Differential Patterns of Delayed Emotion Circuit Maturation in Abused Girls With and Without Internalizing Psychopathology
Publication: American Journal of Psychiatry
Abstract
Objective:
Childhood abuse represents one of the most potent risk factors for developing psychopathology, especially in females. Evidence suggests that exposure to early-life adversity may be related to advanced maturation of emotion processing neural circuits. However, it remains unknown whether abuse is related to early circuit maturation and whether maturation patterns depend on the presence of psychopathology.
Methods:
A multisite sample of 234 girls (ages 8–18 years) completed clinical assessment, maltreatment histories, and high-resolution T1-weighted structural MRI. Girls were stratified by abuse history and internalizing disorder diagnosis into typically developing (no abuse/no diagnosis), resilient (abuse/no diagnosis), and susceptible (abuse/current diagnosis) groups. Machine learning models of normative brain development were aggregated in a stacked generalization framework trained to predict chronological age using gray matter volume in whole-brain, emotion, and language circuit parcellations. Brain age gap estimations (BrainAGEs; predicted age minus true chronological age) were calculated as indices of relative circuit maturation.
Results:
Childhood abuse was related to reduced BrainAGE (delayed maturation) specific to emotion circuits. Delayed emotion circuit BrainAGE was further related to increased hyperarousal symptoms. Childhood physical neglect was associated with increased whole-brain BrainAGE (advanced maturation). Neural contributors to emotion circuit BrainAGE differed in girls with and without an internalizing diagnosis, especially in the lateral prefrontal, parietal, and insular cortices and the hippocampus.
Conclusions:
Abuse exposure in girls is associated with a delayed structural maturation pattern specific to emotion circuitry, a potentially adaptive mechanism enhancing threat generalization. Physical neglect, on the other hand, is associated with a broader brain-wide pattern of advanced structural maturation. The differential influence of fronto-parietal cortices and the hippocampus on emotion circuit maturity in resilient girls may represent neurodevelopmental markers of reduced psychiatric risk following abuse.
Exposure to potentially traumatic events during childhood is pervasive, with two-thirds of children experiencing violence (physical abuse, sexual abuse, or witnessing community or domestic violence) by age 16 (1). Early-life exposure to violence can markedly alter neurobiological, psychological, and social development (2). These changes increase the risk for developing both first-onset and comorbid internalizing psychopathology (3), which has especially high overlap in symptom expression in adolescent girls (4). However, the neurodevelopmental mechanisms conferring resilience and susceptibility to psychiatric disorders following abuse remain unclear. Recent models suggest that early-life adversity may alter maturation patterns in emotion-processing circuits, but it remains unknown how neurodevelopmental maturity may influence the relationship between threat-related (compared with deprivation-related) stress and risk for internalizing psychopathology. Identification of neural maturational markers of resilience and susceptibility could have important implications for clinical monitoring and treatment for youths who are victims of abuse.
Evolutionary trade-offs between an individual’s survival and development (e.g., investment in growth) and reproductive success (e.g., investment in sexual maturity) likely underlie individual differences in child development after early-life adversity. Life history theory and the differential susceptibility model suggest that these trade-offs are likely constrained by the susceptibility of various developmental milestones to early experiences. The stress acceleration hypothesis extends these frameworks by integrating early-life adversity and child neurodevelopment (5). According to the hypothesis, early-life adversity may promote early development of emotion circuits, particularly those underlying threat-safety processing, to meet potentially dangerous environmental demands. Indeed, stress-related changes in the recruitment of emotion circuits often show patterns suggesting advanced development. For example, early development of amygdala-medial prefrontal cortex (mPFC) functional connectivity has been observed in youths exposed to maternal deprivation stress (6) and those residing in disadvantaged socioeconomic neighborhoods (7), and it has been found to be longitudinally associated with the severity of early-life adversity generally (8). However, a more thorough review of the related literature indicates mixed results overall, equally suggesting both delayed maturation and no maturational differences (9). Importantly, the stress acceleration model of adversity and the development of emotion-related circuits may be dependent on the characteristics of adversity experienced; this has been suggested by recent work incorporating the dimensional model of adversity and psychopathology (10), where advanced biological aging measured via telomere shortening, epigenetic age, and pubertal development were specific to threat-related adversity (e.g., abuse) compared with deprivation-related adversity (e.g., neglect) (11).
