry. 2009;66(8):897-905.PubMedArticle
35.
Luby  JL, Belden  AC, Pautsch  J, Si  X, Spitznagel  E.  The clinical significance of preschool depression: impairment in functioning and clinical markers of the disorder. J Affect Disord. 2009;112(1-3):111-119.PubMedArticle
36.
Maxwell  ME. Manual for the Family Interview for Genetic Studies (FIGS). Bethesda, MD: Clinical Neurogenetics Branch, Intramural Research Program, National Insititute of Mental Health; 1992.
37.
Reuter  M, Rosas  HD, Fischl  B.  Highly accurate inverse consistent registration: a robust approach. Neuroimage. 2010;53(4):1181-1196.PubMedArticle
38.
Reuter  M, Schmansky  NJ, Rosas  HD, Fischl  B.  Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012;61(4):1402-1418.PubMedArticle
39.
Kenward  MG, Roger  JH.  Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 1997;53(3):983-997.PubMedArticle
40.
Shaw  P, Greenstein  D, Lerch  J,  et al.  Intellectual ability and cortical development in children and adolescents. Nature. 2006;440(7084):676-679.PubMedArticle
41.
Bruder  GE, Quitkin  FM, Stewart  JW, Martin  C, Voglmaier  MM, Harrison  WM.  Cerebral laterality and depression: differences in perceptual asymmetry among diagnostic subtypes. J Abnorm Psychol. 1989;98(2):177-186.PubMedArticle
42.
Moratti  S, Rubio  G, Campo  P, Keil  A, Ortiz  T.  Hypofunction of right temporoparietal cortex during emotional arousal in depression. Arch Gen Psychiatry. 2008;65(5):532-541.PubMedArticle
43.
Price  JL, Drevets  WC.  Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn Sci. 2012;16(1):61-71.PubMedArticle
44.
Drevets  WC, Savitz  J, Trimble  M.  The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 2008;13(8):663-681.PubMed
45.
Ducharme  S, Albaugh  MD, Hudziak  JJ,  et al.  Anxious/depressed symptoms are linked to right ventromedial prefrontal cortical thickness maturation in healthy children and young adults. Cereb Cortex.2014;24(11):2941-2950.PubMedArticle
46.
MacMaster  FP, Carrey  N, Langevin  LM, Jaworska  N, Crawford  S.  Disorder-specific volumetric brain difference in adolescent major depressive disorder and bipolar depression. Brain Imaging Behav. 2014;8(1):119-127.PubMedArticle
47.
Singh  MK, Kesler  SR, Hadi Hosseini  SM,  et al.  Anomalous gray matter structural networks in major depressive disorder. Biol Psychiatry. 2013;74(10):777-785.PubMedArticle
48.
Zarei  M, Ibarretxe-Bilbao  N, Compta  Y,  et al.  Cortical thinning is associated with disease stages and dementia in Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2013;84(8):875-881.PubMedArticle
49.
Fan  Q, Palaniyappan  L, Tan  L,  et al.  Surface anatomical profile of the cerebral cortex in obsessive-compulsive disorder: a study of cortical thickness, folding and surface area. Psychol Med. 2013;43(5):1081-1091.PubMedArticle
50.
Botteron  KN, Raichle  ME, Drevets  WC, Heath  AC, Todd  RD.  Volumetric reduction in left subgenual prefrontal cortex in early onset depression. Biol Psychiatry. 2002;51(4):342-344.PubMedArticle
51.
Goodman  M, Hazlett  EA, Avedon  JB, Siever  DR, Chu  KW, New  AS.  Anterior cingulate volume reduction in adolescents with borderline personality disorder and co-morbid major depression. J Psychiatr Res. 2011;45(6):803-807.PubMedArticle
52.
Luby  JL, Heffelfinger  A, Koenig-McNaught  AL, Brown  K, Spitznagel  E.  The Preschool Feelings Checklist: a brief and sensitive screening measure for depression in young children. J Am Acad Child Adolesc Psychiatry. 2004;43(6):708-717.PubMedArticle
Original Investigation
January 2016

Early Childhood Depression and Alterations in the Trajectory of Gray Matter Maturation in Middle Childhood and Early Adolescence

Author Affiliations
  • 1Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri
  • 2Department of Psychology, Washington University, St Louis, Missouri
  • 3Department of Radiology, Washington University School of Medicine, St Louis, Missouri
  • 4The Program in Neuroscience, Washington University School of Medicine, St Louis, Missouri
JAMA Psychiatry. 2016;73(1):31-38. doi:10.1001/jamapsychiatry.2015.2356
Abstract

Importance  The trajectory of cortical gray matter development in childhood has been characterized by early neurogenesis and volume increase, peaking at puberty followed by selective elimination and myelination, resulting in volume loss and thinning. This inverted U-shaped trajectory, as well as cortical thickness, has been associated with cognitive and emotional function. Synaptic pruning–based volume decline has been related to experience-dependent plasticity in animals. To date, there have been no data to inform whether and how childhood depression might be associated with this trajectory.

