Vonnie C. McLoyd's research while affiliated with University of Michigan and other places
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Unstable and unpredictable environments are linked to risk for psychopathology, but the underlying neural mechanisms that explain how instability relate to subsequent mental health concerns remain unclear. In particular, few studies have focused on the association between instability and white matter structures despite white matter playing a crucial role for neural development. In a longitudinal sample recruited from a population-based study (N = 237), household instability (residential moves, changes in household composition, caregiver transitions in the first 5 years) was examined in association with adolescent structural network organization (network integration, segregation, and robustness of white matter connectomes; Mage = 15.87) and young adulthood anxiety and depression (six years later). Results indicate that greater instability related to greater global network efficiency, and this association remained after accounting for other types of adversity (e.g., harsh parenting, neglect, food insecurity). Moreover, instability predicted increased depressive symptoms via increased network efficiency even after controlling for previous levels of symptoms. Exploratory analyses showed that structural connectivity involving the left fronto-lateral and temporal regions were most strongly related to instability. Findings suggest that structural network efficiency relating to household instability may be a neural mechanism of risk for later depression and highlight the ways in which instability modulates neural development.
Background
Stressful events, such as the COVID‐19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population‐based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences.
Methods
Data‐driven network‐based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self‐reported at ages 15, 17, and 21 (during COVID‐19). During COVID‐19, participants reported on pandemic‐related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup–adversity interaction were tested to predict change in symptoms over time.
Results
Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (β = .138, p = .042), and this result remained after adjusting for additional covariates (β = .194, p = .023). Furthermore, there was a subgroup–adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (β = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (β = .237, p = .021).
Conclusions
A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.
The special issue brings together scholarship that expands our understanding of the adverse effects of interpersonal, online, and vicarious racial discrimination on Black adolescents’ psychosocial well‐being and sociocultural factors (e.g., racial socialization and positive racial identity) that mitigate these effects. It also focuses attention on ways that adolescents’ behavior and characteristics shape racial socialization. Some of the critical tasks that lie ahead include elevating a developmental perspective, documenting developmental pathways, directly assessing proximal mediating processes, giving more attention to the robustness and replicability of findings, and expanding levels of analyses and outcomes to include both macro‐structural indicators and indicators of physiological and neuropsychological functioning.
Accumulating literature has linked poverty to brain structure and function, particularly in affective neural regions; however, few studies have examined associations with structural connections or the importance of developmental timing of exposure. Moreover, prior neuroimaging studies have not used a proximal measure of poverty (i.e., material hardship, which assesses food, housing, and medical insecurity) to capture the lived experience of growing up in harsh economic conditions. The present investigation addressed these gaps collectively by examining the associations between material hardship (ages 1, 3, 5, 9, and 15 years) and white matter connectivity of frontolimbic structures (age of 15 years) in a low-income sample. We applied probabilistic tractography to diffusion imaging data collected from 194 adolescents. Results showed that material hardship related to amygdala–prefrontal, but not hippocampus–prefrontal or hippocampus–amygdala, white matter connectivity. Specifically, hardship during middle childhood (ages 5 and 9 years) was associated with greater connectivity between the amygdala and dorsomedial pFC, whereas hardship during adolescence (age of 15 years) was related to reduced amygdala–orbitofrontal (OFC) and greater amygdala–subgenual ACC connectivity. Growth curve analyses showed that greater increases of hardship across time were associated with both greater (amygdala–subgenual ACC) and reduced (amygdala–OFC) white matter connectivity. Furthermore, these effects remained above and beyond other types of adversity, and greater hardship and decreased amygdala–OFC connectivity were related to increased anxiety and depressive symptoms. Results demonstrate that the associations between material hardship and white matter connections differ across key prefrontal regions and developmental periods, providing support for potential windows of plasticity for structural circuits that support emotion processing.