The relationship between emotion circuit maturation after abuse and resilience or susceptibility to subsequent psychopathology is also unclear. Current evidence suggests that fast developmental strategies during childhood increase risk for psychopathology in adulthood (12). Additionally, abused youths are at increased risk for internalizing psychopathology earlier, with greater severity, and with more comorbidities (13, 14). The latent vulnerability model (15) suggests that resilience and susceptibility to psychopathology after early-life adversity depends on the degree of neurodevelopmental flexibility in systems underlying salience detection, threat appraisal, and emotion regulation in adapting to future adversity. Earlier-developing circuits underlying salience detection and threat appraisal (e.g., the amygdala, insula, and mPFC) are likely recalibrated toward increased recruitment to threat-related stimuli, spurring development toward a more mature threat processing phenotype. We suspected that vulnerability to psychopathology may then fundamentally depend on how later-developing circuits, especially in the lateral PFC, are recalibrated in response. In contrast to earlier-developing structures, the lateral PFC does not reach developmental plateau until early adulthood, remaining highly plastic throughout adolescence by maintaining increased levels of synaptogenesis and experience-dependent pruning, myelination, and apoptosis (16). Therefore, such systems likely show greater variability in their developmental trajectories after early-life adversity, leading to greater variability in executive control processes underlying resilience or susceptibility to psychopathology. We posit that adaptive flexibility in the maturation of the lateral PFC is a key predictor for the development of internalizing psychopathology after early-life adversity, although this is likely highly dependent on the timing of the adversity.
In this study, we examined whether abused girls show advanced structural maturation in emotion-related circuits compared with whole-brain and language-related circuitry. Language-related circuitry was used as a control circuit to evaluate emotion circuit specificity. We examined whether the degree of structural maturity depended on the absence (resilience) or presence (susceptibility) of internalizing disorders and whether these effects were specific to abuse (threat) compared with neglect (deprivation). The focus on internalizing disorders broadly was a result of high rates of comorbidity between anxiety, depression, and posttraumatic stress disorder (PTSD), which if segregated would have considerably limited sample sizes and statistical power for psychopathology-related comparisons. Finally, we examined whether particular brain regions contributed to overall circuit maturation differently in abused girls who are resilient to internalizing disorders compared with those who are susceptible to internalizing disorders. By using machine learning in a stacked generalization framework, we implemented a normative development model trained to predict chronological age from regional gray matter volume estimates in typically developing girls. We then used the normative model to calculate a brain age gap estimation (BrainAGE; predicted age minus chronological age), an index of relative circuit maturation. BrainAGE has been shown to represent a biologically meaningful index that is reliable (17, 18), heritable (18), and associated with developmental neurophenotypes underlying illness (19, 20). This index is specific to an individual’s brain at the time the data were collected and should not be interpreted as reflecting a developmental trajectory that the brain will follow into the future. We hypothesized that abused girls, both resilient and susceptible to internalizing disorders, would show greater emotion circuit BrainAGE (indicating advanced maturation) relative to typically developing girls. We also hypothesized that abuse-related gray matter volume in early-developing regions underlying salience detection and threat appraisal (e.g., the amygdala and insula) would contribute to more positive (advanced) BrainAGE. Finally, we hypothesized that late-developing regions underlying attentional processes and executive control (e.g., the lateral PFC) would best differentiate circuit maturation in resilient compared with susceptible abused girls.
METHODS
Participant Recruitment and Assessment
A total of 246 adolescent girls (chronological age range, 8–18 years) were pooled from research studies at three sites: Madison, Wisc.; Little Rock, Ark.; and Seattle. We investigated violence exposure-related differences specific to abuse (physical, sexual, and emotional). Details regarding sample sizes, MRI parameters, and demographic information for each site are presented in Table 1 and Table S1 in the online supplement. Written informed consent and/or verbal assent were obtained from all participants, and study procedures were approved by institutional review boards. A detailed account of clinical assessments, including diagnostic battery, anxiety, depression, and PTSD symptom severity, and maltreatment history is provided in the online supplement. The girls were classified into three groups based on binary abuse exposure (unexposed compared with exposed) and internalizing diagnosis (presence compared with absence): typically developing (unexposed, no diagnoses), resilient (exposed, no diagnoses), and susceptible (exposed, at least one diagnosis). It is important to note that we use the terms resilient and susceptible to refer to the current absence or presence of internalizing psychopathology after abuse, respectively. Because many youths in this sample had yet to reach the chronological age corresponding to the average age at onset for some internalizing disorders, in this context, resiliency does not imply that these youths may not be susceptible to disorders in the future.