Objective  To examine the effects of early childhood depression, from the preschool age to the school age period, on cortical gray matter development measured across 3 waves of neuroimaging from late school age to early adolescence.

Design, Setting, and Participants  Data were collected in an academic research setting from September 22, 2003, to December 13, 2014, on 193 children aged 3 to 6 years from the St Louis, Missouri, metropolitan area who were observed for up to 11 years in a longitudinal behavioral and neuroimaging study of childhood depression. Multilevel modeling was applied to explore the association between the number of childhood depression symptoms and prior diagnosis of major depressive disorder and the trajectory of gray matter change across 3 scan waves. Data analysis was conducted from October 29, 2014, to September 28, 2015.

Main Outcomes and Measures  Volume, thickness, and surface area of cortical gray matter measured using structural magnetic resonance imaging at 3 scan waves.

Results  Of the 193 children, 90 had a diagnosis of major depressive disorder; 116 children had 3 full waves of neuroimaging scans. Findings demonstrated marked alterations in cortical gray matter volume loss (slope estimate, −0.93 cm3; 95% CI, −1.75 to −0.10 cm3 per scan wave) and thinning (slope estimate, −0.0044 mm; 95% CI, −0.0077 to −0.0012 mm per scan wave) associated with experiencing an episode of major depressive disorder before the first magnetic resonance imaging scan. In contrast, no significant associations were found between development of gray matter and family history of depression or experiences of traumatic or stressful life events during this period.

Conclusions and Relevance  This study demonstrates an association between early childhood depression and the trajectory of cortical gray matter development in late school age and early adolescence. These findings underscore the significance of early childhood depression on alterations in neural development.

Introduction

Longitudinal studies of childhood structural brain development using magnetic resonance imaging in healthy children have begun to map the normative pattern of development of gray matter from school age through adolescence.1 Although development of gray matter begins in utero, there has been much interest in its trajectory during the school-age and early adolescent period.2 The specific cellular processes that underlie gray matter change during this period in humans remain to be elucidated. In contrast with white matter, which shows linear increases in volume, findings suggest a pattern of rapid neurogenesis and related increases in gray matter volume during early childhood, peaking in early puberty followed by a process of selective elimination and myelination, resulting in volume loss and thinning.35 The inverted U-shaped trajectory of the development of cortical and subcortical gray matter regions is influenced by sex, pubertal status, IQ, and genetic and psychosocial factors.6,7 The characteristics of this inverted U-shaped trajectory for gray matter have been associated with function across several domains.5 More important, associations between cortical thickness, mood regulation, and executive functioning have been demonstrated in cross-sectional analyses.8,9 There is some evidence that this synaptic pruning–based volume decline is associated with experience-dependent plasticity.1012 The notion that the rate of decline of cortical gray matter in humans could vary based on history of experience has powerful public health implications.

Building on advances in mapping the normative trajectory of gray matter development, there has been escalating interest in whether symptoms of childhood psychiatric disorders and/or genetic predisposition to psychiatric disorders alter this trajectory. As many major psychiatric disorders are thought to be neurodevelopmental, investigations of whether and how childhood mood and emotional functioning might alter this trajectory is of great interest and represents a critical gap in the literature on developmental neuroscience. Alterations in maturation of gray matter have been detected in some childhood psychiatric disorders.1315 A pattern of loss of cortical gray matter during adolescence, consistent with the normative process but at an accelerated rate, has been observed in children with schizophrenia and bipolar disorder.16,17 This pattern of accelerated loss of gray matter may be interpreted as reflecting synaptic pruning taking place earlier in development, before being fully informed by the effects of broader experience.

Numerous cross-sectional neuroimaging studies report decreased cortical gray matter in depressed adults compared with healthy adults.18 These findings have been interpreted as a pattern of premature brain aging.19 However, to date, there have been very few data on longitudinal imaging to inform whether childhood depression is also associated with alterations in development of gray matter. Given the known cognitive and emotional impairments in children with depression, accelerated reductions in cortical gray matter development are hypothesized.20

In one of the only available longitudinal imaging studies in which adolescents at high and low risk for depression underwent neuroimaging twice, volumetric changes in the hippocampus, putamen, and amygdala were found, but no changes in cortical gray matter were reported.21 In contrast, in a cross-sectional study of the 6- to 54-year-old offspring of depressed parents, cortical gray matter thinning across the lateral surface of the right hemisphere was reported in high-risk participants vs controls and was associated with current symptom severity.9 The lack of longitudinal imaging data in child samples represents a critical gap in the literature given the rapid changes in cortical gray matter during this developmental period.