School connectedness, a construct indexing supportive school relationships, has been posited to promote resilience to environmental adversity. Consistent with prominent calls in the field, we examined the protective nature of school connectedness against two dimensions of early adversity that index multiple levels of environmental exposure (violence exposure, social deprivation) when predicting both positive and negative outcomes in longitudinal data from 3,246 youth in the Fragile Families and Child Wellbeing Study (48% female, 49% African American). Child and adolescent school connectedness were promotive, even when accounting for the detrimental effects of early adversity. Additionally, childhood school connectedness had a protective but reactive association with social deprivation, but not violence exposure, when predicting externalizing symptoms and positive function. Specifically, school connectedness was protective against the negative effects of social deprivation, but the effect diminished as social deprivation became more extreme. These results suggest that social relationships at school may compensate for low levels of social support in the home and neighborhood. Our results highlight the important role that the school environment can play for youth who have been exposed to adversity in other areas of their lives and suggest specific groups that may especially benefit from interventions that boost school connectedness.
Background
Childhood adversity is, unfortunately, highly prevalent and strongly associated with later psychopathology. Recent theories posit that two dimensions of early adversity, threat and deprivation, have distinct effects on brain development. The current study evaluated whether violence exposure (threat) and social deprivation (deprivation) were associated with adolescent amygdala and ventral striatum activation, respectively, in a prospective, well-sampled, longitudinal cohort using a pre-registered, open science approach.
Methods
167 adolescents from the Fragile Families and Child Wellbeing Study completed fMRI scanning. Prospective longitudinal data from ages 3, 5 and 9 were used to create indices of childhood violence exposure and social deprivation. We evaluated whether these dimensions were associated with adolescent brain function in response to threatening and rewarding faces.
Results
Childhood violence exposure was associated with decreased amygdala habituation (i.e., more sustained activation) and activation to angry faces in adolescence, whereas childhood social deprivation was associated with decreased ventral striatum activation to happy faces in adolescence. These associations held when controlling for the other dimension of adversity and their interaction, gender, internalizing psychopathology, and current life stress.
Conclusions
Consistent with recent theories, different forms of early adversity were associated with region-specific differences in brain activation.
Importance:
Adverse childhood experiences are a public health issue with negative sequelae that persist throughout life. Current theories suggest that adverse childhood experiences reflect underlying dimensions (eg, violence exposure and social deprivation) with distinct neural mechanisms; however, research findings have been inconsistent, likely owing to variability in how the environment interacts with the brain.
Objective:
To examine whether dimensional exposure to childhood adversity is associated with person-specific patterns in adolescent resting-state functional connectivity (rsFC), defined as synchronized activity across brain regions when not engaged in a task.
Design, setting, and participants:
A sparse network approach in a large sample with substantial representation of understudied, underserved African American youth was used to conduct an observational, population-based longitudinal cohort study. A total of 183 adolescents aged 15 to 17 years from Detroit, Michigan; Toledo, Ohio; and Chicago, Illinois, who participated in the Fragile Families and Child Wellbeing Study were eligible for inclusion. Environmental data from birth to adolescence were collected via telephone and in-person interviews, and neuroimaging data collected at a university lab. The study was conducted from February 1, 1998, to April 26, 2017, and data analysis was performed from January 3, 2019, to May 22, 2020.
Exposures:
Composite variables representing violence exposure and social deprivation created from primary caregiver reports on children at ages 3, 5, and 9 years.
Main outcomes and measures:
Resting-state functional connectivity person-specific network metrics (data-driven subgroup membership, density, and node degree) focused on connectivity among a priori regions of interest in 2 resting-state networks (salience network and default mode) assessed with functional magnetic resonance imaging.