| Group | Comparisonc | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Typically Developing (N=99) | Resilient (N=50) | Susceptible (N=85)b | |||||||||
| Characteristic | Mean | SD | Mean | SD | Mean | SD | F | t | df | Direction | p |
| Age (years) | 13.78 | 2.64 | 14.15 | 2.25 | 14.55 | 2.32 | 2.59 | n.s. | 1, 150 | — | 0.076 |
| IQ | 111.68 | 16.73 | 106.02 | 17.05 | 99.14 | 15.64 | 32.70 | –3.31 | 1, 150 | Typically developing > resilient | <0.001 |
| –7.32 | 1, 150 | Resilient > susceptible | <0.001 | ||||||||
| Tanner staged | 3.67 | 1.20 | 3.49 | 1.41 | 3.35 | 1.39 | 1.22 | n.s. | 1, 65 | — | 0.296 |
| CTQ abuse | 15.94 | 1.09 | 25.92 | 8.37 | 33.88 | 12.37 | 141.10 | 8.28 | 1, 150 | Typically developing < resilient | <0.001 |
| 15.79 | 1, 150 | Resilient < susceptible | <0.001 | ||||||||
| CTQ physical neglect | 6.76 | 3.08 | 8.76 | 3.57 | 9.69 | 4.44 | 13.82 | 2.74 | 1, 150 | Typically developing < resilient | <0.001 |
| 5.00 | 1, 150 | Typically developing < susceptible | <0.001 | ||||||||
| MFQd | — | — | 4.60 | 4.39 | 17.01 | 10.53 | 171.20 | 12.97 | 1, 110 | Resilient < susceptible | <0.001 |
| SCAREDd | — | — | 19.83 | 11.50 | 32.84 | 16.54 | 36.43 | 67.80 | 1, 64 | Resilient < susceptible | <0.001 |
| PTSD-RId | — | — | 20.58 | 15.42 | 43.53 | 17.62 | 89.72 | 3.07 | 1, 97 | Resilient < susceptible | <0.001 |
a
CTQ=Childhood Trauma Questionnaire; MFQ=Mood and Feelings Questionnaire; n.s.=nonsignificant; PTSD=posttraumatic stress disorder; PTSD-RI=UCLA Child and Adolescent PTSD Reaction Index for DSM-5; SCARED=Screen for Child Anxiety Related Emotional Disorders.
b
Internalizing disorders in the susceptible group were as follows: anxiety disorder, N=38 (44.7%); depressive disorder, N=59 (69.4%); and posttraumatic stress disorder, N=56 (65.9%).
c
Group comparison statistics are indicated with the effect directions for significant differences (p<0.05).
d
Variables that were not consistently available for all participants (missing not at random).
Image Acquisition and Individual Preprocessing
A detailed account of image acquisition and individual preprocessing of MRI data is provided in the Materials section and Table S1 in the online supplement. Briefly, mean voxel-wise cortical and subcortical gray matter volume was extracted from processed T1-weighted MRI scans using the Brainnetome Atlas (21). Neurosynth (22) was used to identify regions of interest belonging to emotion-related and language-related circuits, which are listed in Table S2 and diagrammed in Figure S1 in the online supplement. Finally, each region of interest was scaled to total intracranial volume and harmonized across scanners.
Normative Models of Gray Matter Volume Development
Model building and training.
Ensemble machine learning using a stacked generalization (23) approach was used to build normative models of gray matter volume development with respect to whole-brain, emotion, and language circuit features. More specifically, a “super learner,” optimizing the aggregation of multiple learning algorithms and minimizing cross-validation risk, was implemented for each neural feature set. Stacked generalization is a form of ensemble machine learning whereby individual “lower-level” learning algorithms are aggregated to increase predictive power by utilizing the strengths of each base model (referred to as submodels). A super learner, therefore, is a final prediction aggregation model with the objective of finding the optimal combination of submodel predictions. A more detailed overview of the super learning algorithm (Figure 1A) and the model-building protocol (Figure 1B) are provided in the online supplement.
FIGURE 1. Super-learner algorithm and normative model-building protocola
a Panel A shows the super-learner algorithm as implemented with an L2-penalized linear combination of predictions from N unique machine learning algorithms (submodels, including general linear ridge, multilayer perceptron, random forest, support vector machine, and gradient-boosting machine regression). Submodel held-out predictions in each round of cross-validation were used to tune the super-learner parameters. The full training set was used to train each submodel and the super-learner coefficients (β1…βN). Submodel predictions, S=(S1…SN), were input to the super learner to make final age predictions and calculate brain age gap estimations (BrainAGEs). Panel B shows a schematic of the model-building protocol for predicting chronological age and BrainAGE from gray matter volume features. Data were pooled, and participants were pseudorandomly assigned to the training or validation set (stratified by scanner and age). First, hyperparameters for each submodel algorithm were tuned using 10-fold cross-validation. The optimized submodels make predictions using 10-fold cross-validation, and the hold-out set is used to tune the super-learner hyperparameters. Finally, the optimized submodels are trained on the full training, and the optimized super learner is trained on the full hold-out set. The trained super learner is then evaluated using the set-aside validation set.
Model evaluation.
The super learner and comprising submodels were evaluated with a validation set of typically developing girls absent during training. Girls in the validation set were pseudorandomly assigned so as to remain representative of the training set (stratified by age and scanner). Mean absolute error was used to evaluate model performance. To ensure above-chance performance, a null distribution was created with 1,000 bootstrap samples of the label vector (chronological age), and age was predicted with each bootstrap. Median differences between the true performance and null performance distributions were calculated. All models underwent a final evaluation step comparing predicted and chronological ages using Pearson’s correlation (r), and BrainAGEs were calculated for all abused girls. Here, a negative BrainAGE in abused girls relative to typically developing girls indicates the extent of delayed maturation, and a positive BrainAGE indicates the extent of advanced maturation.