This gap is important given that depression has been shown to have an onset as early as the preschool period, with validity evidenced by numerous neurobehavioral markers, including alterations in the function and structure of key brain regions involved in emotion processing.2225 More important, depression that begins at preschool age exhibits continuity with depression in later childhood that meets DSM-5 criteria, suggesting that it is an early manifestation of the well-validated childhood and adolescent disorder.26 Our current investigation uses data from an ongoing 11-year longitudinal study of early childhood depression to examine the effects of childhood depression on the trajectory of cortical gray matter development, accounting for sex, age, IQ, poverty, family history of depression, and adverse experiences. Cortical gray matter volume was the primary variable because it is the most global metric. Significant volume findings were followed up by separate analyses of thickness and surface area, as they can have differential developmental trajectories, genetic influences, and patterns of impairment across neuropsychiatric conditions.2729

In addition to behavioral data across 8 annual (and numerous semiannual) waves, 3 waves of neuroimaging during school age and early adolescence were obtained. Neuroimaging was initiated in this sample (first scan) just past the age of peak cortical growth for most children and maps the downward slope of the inverted U-shaped curve during pruning and myelination, both key processes contributing to volume reduction and thinning as assessed in human neuroimaging studies.30,31 The available data allow an unprecedented investigation into the association of childhood depression with the trajectories of cortical gray matter development across late school age and early adolescence.

Methods

Two hundred eleven children participated in a longitudinal study of preschool depression from September 22, 2003, to December 13, 2014, comprising 3 waves of neuroimaging. Healthy children and those with a history of depression were invited to participate and undergo neuroimaging (see eFigure 1 in the Supplement for exclusion criteria). One hundred ninety-three children had useable IQ data and neuroimaging data at 1 or more waves. Before and at the time of the first scan, children participated in behavioral assessments comprising 1 to 7 annual (and 1-4 semiannual) waves. These assessments included parent and child report using age-appropriate psychiatric interviews (Preschool Age Psychiatric Assessment [PAPA], ages 3-7 years, parent-only report; Child and Adolescent Psychiatric Assessment [CAPA], age 8 years, parent-only report and ages ≥9 years, parent and child report).32,33 The validity and reliability of the major depressive disorider (MDD) module in the PAPA to identify depression in preschool-aged children (using a 6-month primary period) have been previously established.34,35 In addition, demographic, psychosocial (including stressful and traumatic life events assessed using the PAPA or CAPA), and developmental characteristics were also assessed (life events detailed in eTable 1 in the Supplement).34 To examine the effect of socioeconomic status on structural brain outcomes, we used a ratio of income to needs, defined as the income level of the family divided by the federal poverty line by family size. A childhood depression symptom number score that spanned the preschool-age through school-age period (P-SA-MDD) was calculated for each participant by taking the mean number of core symptoms of MDD endorsed at all behavioral assessment waves using the PAPA and/or CAPA before and concurrent with and including the first scan. During this period, participants completed a mean (SD) of 5.48 (2.80) (range, 1-11) behavioral assessments. We also created categorical groups based on history of diagnoses of MDD in the same period. The Child Depression Inventory (CDI) parent and child reports were completed at the time of neuroimaging. In addition, the Family Interview for Genetic Studies was used to assess for family history of depression in first- and second-degree relatives.36 All study methods were reviewed and approved by the Washington University School of Medicine Institutional Review Board. Written informed consent and assent were obtained from all study participants. Data analysis was conducted from October 29, 2014, to September 28, 2015.

Participants underwent neuroimaging with a magnetic resonance imaging scanner (model 3.0-T Tim Trio; Siemens Healthcare GmbH). The 2 results of magnetization-prepared rapid acquisition gradient echo scans were assessed visually, and the best one was selected for further processing by blinded raters. The selected magnetization-prepared rapid acquisition gradient echo image for each wave was processed using the longitudinal stream in FreeSurfer software package, version 5.3 (http://surfer.nmr.mgh.harvard.edu/).37 Several processing steps, such as skull stripping, Talairach transformations, and atlas registration, as well as spherical surface maps and parcellations, were initialized with common information from an unbiased within-patient template. This longitudinal stream reduces the bias that would otherwise be present in selecting a single scan result as baseline, and significantly increases reliability and statistical power.38 Further details of FreeSurfer methods are available in eAppendix 1 in the Supplement.

The longitudinal multilevel linear model (MLM) was implemented in SAS, version 9.3 (PROC MIXED; SAS Institute, Inc). The growth curve model included both random intercept and random slope components (with an unstructured covariance matrix between the 2 components). Time was coded as wave number. All models included the following covariates: age at first neuroimaging scan (centered at mean = 10.3 years), quadratic age at first neuroimaging scan, sex (male, 1; female, 0), ratio of income to needs at first neuroimaging scan (centered at mean = 1.74), and IQ (centered at 105.6). We also considered an MLM that included an autoregressive estimate of covariance between neuroimaging waves in the model residuals. However, this model had convergence problems with default settings, and even after achieving convergence via more lenient convergence parameters, the model fit statistics (Akaike information criterion, Akaike information criterion corrected for infinite sample sizes, Bayesian information criterion) favored the simpler growth-curve only model. Degrees of freedom calculations used the method of Kenward and Roger.39

Results

Participant characteristics at each neuroimaging scan are provided in Table 1. Of the 193 participants, 90 had lifetime MDD. eFigure 1 in the Supplement details the study flow, including dropout rates and reasons. The P-SA-MDD score was significantly different in depressed participants than in those without depression (eAppendix 2 in the Supplement).