Results:
Of the 183 eligible adolescents, 175 individuals (98 girls [56%]) were included in the analysis; mean (SD) age was 15.88 (0.53) years and 127 participants (73%) were African American. Adolescents with high violence exposure were 3.06 times more likely (95% CI, 1.17-8.92) to be in a subgroup characterized by high heterogeneity (few shared connections) and low network density (sparsity). Childhood violence exposure, but not social deprivation, was associated with reduced rsFC density (β = -0.25; 95% CI, -0.41 to -0.05; P = .005), with fewer salience network connections (β = -0.26; 95% CI, -0.43 to -0.08; P = .005) and salience network-default mode connections (β = -0.20; 95% CI, -0.38 to -0.03; P = .02). Violence exposure was associated with node degree of right anterior insula (β = -0.29; 95% CI, -0.47 to -0.12; P = .001) and left inferior parietal lobule (β = -0.26; 95% CI, -0.44 to -0.09; P = .003).
Conclusions and relevance:
The findings of this study suggest that childhood violence exposure is associated with adolescent neural network sparsity. A community-detection algorithm, blinded to child adversity, grouped youth exposed to heightened violence based only on patterns of rsFC. The findings may have implications for understanding how dimensions of adverse childhood experiences impact individualized neural development.
Childhood adversity is heterogeneous with potentially distinct dimensions of violence exposure and social deprivation. These dimensions may differentially shape emotion-based neural circuitry, such as amygdala–PFC white matter connectivity. Amygdala–orbitofrontal cortex (OFC) white matter connectivity has been linked to regulation of the amygdala’s response to emotional stimuli. Using a preregistered analysis plan, we prospectively examined the effects of childhood exposure to two dimensions of adversity, violence exposure and social deprivation, on the adolescent amygdala–PFC white matter connectivity. We also reproduced the negative correlation between amygdala–PFC white matter connectivity and amygdala activation to threat faces. 183 15-17-year-olds were recruited from the Fragile Families and Child Wellbeing Study — a longitudinal, birth cohort, sample of predominantly low-income youth. Probabilistic tractography revealed that childhood violence exposure and social deprivation interacted to predict the probability of adolescent right hemisphere amygdala–OFC white matter connectivity. High violence exposure with high social deprivation related to less amygdala–OFC white matter connectivity. Violence exposure was not associated with white matter connectivity when social deprivation was at mean or low levels (i.e., relatively socially supportive contexts). Therefore, social deprivation may exacerbate the effects of childhood violence exposure on the development of white matter connections involved in emotion processing and regulation. Conversely, social support may buffer against them.
Childhood adversity is heterogeneous with potentially distinct dimensions of violence exposure and social deprivation. These dimensions may differentially shape emotion-based neural circuitry, such as amygdala–PFC white matter connectivity. Amygdala–orbitofrontal cortex (OFC) white matter connectivity has been linked to regulation of the amygdala’s response to emotional stimuli. Using a preregistered analysis plan, we prospectively examined the effects of childhood exposure to two dimensions of adversity, violence exposure and social deprivation, on the adolescent amygdala–PFC white matter connectivity. We also reproduced the negative correlation between amygdala–PFC white matter connectivity and amygdala activation to threat faces. 183 15-17-year-olds were recruited from the Fragile Families and Child Wellbeing Study — a longitudinal, birth cohort, sample of predominantly low-income youth. Probabilistic tractography revealed that childhood violence exposure and social deprivation interacted to predict the probability of adolescent right hemisphere amygdala–OFC white matter connectivity. High violence exposure with high social deprivation related to less amygdala–OFC white matter connectivity. Violence exposure was not associated with white matter connectivity when social deprivation was at mean or low levels (i.e., relatively socially supportive contexts). Therefore, social deprivation may exacerbate the effects of childhood violence exposure on the development of white matter connections involved in emotion processing and regulation. Conversely, social support may buffer against them.
... For example, in examining the brain as a mediator (Figure 3), greater neighborhood disadvantage was linked to worse response inhibition via prefrontal activation during a cognitive control task . As another example, greater household instability in childhood predicted depressive symptoms in young adulthood via more efficient neural information flow during adolescence (Hardi, Goetschius, Tillem, et al., 2023). ...