BrainAGE Group-Level Analyses
Linear mixed-effects models in R (lme4 package) were used to determine abuse-related and diagnosis-related differences in BrainAGE from whole-brain, emotion, and language circuit features. BrainAGEs for abused girls were standardized to the typically developing set, and for each neural feature set, an abuse-by-diagnosis interaction and main effects were included. As is common with BrainAGE (24), there was an age bias in the super learner; therefore, chronological age was included as a covariate. This bias correction was only included when analyzing BrainAGE at the group level and therefore does not change the interpretability of the BrainAGE as a maturational index. Additionally, IQ (gray matter volume is strongly associated with IQ in older children and adolescents [25]), scanner, and physical neglect experiences were included as covariates to safeguard against scanner effects and test specificity to abuse. Finally, an additional Bonferroni correction was applied across the three sets of circuit features (alpha=0.017, 0.05/3). Post hoc analyses were conducted if significant relationships were identified, investigating the relationship between BrainAGE differences and internalizing symptom severity (PTSD, depression, and anxiety symptoms) and pubertal stage. Additional details regarding all models tested, binarized compared with continuous analyses, and covariates are provided in the online supplement.
Feature Influence Analysis
Calculating regional influence on BrainAGE.
We hypothesized that regional gray matter volume phenotypes related to abuse may differentially influence BrainAGE estimates in resilient girls compared with susceptible girls. In order to determine how influential abuse-related gray matter volume was to BrainAGE, a perturbation sensitivity approach was used (see Figure S3 in the online supplement). Perturbation in this context does not refer to an anxiety phenotype but rather to a change in model performance after that model had been altered in some way. The goals of perturbation sensitivity were twofold: first, to determine whether an altered distribution of BrainAGEs was significantly different from the unaltered distribution of BrainAGEs (significant feature influence), and second, to determine whether perturbation with an abuse-related phenotype caused a shift in BrainAGE distribution relative to the distribution observed with typically developing perturbation (beyond chance expectations). In pursuit of these goals, perturbation across more than two of the groups at once was ruled out as a possible approach. The rationale for this analysis is as follows: perturbing a typically developing gray matter volume phenotype with an abuse-related gray matter volume phenotype should strongly affect the normative model’s age prediction (and related BrainAGE) if that region’s abuse phenotype is informative to either significantly increased or decreased BrainAGE. Similarly, if an abuse-related phenotype is not informative for the model’s age prediction, there will be no significant change in the distribution of BrainAGEs. Feature influence was calculated as the median change in BrainAGE distribution after perturbation (perturbed median BrainAGE minus true median BrainAGE). Further details regarding abuse-related gray matter volume sampling and statistical tests comparing BrainAGE distributions are provided in the online supplement.
RESULTS
Participant Demographic Characteristics
Aggregated demographic, clinical, and maltreatment variables across study sites are summarized in Table 1 and are split by study site in Table S1 in the online supplement. The pooled cohort comprised 234 adolescent girls between 8 and 18 years old (mean age, 14.15 years [SD=2.47]). In total, 99 girls had no abuse exposure or diagnosed internalizing psychopathology (typically developing), 50 girls had abuse exposure and no diagnosis (resilient), and 85 girls had abuse exposure and at least one diagnosis (susceptible). Within the susceptible group, 38 girls (44.7%) had an anxiety disorder, 59 (69.4%) had a depressive disorder, and 56 (65.9%) had PTSD; 62 girls (72.9%) had at least two of these disorders.
Normative Model Performance and Bias
The super learner and its submodels performed significantly better than chance expectations (all p values <<0.001). Model performances on the validation set of typically developing girls for each super learner and comprising submodels are reported in Table S4 in the online supplement. Models generalized well to the validation set (means for whole-brain: mean absolute error=2.463 years, r=0.507, p=0.048; means for emotion: mean absolute error=1.851 years, r=0.594, p=0.003; means for language: mean absolute error=2.326 years, r=0.566, p=0.009). For each feature set, the super learner performed better than all comprising submodels (whole-brain: mean absolute error=1.666 years, r=0.677, p<0.001; emotion: mean absolute error=1.602 years, r=0.663, p<0.001; language: mean absolute error=1.569 years, r=0.655, p<0.001). There was an age bias in each super learner, overestimating and underestimating BrainAGEs in younger and older girls, respectively (see Figure S4 in the online supplement; emotion circuitry: t=−11.963, df=756, p<<0.001)
Group-Level BrainAGE Relationships
Group differences in BrainAGE.
In the whole-brain and language circuitry analyses, no significant abuse-related or diagnosis-related differences were identified (Figure 2A, 2C). In the emotion circuitry analysis, an abuse effect was identified, where girls exposed to abuse showed significantly reduced BrainAGE (Figure 2B) (F=15.680, df=1, 150, p<0.001; t=−2.366, df=150, p=0.014), an average of 0.70 years younger than typically developing girls of the same chronological age. There was a significant effect of physical neglect severity for whole-brain BrainAGE, where physical neglect experiences were positively associated with BrainAGE across all girls (β=0.064; F=7.933, df=1, 150, p=0.006; t=2.817, df=150, p=0.006). There were no significant or nearly significant scanner effects (see Figure S5 in the online supplement).