A longitudinal MLM was conducted to examine the effect of P-SA-MDD symptom scores (centered at mean = 2.2) on initial values and change in global cortical gray volume. Growth trajectories (intercept, reflecting initial values, and linear change across time) were estimated for each participant. The P-SA-MDD symptom scores were used to predict variation in intercept and linear change over time (defined as neuroimaging scan number). Quadratic models were considered but the quadratic term was nonsignificant, indicating linear trajectories across time.

The longitudinal growth model result for gray matter volume is shown in Table 2. There was a main effect of time (B, −7.80; SE, = 0.61; t, = −12.73; P < .001), indicating that youths’ cortical gray volume decreased significantly across neuroimaging scan waves, consistent with normative gray matter reduction seen at the age range studied. In addition, male sex, higher ratio of income to needs, and higher IQ scores were significantly associated with larger volume. More important, there was a significant interaction between the P-SA-MDD symptom score and time, indicating that global cortical gray volume decreased faster for those with higher P-SA-MDD symptom scores (eFigure 2 in the Supplement). Specifically, children with a mean P-SA-MDD score of 5 (the number of symptoms needed for a diagnosis of MDD and almost 2 SDs above the mean) had a 1.81 times greater decrease in global cortical volume from the first neuroimaging scan to the third compared with children with a mean MDD severity of 0 (–3.65% vs –2.02%). Figure 1 shows individually estimated gray matter volume slopes as a function of P-SA-MDD symptom score.

To better understand the association of P-SA-MDD symptom score with gray matter decline, we conducted MLMs for global (mean) cortical thickness and global cortical area of the gray and white matter surface to determine which of these factors was contributing to the volume changes. As shown in Table 2, we saw a significant interaction between P-SA-MDD symptom score and time for global cortical thickness (Figure 2 and eFigure 2 in the Supplement), such that children with a P-SA-MDD symptom score of 5 had a 1.69 times greater decrease in cortical thickness from the first neuroimaging scan to the third compared with those with a mean MDD score of 0 (–3.93% vs –2.33%). No evidence was found for an effect of MDD symptom score on change in global cortical surface area (Table 2). However, main effects of time, sex, and IQ score indicated that the surface area decreased with time and that males and those with higher IQ scores had greater surface areas. Additional MLMs conducted separately for the left and right brain hemisphere indicated a significant interaction of P-SA-MDD symptom score with time in gray matter volume in the left hemisphere (P = .02), a marginally significant interaction in gray matter volume in the right hemisphere (P = .05), and significant interactions in both hemispheres for gray matter thickness (eTable 2 in the Supplement). These effects for volume and gray matter thickness were substantively identical and significant for trend level when the analyses were run separately for males and females, even though these analyses each had approximately half the sample size (eTable 3 and eTable 4 in the Supplement).

To aid in clinical interpretation, the models were run again using the dichotomous variable of MDD diagnosis before or concurrent with the first neuroimaging scan. The interaction between MDD and time was significant for volume (B, 2.84; SE, 1.24; t, 2.29; P = .02) and thickness (B, 0.0132; SE, 0.0049; t, 2.70; P = .008) but not surface area (B, 1.45; SE, 1.52; t, 0.96; P = .34), as was seen in the models of P-SA-MDD symptom scores.

When child-reported CDI score was used in the model, the interaction between CDI total T score and time was significant for volume (B, –0.26; SE, 0.10; t, –2.46; P = .02) and thickness (B, –0.0013; SE, 0.0004; t, –3.27; P = .001) but not surface area (B, –0.11; SE, 0.13; t, –0.81; P = .42). Results for parent-reported CDI score were going in the same direction (eAppendix 2 in the Supplement).

To further investigate the specificity of this effect for depression (vs psychotropic exposure), models were rerun removing participants with any lifetime psychotropic medication use, resulting in a sample size of 156. Despite this reduced sample size and elimination of some of the children with the most severe depression who received medication (mean P-SA-MDD symptom score before the first neuroimaging scan, 3.74 vs 1.85 for those with and without medication exposure, respectively; t, 8.27; df, 191; P < .001), similar results to those in the models with all children were found (eAppendix 2 in the Supplement).