FIGURE 2. Abuse-related and internalizing diagnosis-related associations with brain age gap estimates (BrainAGEs) for whole-brain, emotion circuit, and language circuit feature setsa
a BrainAGEs for abused girls are represented as residualized z-scores relative to typically developing girls, controlling for label (chronological age), scanner, IQ, and physical neglect. For the whole-brain analysis, there were no significant differences in BrainAGE across groups (panel A). For the emotion circuit analysis, an abuse main effect reveals that resilient and susceptible girls showed significantly reduced average BrainAGEs relative to typically developing girls (panel B). For the language circuit analysis, there were no significant differences in BrainAGE across groups (panel C). Symptom-level association with emotion circuit BrainAGEs across abused girls are shown in panel D. Emotion circuitry BrainAGEs were negatively associated with hyperarousal symptoms (UCLA Child and Adolescent PTSD Reaction Index for DSM-5 subscale D score).
*p<0.017 after experiment-wide Bonferroni correction.
Symptom and puberty relationships with BrainAGE.
In the symptom analyses with emotion circuit BrainAGEs, there was a significant effect of PTSD hyperarousal symptoms (UCLA Child and Adolescent PTSD Reaction Index for DSM-5 [PTSD-RI] subscale D score). Here, across all abused girls, hyperarousal symptom severity was negatively associated with BrainAGE (Figure 3) (β=−0.091; t=−2.050, df=96, p=0.043). This relationship was identified only when controlling for all previous group-level covariates, abuse severity, and the remaining PTSD-RI subscale scores (unadjusted β=−0.053; t=−1.327, df=98, p=0.187). Finally, there were no significant relationships between BrainAGE and total PTSD (t=1.142, df=98, p=0.256), anxiety (t=0.354, df=65, p=0.724), and depression (t=0.340, df=110, p=0.734) symptoms or pubertal milestones (t=1.114, df=65, p=0.270).
FIGURE 3. Spatially unique feature influence results for resilient and susceptible girls from the perturbation sensitivity analysisa
a The region-of-interest color corresponds to the mean effect size (adjusted R2) of the brain age gap estimation (BrainAGE) distribution shift when the region is perturbed with an abuse-related gray matter volume phenotype. Darker green indicates greater positive shift, and darker red indicates greater negative shift.
Regional Influence on Abuse-Related BrainAGE Distribution
Regions influencing BrainAGE: all abused girls.
A summary of results from the feature influence analysis is presented in Table 2, separated by spatially overlapping (all abused girls) or unique (resilient compared with susceptible girls) contributors to BrainAGE. Resilient and susceptible girls showed few overlapping influential regions. Generally, thalamic gray matter volume from abused girls contributed to a positive (advanced) shift in BrainAGE: gray matter volume in the left and right mediodorsal thalamus (medial prefrontal thalamus: adjusted R2=0.087, false discovery rate-corrected p [pFDR] p<0.001; lateral prefrontal thalamus: adjusted R2=0.053, pFDR=0.040) and the left lateral pulvinar nucleus (caudal temporal thalamus: adjusted R2=0.049, pFDR=0.005) contributed to a positive shift in BrainAGE. Additionally, gray matter volume in the right caudal anterior cingulate cortex (adjusted R2=0.076, pFDR<0.039) contributed to a positive shift in BrainAGE for both resilient and susceptible girls. No regions contributed to a negative (delayed) shift in BrainAGE.
| Effect and Direction | Region | Hemisphere | Brodmann’s Area | Power et al. (52) Network Parcellations | Atlas Label | Adjusted R2 | False Discovery Rate p |
|---|---|---|---|---|---|---|---|
| Abuse exposure | |||||||
| Greater BrainAGE | Medial prefrontal thalamus | Left | — | Subcortical | Tha_8_1 | 0.087 | <0.001 |
| Caudal anterior cingulate cortex | Right | 24 | Cingulo-opercular | CG_7_5 | 0.076 | 0.039 | |
| Lateral prefrontal thalamus | Right | — | Subcortical | Tha_8_8 | 0.053 | 0.040 | |
| Caudal temporal thalamus | Left | — | Subcortical | Tha_8_7 | 0.049 | <0.001 | |
| Unique to resilient | |||||||
| Greater BrainAGE | Rostral inferior parietal lobule | Left | 40 | Fronto-parietal | IPL_6_6 | 0.098 | <0.001 |
| Premotor thalamus | Left | — | Subcortical | Tha_8_2 | 0.092 | <0.001 | |
| Dorsal inferior parietal lobule | Right | 39 | Fronto-parietal | IPL_6_2 | 0.087 | <0.001 | |
| Dorsolateral prefrontal cortex | Right | 8 | Fronto-parietal | MFG_7_5 | 0.087 | 0.006 | |