Childhood depression is frequently comorbid with externalizing disorders, which are also associated with alterations in volume and/or thickness of gray matter.40 To determine whether patterns of gray matter change held when accounting for comorbid externalizing disorders, we reran the models including the presence of externalizing disorder (attention-deficit/hyperactivity disorder, oppositional defiant disorder, or conduct disorder) before the first neuroimaging scan and its interaction with time as additional covariates. The estimates, SEs, and significance of the interactions of P-SA-MDD symptom score with time were similar to those in the models that did not include externalizing disorders, again demonstrating greater declines in global volume of cortical gray matter (B, –1.09, SE, 0.51; t, –2.15; P = .03) and global thickness of cortical gray matter (B, –0.0044; SE, 0.0020; t, –2.22; P = .03) for those with higher P-SA-MDD symptom scores.

To explore whether the association between P-SA-MDD symptom score and cortical gray matter development might reflect the influence of the early experience of depression vs a familial or genetically influenced outcome, we included family history of depression (a potential marker of genetic risk) and its interaction with time as covariates in the models and found that the interaction of P-SA-MDD symptom score and time remained significant for global volume of cortical gray matter (B, –1.04; SE, 0.42; t, –2.44; P = .02) and global thickness of cortical gray matter (B, –0.0050; SE, 0.0017; t, –3.00; P = .003) and remained nonsignificant for global surface area of cortical gray matter (B, –0.53; SE, 0.53; t, –1.01; P = .32). To investigate whether the effect was specific to experiences of depression or based on more general forms of adversity, additional models included experiences of traumatic and stressful life events and their interaction with time as model covariates. Again, the interaction of P-SA-MDD symptom score and time remained significant for volume of cortical gray matter (B, –0.95; SE, 0.43; t, –2.19; P = .03) and thickness of cortical gray matter (B, –0.0046; SE, 0.0017; t, –2.75; P = .007) and remained nonsignificant for surface area of cortical gray matter (B, –0.46; SE, 0.53; t, –0.86; P = .39).

Additional MLMs were run to test for effects of P-SA-MDD symptom score over time in specific regions of the brain (eAppendix 2 in the Supplement). There were no significant interactions of P-SA-MDD symptom score with time after correction for multiple comparisons. Effect sizes were calculated by taking the correlation of P-SA-MDD symptoms score with participants’ individual slope estimates. Effect sizes for all regions tested are in eTable 5 in the Supplement.

Discussion

These longitudinal findings demonstrate marked bilateral decreases in thickness of cortical gray matter and in volume of the right hemisphere (with marginal significance on the left hemisphere) associated with mean level of depression symptom scores and MDD diagnosis experienced from preschool to school age. Children with depression symptom scores 2 SDs above the mean had reduction in volumes of gray matter at almost twice the rate of those with no childhood depression symptoms. Similarly, cortical thickness also decreased more rapidly at almost the same rate. To our knowledge, these findings provide the first longitudinal neuroimaging data showing increases in rates of volume reduction and cortical thinning related to number of childhood depression symptoms and diagnosis during the preschool-age to school-age period.

The absence of lateralized findings for thickness and the marginally significant finding on the left hemisphere for volume is notable given that much of the extant literature on MDD has discussed the right hemisphere.9,41,42 In addition, the lack of specific findings in regions that regulate the emotions is also notable given that these more specific volume reductions have been reported in depression.16,4346 These more global findings in children suggest a relatively generalized process that may be operative at earlier developmental stages. While we did see thinning in some candidate regions, none survived multiple comparison correction. The whole-brain effects likely arise from the aggregation of thinning across several regions. Such global volumetric and thickness alterations are also consistent with reports of large-scale structural network anomalies of gray matter in depressed adults.47 Furthermore, significant effects of sex and ratio of income to needs on the trajectory of volume and thickness of cortical gray matter were also found and are worthy of further follow-up. The finding of differences in thickness and volume without differences in surface area has been reported in several neuropsychiatric conditions.48,49

The study findings from this population enriched for early childhood depression and followed up into middle childhood and early adolescence provide the first data, to our knowledge, demonstrating depression-related alterations in volume and thickness of cortical gray matter evident as early as middle childhood. These findings add to a growing body of neuroimaging data in children and adolescents with depression reporting structural alterations in frontal cortical areas.42,46,50,51 The study findings provide support for the notion that cortical thinning evident in depression begins in childhood, as speculated by Peterson et al.9 A limitation of the study is that it was not possible to separate developmental timing effects. Future studies designed to compare patients with early childhood depression during the preschool period vs those without preschool depression with onset of depression at school age are needed to clarify whether there are effects of timing of childhood depression on brain change.

We speculate that changes in cortical gray matter related to childhood depression may reflect experience-dependent neuroplasticity. However, the study was not designed to definitively inform whether the cortical changes were related to depression-based experience-dependent plasticity vs other genetic or psychosocial processes. Nonetheless, the current findings are consistent with an experience-dependent process given the fact that a family history of depression, a putative marker of family genetic risk, was not related to trajectories of cortical gray matter. Furthermore, the finding that dimensional and categorical measures of MDD as well as ratio of income to needs predicted cortical change, and that the experience of stressful and traumatic life events during this same period was not related to cortical change, suggests that the finding may be more specifically related to certain types of negative experience rather than a more general effect of stressful events. While an experimental design with neuroimaging prior to depression would be needed to test this hypothesis, the findings highlight the potential unique importance of the experience of childhood depression in the developmental trajectory of cortical gray matter.

The functional and depression-specific clinical implications of the increased rate of volume reduction and thinning of the cerebral cortex found in this study are not clear from these data. Such alterations in other psychiatric disorders have been interpreted as accelerated maturation, which may be seen as maladaptive given that pruning may be strongly informed by early negative disorder-related experience rather than more developmentally normative positive experiences later. Furthermore, our study cannot inform the cellular processes that underlie the alterations in gray matter development and whether they are related to increased rates of myelination and/or cell loss owing to pruning.30,31 In addition, since the neuroimaging scans began after the cortical peak, it is not possible to determine whether early or premature and/or accelerated pruning is taking place.

Conclusions

Of critical importance is that childhood depressive symptoms were associated with decline in volume and thickness of cortical gray matter even after accounting for other key factors known to affect this developmental process. These findings of markedly increased rates of cortical volume loss and thinning across the entire cortex underscore the importance of attention to childhood depression as a marker of altered childhood cortical brain development. Whether these early alterations serve as an endophenotype of risk for later depressive episodes or chronic course is a question of interest as the study sample is followed up through adolescence. The study findings signal the need for greater public health attention and screening for depression in young children.52

Back to top
Article Information

Submitted for Publication: June 15, 2015; final revision received October 1, 2015; accepted October 6, 2015.

Corresponding Author: Joan L. Luby, MD, Department of Psychiatry, Washington University School of Medicine, Campus Box 8134, 660 S Euclid, St Louis, MO 63110 (lubyj@psychiatry.wustl.edu).

Published Online: December 16, 2015. doi:10.1001/jamapsychiatry.2015.2356.

Author Contributions: Drs Luby and Barch had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Luby, Belden, Barch.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Luby, Jackson, Tillman, Barch.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Belden, Jackson, Lessov-Schlaggar, Harms, Tillman, Whalen, Barch.

Obtained funding: Luby, Belden, Botteron, Barch.

Administrative, technical, or material support: Luby, Botteron, Barch.

Study supervision: Luby, Botteron, Barch.

Conflict of Interest Disclosures: Dr Luby reported receiving royalties from Guilford Press. Dr Barch reported serving as a consultant for Pfizer, Amgen, and Roche, and reported having a contract to analyze imaging data for Pfizer. No other disclosures were reported.

Funding/Support: This study was supported by grants R01MH66031, R01MH084840, R01MH090786, and R01MH098454-S from the National Institute of Mental Health; grant U54MH091657 from the National Institutes of Health Blueprint; (Dr Barch); grants 2R01 MH064769-06A1 (Dr Luby), PA-07-070 NIMH R01 (Drs Luby, Barch, and Botteron) 5K01MH090515-04 (Dr Belden), and T32 MH100019 (Dr Whalen and principal investigators Drs Barch and Luby) from the National Institutes of Health.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We acknowledge our child participants and their parents whose participation and cooperation made this research possible.