| Primary auditory cortex | Left | 41 | Auditory | STG_6_3 | 0.042 | 0.043 | |
| Ventral caudate nucleus | Left | — | Subcortical | BG_6_1 | 0.039 | 0.014 | |
| Reduced BrainAGE | Posterior parahippocampal gyrus | Right | 27 | Default mode | PhG_6_3 | 0.098 | 0.034 |
| Dorsal inferior parietal lobule | Left | 39, 40 | Dorsal attention | IPL_6_3 | 0.095 | <0.001 | |
| Dorsal agranular insula | Right | 13 | Cingulo-opercular | INS_6_3 | 0.083 | 0.050 | |
| Lateral orbitofrontal cortex | Right | 12, 47 | Cingulo-opercular | OrG_6_6 | 0.080 | 0.034 | |
| Lateral opercular prefrontal cortex | Right | 44 | Cingulo-opercular | IFG_6_6 | 0.079 | <0.001 | |
| Unique to susceptible | |||||||
| Greater BrainAGE | Occipital thalamus | Left | — | Subcortical | Tha_8_6 | 0.093 | <0.001 |
| Rostral temporal thalamus | Left | — | Subcortical | Tha_8_4 | 0.088 | <0.001 | |
| Dorsal dysgranular insula | Left | 13 | Cingulo-opercular | INS_6_6 | 0.081 | <0.001 | |
| Opercular prefrontal cortex | Right | 44 | Cingulo-opercular | IFG_6_5 | 0.051 | 0.028 | |
| Reduced BrainAGE | Caudal hippocampus | Left | — | Default mode | Hipp_2_2 | 0.088 | <0.001 |
| Caudal hippocampus | Right | — | Default mode | Hipp_2_2 | 0.087 | <0.001 | |
| Rostral temporal thalamus | Right | — | Subcortical | Tha_8_4 | 0.067 | <0.001 |
a
Regions are separated by overlapping compared with unique effects between abuse groups. Within these separations, regions are sorted by descending adjusted R2. The R2 and false discovery rate-corrected p values are for the abuse-perturbed brain age gap estimation (BrainAGE) comparison relative to the typically developing perturbed (chance) BrainAGE comparison. All probability values for the abuse-perturbed BrainAGE comparison relative to the true BrainAGE comparison were less than 0.001 and are not included to avoid redundancy.
Regions influencing BrainAGE: resilient girls.
Resilient girls showed regional gray matter volume phenotypes that uniquely influenced BrainAGE relative to susceptible girls (Table 2, Figure 3A). Regions contributing to a positive shift in BrainAGE included the left ventral lateral thalamic nucleus (premotor thalamus: adjusted R2=0.092, pFDR<0.001), the left rostral inferior parietal lobule (Brodmann’s area [BA] 40; adjusted R2=0.098, pFDR<0.001), the right dorsal inferior parietal lobule (BA 39; adjusted R2=0.087, pFDR<0.001), and the right dorsolateral prefrontal cortex (dlPFC; BA 8; adjusted R2=0.087, pFDR=0.006). Regions that contributed to a negative shift in BrainAGE included the right posterior parahippocampal gyrus (BA 27; adjusted R2=0.098, pFDR<0.034), the right dorsal insular cortex (BA 13; adjusted R2=0.083, pFDR=0.050), the right lateral orbitofrontal cortex (BA 12/47; adjusted R2=0.080, pFDR=0.034), and the right lateral opercular prefrontal cortex (BA 44; adjusted R2=0.079, pFDR<0.001).
Regions influencing BrainAGE: susceptible girls.
Susceptible girls showed regional gray matter volume phenotypes that uniquely influenced BrainAGE relative to resilient girls (Table 2, Figure 3B). Regions that contributed to a positive shift in BrainAGE included the left lateral geniculate nucleus (occipital thalamus: adjusted R2=0.093, pFDR<0.001), the medial pulvinar nucleus (rostral temporal thalamus: adjusted R2=0.088, pFDR<0.001), the left dorsal insular cortex (adjusted R2=0.081, pFDR<0.001), and the right opercular PFC (BA 44; adjusted R2=0.051, pFDR=0.028). Regions that contributed to a negative shift in BrainAGE included the left and right caudal hippocampus (left: adjusted R2=0.088, pFDR<0.001; right: adjusted R2=0.087, pFDR<0.001).
DISCUSSION
Abused girls, regardless of diagnostic status, showed delayed maturity in emotion circuitry (counter to our original hypothesis), which was further associated with increased hyperarousal symptoms. Advanced whole-brain BrainAGE was associated with increased physical neglect severity, suggesting differential effects of threat and deprivation stress on patterns of circuit-specific and whole-brain neurodevelopment. Additionally, we found unique regional contributors to emotion circuitry maturation in resilient and susceptible girls, most prominently in frontoparietal, hippocampal, and insular gray matter. Altogether, our findings provide new insights into brain maturational patterns related to threat- and deprivation-related adversity and internalizing psychopathology and point to potential systems-level mechanisms differentiating resilient and susceptible developmental trajectories.