References
1.
Giedd  JN, Raznahan  A, Alexander-Bloch  A, Schmitt  E, Gogtay  N, Rapoport  JL.  Child psychiatry branch of the National Institute of Mental Health longitudinal structural magnetic resonance imaging study of human brain development. Neuropsychopharmacology.2015;40(1):43-49.PubMedArticle
2.
Gilmore  JH, Schmitt  JE, Knickmeyer  RC,  et al.  Genetic and environmental contributions to neonatal brain structure: a twin study. Hum Brain Mapp. 2010;31(8):1174-1182.PubMed
3.
Giedd  JN, Rapoport  JL.  Structural MRI of pediatric brain development: what have we learned and where are we going? Neuron. 2010;67(5):728-734.PubMedArticle
4.
Sowell  ER, Levitt  J, Thompson  PM,  et al.  Brain abnormalities in early-onset schizophrenia spectrum disorder observed with statistical parametric mapping of structural magnetic resonance images. Am J Psychiatry. 2000;157(9):1475-1484.PubMedArticle
5.
Paus  T.  Mapping brain maturation and cognitive development during adolescence. Trends Cogn Sci. 2005;9(2):60-68.PubMedArticle
6.
Lenroot  RK, Schmitt  JE, Ordaz  SJ,  et al.  Differences in genetic and environmental influences on the human cerebral cortex associated with development during childhood and adolescence. Hum Brain Mapp. 2009;30(1):163-174.PubMedArticle
7.
Frangou  S, Chitins  X, Williams  SC.  Mapping IQ and gray matter density in healthy young people. Neuroimage. 2004;23(3):800-805.PubMedArticle
8.
van Eijndhoven  P, van Wingen  G, Katzenbauer  M,  et al.  Paralimbic cortical thickness in first-episode depression: evidence for trait-related differences in mood regulation. Am J Psychiatry. 2013;170(12):1477-1486.PubMedArticle
9.
Peterson  BS, Warner  V, Bansal  R,  et al.  Cortical thinning in persons at increased familial risk for major depression. Proc Natl Acad Sci U S A. 2009;106(15):6273-6278.PubMedArticle
10.
Gogtay  N, Rapoport  JL.  Childhood-onset schizophrenia: insights from neuroimaging studies. J Am Acad Child Adolesc Psychiatry. 2008;47(10):1120-1124.PubMedArticle
11.
Paus  T, Keshavan  M, Giedd  JN.  Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci. 2008;9(12):947-957.PubMed
12.
Zatorre  RJ, Fields  RD, Johansen-Berg  H.  Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15(4):528-536.PubMedArticle
13.
Gogtay  N, Thompson  PM.  Mapping gray matter development: implications for typical development and vulnerability to psychopathology. Brain Cogn. 2010;72(1):6-15.PubMedArticle
14.
Shaw  P, Eckstrand  K, Sharp  W,  et al.  Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci U S A. 2007;104(49):19649-19654.PubMedArticle
15.
Greven  CU, Bralten  J, Mennes  M,  et al.  Developmentally stable whole-brain volume reductions and developmentally sensitive caudate and putamen volume alterations in those with attention-deficit/hyperactivity disorder and their unaffected siblings. JAMA Psychiatry. 2015;72(5):490-499.PubMedArticle
16.
Gogtay  N.  Cortical brain development in schizophrenia: insights from neuroimaging studies in childhood-onset schizophrenia. Schizophr Bull. 2008;34(1):30-36.PubMedArticle
17.
Rapoport  JL, Inoff-Germain  G.  Update on childhood-onset schizophrenia. Curr Psychiatry Rep. 2000;2(5):410-415.PubMedArticle
18.
Bora  E, Fornito  A, Pantelis  C, Yücel  M.  Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies. J Affect Disord. 2012;138(1-2):9-18.PubMedArticle
19.
Grieve  SM, Korgaonkar  MS, Koslow  SH, Gordon  E, Williams  LM.  Widespread reductions in gray matter volume in depression. Neuroimage Clin. 2013;3:332-339.PubMedArticle
20.
Gibb  B. Depression in childhood. In: Gotlib  I, Hammen  CL, ed. Handbook of Depression.3rd ed. New York, NY: Guilford Publications; 2014.
21.
Whittle  S, Lichter  R, Dennison  M,  et al.  Structural brain development and depression onset during adolescence: a prospective longitudinal study. Am J Psychiatry. 2014;171(5):564-571.PubMedArticle
22.
Belden  AC, Barch  DM, Oakberg  TJ,  et al.  Anterior insula volume and guilt: neurobehavioral markers of recurrence after early childhood major depressive disorder. JAMA Psychiatry. 2015;72(1):40-48.PubMedArticle
23.
Barch  DM, Gaffrey  MS, Botteron  KN, Belden  AC, Luby  JL.  Functional brain activation to emotionally valenced faces in school-aged children with a history of preschool-onset major depression. Biol Psychiatry. 2012;72(12):1035-1042.PubMedArticle
24.
Gaffrey  MS, Barch  DM, Singer  J, Shenoy  R, Luby  JL.  Disrupted amygdala reactivity in depressed 4- to 6-year-old children. J Am Acad Child Adolesc Psychiatry. 2013;52(7):737-746.PubMedArticle
25.
Marrus  N, Belden  A, Nishino  T,  et al.  Ventromedial prefrontal cortex thinning in preschool-onset depression. J Affect Disord. 2015;180:79-86.PubMedArticle
26.
Luby  JL, Gaffrey  MS, Tillman  R, April  LM, Belden  AC.  Trajectories of preschool disorders to full DSM depression at school age and early adolescence: continuity of preschool depression. Am J Psychiatry. 2014;171(7):768-776.PubMedArticle
27.
Amlien  IK, Fjell  AM, Tamnes  CK,  et al.  Organizing principles of human cortical development—thickness and area from 4 to 30 years: insights from comparative primate neuroanatomy [published online September 21, 2014]. Cereb Cortex. doi:10.1093/cercor/bhu214.
28.
Cai  DC, Fonteijn  H, Guadalupe  T,  et al.  A genome-wide search for quantitative trait loci affecting the cortical surface area and thickness of Heschl’s gyrus. Genes Brain Behav. 2014;13(7):675-685.PubMedArticle
29.
McKay  DR, Knowles  EE, Winkler  AA,  et al.  Influence of age, sex and genetic factors on the human brain. Brain Imaging Behav. 2014;8(2):143-152.PubMedArticle
30.
Sowell  ER, Thompson  PM, Holmes  CJ, Jernigan  TL, Toga  AW.  In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nat Neurosci. 1999;2(10):859-861.PubMedArticle
31.
Sowell  ER, Thompson  PM, Tessner  KD, Toga  AW.  Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. J Neurosci. 2001;21(22):8819-8829.PubMed
32.
Egger  HL, Ascher  AA. Preschool Age Psychiatric Assessment (PAPA). Durham, NC: Duke University Medical Center; 1999.
33.
Angold  A, Costello  EJ.  The Child and Adolescent Psychiatric Assessment (CAPA). J Am Acad Child Adolesc Psychiatry. 2000;39(1):39-48.PubMedArticle
34.
Luby  JL, Si  X, Belden  AC, Tandon  M, Spitznagel  E.  Preschool depression: homotypic continuity and course over 24 months. Arch Gen Psychiatry. 2009;66(8):897-905.PubMedArticle
35.
Luby  JL, Belden  AC, Pautsch  J, Si  X, Spitznagel  E.  The clinical significance of preschool depression: impairment in functioning and clinical markers of the disorder. J Affect Disord. 2009;112(1-3):111-119.PubMedArticle
36.
Maxwell  ME. Manual for the Family Interview for Genetic Studies (FIGS). Bethesda, MD: Clinical Neurogenetics Branch, Intramural Research Program, National Insititute of Mental Health; 1992.
37.
Reuter  M, Rosas  HD, Fischl  B.  Highly accurate inverse consistent registration: a robust approach. Neuroimage. 2010;53(4):1181-1196.PubMedArticle
38.
Reuter  M, Schmansky  NJ, Rosas  HD, Fischl  B.  Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012;61(4):1402-1418.PubMedArticle
39.
Kenward  MG, Roger  JH.  Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 1997;53(3):983-997.PubMedArticle
40.
Shaw  P, Greenstein  D, Lerch  J,  et al.  Intellectual ability and cortical development in children and adolescents. Nature. 2006;440(7084):676-679.PubMedArticle
41.
Bruder  GE, Quitkin  FM, Stewart  JW, Martin  C, Voglmaier  MM, Harrison  WM.  Cerebral laterality and depression: differences in perceptual asymmetry among diagnostic subtypes. J Abnorm Psychol. 1989;98(2):177-186.PubMedArticle
42.
Moratti  S, Rubio  G, Campo  P, Keil  A, Ortiz  T.  Hypofunction of right temporoparietal cortex during emotional arousal in depression. Arch Gen Psychiatry. 2008;65(5):532-541.PubMedArticle
43.
Price  JL, Drevets  WC.  Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn Sci. 2012;16(1):61-71.PubMedArticle
44.
Drevets  WC, Savitz  J, Trimble  M.  The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 2008;13(8):663-681.PubMed
45.
Ducharme  S, Albaugh  MD, Hudziak  JJ,  et al.  Anxious/depressed symptoms are linked to right ventromedial prefrontal cortical thickness maturation in healthy children and young adults. Cereb Cortex.2014;24(11):2941-2950.PubMedArticle
46.
MacMaster  FP, Carrey  N, Langevin  LM, Jaworska  N, Crawford  S.  Disorder-specific volumetric brain difference in adolescent major depressive disorder and bipolar depression. Brain Imaging Behav. 2014;8(1):119-127.PubMedArticle
47.
Singh  MK, Kesler  SR, Hadi Hosseini  SM,  et al.  Anomalous gray matter structural networks in major depressive disorder. Biol Psychiatry. 2013;74(10):777-785.PubMedArticle
48.
Zarei  M, Ibarretxe-Bilbao  N, Compta  Y,  et al.  Cortical thinning is associated with disease stages and dementia in Parkinson’s disease. J Neurol Neurosurg Psychiatry. 2013;84(8):875-881.PubMedArticle
49.
Fan  Q, Palaniyappan  L, Tan  L,  et al.  Surface anatomical profile of the cerebral cortex in obsessive-compulsive disorder: a study of cortical thickness, folding and surface area. Psychol Med. 2013;43(5):1081-1091.PubMedArticle
50.
Botteron  KN, Raichle  ME, Drevets  WC, Heath  AC, Todd  RD.  Volumetric reduction in left subgenual prefrontal cortex in early onset depression. Biol Psychiatry. 2002;51(4):342-344.PubMedArticle
51.
Goodman  M, Hazlett  EA, Avedon  JB, Siever  DR, Chu  KW, New  AS.  Anterior cingulate volume reduction in adolescents with borderline personality disorder and co-morbid major depression. J Psychiatr Res. 2011;45(6):803-807.PubMedArticle
52.
Luby  JL, Heffelfinger  A, Koenig-McNaught  AL, Brown  K, Spitznagel  E.  The Preschool Feelings Checklist: a brief and sensitive screening measure for depression in young children. J Am Acad Child Adolesc Psychiatry. 2004;43(6):708-717.PubMedArticle
×