Abuse exposure was associated with delayed emotion circuit maturity relative to typically developing girls, which was further related to increased hyperarousal symptoms (but only when controlling for group-level covariates and other PTSD-related symptoms). Although speculative, this suggests that delayed structural maturity in emotion circuits may underlie sensitive salience and threat detection systems in the brain, potentially leading to reduced threat-safety discrimination and states of generalized hypervigilance. Although enhanced threat bias in neurobiological reactivity is likely adaptive in abusive environments, this may lead to reliably misinterpreting safety cues as dangerous. Indeed, reduced threat-safety discrimination has previously been associated with younger developmental stage (children < adolescents < adults) (26, 27), as well as with increased recruitment of threat processing circuits with maltreatment exposure and increased risk for psychopathology (28, 29). For this reason, girls exposed to abuse may habitually recruit threat-related circuitry even in canonically safe contexts: this type of threat generalization, typically observed only in younger children, likely delays increases in synaptic pruning, circuit myelination, and other related processes that result in age-appropriate reductions in gray matter volume.
Physical neglect experiences, on the other hand, were positively associated with whole-brain maturity across typically developing and abused girls. Here, increased physical neglect corresponded to advanced whole-brain maturation. In contrast to the delayed emotion circuit maturation associated with abuse, this suggests that neglect and deprivation-related adversity may have global rather than circuit-specific effects on brain maturation. Indeed, the absence of expected age-typical cognitive and social inputs to the brain, as is often the case with physical neglect, likely affects the association cortex broadly, representing a global acceleration of neuronal and synapse elimination mechanisms typical of low-complexity environments (10, 30). These mechanisms would translate to decreased gray matter volume in a deprivation context for equivalent chronological age, an advanced maturation phenotype. This may also explain the seeming discrepancy between previously reported findings of advanced emotion circuit development with adversity and the findings reported here, because the majority of these previous studies documented adversity more specific to deprivation (maternal separation [6], disadvantaged socioeconomic neighborhoods [7]) or a combination of threat and deprivation (abuse with physical or emotional neglect [8)]). Thus, we suspect that the current formulation of the stress acceleration hypothesis, as it pertains to brain maturation, may more accurately account for broader whole-brain patterns specific to deprivation-related adversity. Additionally, because co-occurrence of deprivation- and threat-related adversity is common, global advanced maturation patterns specific to deprivation may mask threat-specific delayed maturation patterns only observed in emotion circuits when not examined separately, causing them to go unnoticed in earlier studies.
We observed unique gray matter volume phenotypes from abused girls contributing to significant shifts in BrainAGE distribution. Resilient, but not susceptible, abused girls showed dlPFC and lateral inferior parietal lobule gray matter volumes contributing to a positive (advanced) shift in emotion circuit maturation, supporting our original hypotheses. We previously reported that internalizing-susceptible youths show developmentally delayed gray matter volume reduction in the dlPFC (31), and cortical expansion of the dlPFC differentiated which youths showed remission or persistence of PTSD symptoms at 1-year follow-up (32). Although abnormal dlPFC structure has not been reliably associated with childhood abuse exposure (9), changes in dlPFC structure and function have been broadly associated with reduced internalizing symptoms (33, 34) and recovery from trauma (35). For example, an inability to recruit the dlPFC during differentiation of threat from nonthreat mediates the relationship between anxiety disorders and generalized fear (36). Additionally, repetitive transcranial magnetic stimulation of the dlPFC, altering functional connectivity with the amygdala (37) and nucleus accumbens (38), has been shown to reduce both anxiety and depression symptoms. Therefore, fronto-parietal structures contributing to advanced emotion circuit maturation in resilient girls may translate to a compensatory trajectory promoting more mature attentional and executive control processes, approaching adult patterns of function and decreasing psychiatric risk.
We also found that bilateral hippocampus gray matter volume contributed to negative (delayed) shifts in emotion circuit maturation in susceptible, but not resilient, abused girls. We previously reported age-related hippocampus gray matter abnormalities in traumatized youths with PTSD (39), one of the most commonly reported neural correlates of early-life stress in youths and adults. Here, both childhood maltreatment and trauma exposure generally are associated with decreased hippocampal volume (40–42), likely driven by a combination of decreased neurogenesis/neural progenitor cells (43), atrophy of dendrites and reduced postsynaptic dendritic spines (44), and a reduced pool of stem cells into adulthood (45). In alignment with our findings, decreased hippocampal volume appears to be more pronounced in maltreatment victims with PTSD and other internalizing disorders compared with those without (42). Together, these results suggest that neurodevelopment underlying resilience to internalizing after abuse is critically dependent on regional patterns of maturation, where regions with a late (e.g., the dlPFC) compared with an early (e.g., the hippocampus) developmental plateau may have extended windows of change susceptibility in which to reorganize, a key factor in the development of pathology-inducing circuit phenotypes. The factors determining which children will undergo resilient circuit reorganization and which will not are unknown and warrant investigation.
The majority of differences in BrainAGE influential regions between resilient and susceptible girls were found in nodes of the cingulo-opercular network, including the insular cortex, opercular PFC, and lateral orbitofrontal cortex. Gray matter volume from susceptible and resilient girls contributed to more positive (advanced) and more negative (delayed) shifts in emotion circuit maturation, respectively. The cingulo-opercular network is a key substrate of threat-processing circuitry and is recruited by salient or unexpected stimuli to reorient attention circuitry toward relevant cues and inform subsequent responses (46, 47). Abnormal development of the orbitofrontal cortex, cross-sectionally and longitudinally, has been reported in studies of abused youths (31, 39). As the brain’s “engine of alertness,” abnormalities likely underlie symptoms of hyperarousal and hypervigilance commonly observed after abuse (48, 49). In fact, meta-analyses across all DSM-IV axis I disorders suggest that gray matter volume in the insular cortex is the best differentiator of individuals with diagnosable psychopathology broadly and those without (50, 51). We suspect that differences in the maturation of the cingulo-opercular network underlie important differences between resilient and susceptible girls after abuse, presumably through biasing attentional processes toward threat detection and the promotion of emotional reactivity.
Our study is not without limitations. First, as is the case for many pediatric neuroimaging studies, the sample size of typically developing and abused girls was modest. The small sample size for nonabused girls with internalizing diagnoses precluded us from an omnibus analysis interrogating maturation patterns specific to internalizing psychopathology. Second, given that many measures collected had substantial missing data (depression, anxiety symptoms, and pubertal milestones), and these were not missing-at-random data, associated null findings should be interpreted with caution. For example, in a full sample, we would expect that earlier pubertal milestones for the same chronological age would be associated with more positive BrainAGE and, accordingly, that abuse exposure would be associated with delayed pubertal milestones. This would represent an important step in evaluating the biological validity of BrainAGE and its ability to test hypotheses regarding maturation. Finally, our sample included only female youths. While relevant given the higher prevalence of internalizing disorders in girls, studies including boys could begin to disentangle which reported effects are sex specific. Future research would be strengthened by using both structural and functional MRI data for abused youths simultaneously, supporting the functional implications of structural maturity differences. Longitudinal studies are required to confirm maturational differences associated with abuse, as well as whether preadversity gray matter volume accounts for developmental delays and whether clinical interventions can bring emotion circuit maturity back into a healthy range.
Despite these limitations, our study has many strengths. First is our use of ensemble machine learning and feature influence analyses. To our knowledge, this is the first study to interrogate multivariate circuit-specific BrainAGEs, where constraining the neural feature set to specific domains of function allows a more precise indexing of maturation. This allows for the detection of altered circuit maturation, which may normally go undetected in whole-brain analyses. These methods also allowed us to explore how abuse-related gray matter volume phenotypes contributed to changes in BrainAGE, an important step in understanding the neurodevelopmental relationships learned by the normative model. Second, our study focused on disentangling the effects of threat-related adversity compared with deprivation-related adversity on circuit maturation, which are all too often aggregated into a single “adversity” cohort. Finally, this is one of only a handful of studies using BrainAGE to interrogate important questions in development, and more specifically, related to early-life adversity and psychopathology in youths. Normative neurodevelopment models and the BrainAGE maturity index have the potential to allow researchers to test treatment strategies targeting specific circuits and help clinicians monitor the neurodevelopmental trajectories of their patients, with the aim of helping guide them back into healthy ranges.
Footnote
The authors thank Rachael Meline, Shelby Weaver, and others from the Herringa, McLaughlin, and Cisler laboratories for their work in the recruitment and data collection for this study. The authors also thank Drs. Andrew Sheldon and Richard Davidson for their assistance in early iterations of the analyses and interpretation of results, as well as the youths and their families who participated in this study.
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Information & Authors
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History
Received: 10 August 2020
Revision received: 9 December 2020
Revision received: 26 March 2021
Accepted: 3 May 2021
Published online: 19 August 2021
Published in print: November 2021
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Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
Supported by NIH/NIMH (grants R01 MH103291, R01 MH106482, and R56 MH119194 to Dr. McLaughlin; grant R01 MH115910 to Dr. Herringa; grant R01 MH117141 to Drs. Zhu and Herringa; grants R21 MH097784 and R21 MH106869 to Dr. Cisler; grant K08 MH100267 to Dr. Herringa; and grant 5T32 MH018931 to Dr. Russell), an American Academy of Child and Adolescent Psychiatry Junior Investigator Award (to Dr. Herringa), the Arkansas Science and Technology Authority and the State of Arkansas (Dr. Cisler), NARSAD Young Investigator awards (to Drs. Cisler and Herringa), the University of Wisconsin Institute for Clinical and Translational Research Translational Pilot Award (grant NIH/NCATS UL1TR000427 to Dr. Herringa), a National Science Foundation Graduate Research Fellowship Award (grant DGE 1747503 to Mr. Keding), a University of Wisconsin Institute of Clinical and Translational TL1 Training Award (grant TL1TR000429 to Dr. Heyn), and the University of Wisconsin School of Medicine and Public Health.
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