... There were also no associations observed between network metrics and depression at age 17 (before the pandemic), suggesting that these effects could be unique to the circumstance of heightened stress during COVID, or could indicate that these brain-depression findings only emerge later in development when rates of depression continue to increase. Interestingly, we found no association between network connectivity and anxiety symptoms, echoing the potentially differential neural mechanisms relating to anxiety and depression during this period (Hardi et al., 2022b), thus indicating a future direction to further tease apart the distinct etiology preceding these mental health disorders. ...
... But even before Coll et al.'s (1996) recommendations, and likely limited due to patterns of racism within the field, HDFS scholars have produced special issues on the topic of race, child development, and family systems since the late 1970s and early 1980s (Comer, 1985;Peters, 1978) and from that point continued to do so about once per decade until recently (Leman et al., 2017;McLoyd, 1990;Quintana et al., 2006). Although slow, HDFS has increasingly heeded the call to recognize and eradicate racially oppressive assumptions, a push that HDFS scholars of color have long been advocating for (Coll et al., 1996;McLoyd & Randolph, 1984;McLoyd, 2006aMcLoyd, , 2022Umaña-Taylor & Hill, 2020). ...
... Still, the majority of studies are consistent with general research that has investigated socioenvironmental impacts on brain development in general and have shown that early chronic stress (e.g., childhood maltreatment) is associated with accelerated maturation of brain volume and cortical thickness that contribute to altered neurobiology in adulthood (Teicher et al., 2003). In addition, studies on socioeconomic disadvantage have shown that this chronic stress is initially associated with earlier maturation of fronto-limbic circuitry and greater sensory network integration (Rakesh et al., 2021;Noble et al., 2012;Hardi et al., 2022), even in neonates Brady et al., 2022). ...
... It plays a crucial role in influencing the health and well-being of young individuals. Numerous studies have demonstrated that school connectedness serves as a protective factor against emotional problems, such as depression, anxiety, and other negative emotions experienced by adolescents (Abigail et al., 2012;Goetschius et al., 2021;Yang et al., 2022). However, the relationship between school connectedness and alexithymia has been understudied. ...
... fMRI studies on emotional regulation following childhood maltreatment, also report alterations in the connectivity and activity of neural circuits in the frontal-limbic regions, more specifically in the amygdala and ventral anterior cingulate cortex (ACC) [34]. Differences in impact due to adversity type was again reported, in a longitudinal study, where childhood abuse was associated with increased amygdala activity while childhood neglect with decreased ventral striatum response to happy faces, in adolescents [35]. Other frontal-limbic regions beyond the amygdala that play a crucial role in the cognitive modulation of emotions like the dlPFC [36], and, in the automatic regulation of stress hormones like the hippocampus [37], are also implicated. ...
... Despite such complications presented by attempting to break down complex experiences, recent studies using this framework provide various levels of support for a dimensional approach when evaluating outcomes related to brain function (Sheridan et al., 2017;Young et al., 2022) and connectivity (Chahal et al., 2022;Cheng et al., 2021;Goetschius et al., 2020) as well as behavioral outcomes such as emotion regulation, cognitive control, and psychopathology (Lambert et al., 2017;Miller et al., 2018;Sheridan et al., 2017Sheridan et al., , 2020Young et al., 2022). Reviews of studies that solely examined one dimension or another suggest that threat and deprivation are each uniquely associated with brain structure (Colich et al., 2020;McLaughlin et al., 2019). ...
... The role of OFC activity and OFC-amygdala connectivity in punishment and fear-related learning has also been well documented in animals (Bukalo et al., 2015;Hsieh and Chang, 2020;Ma et al., 2020;Shih and Chang, 2021). In humans, structural OFC-amygdala connectivity has been shown to decrease following ELS (Goetschius et al., 2020). Functional connectivity between parts of the mPFC and with both amygdala and OFC correlate with successful emotion regulation (Banks et al., 2007). ...
... A major contributor to these health conditions may be the biological cascade of high allostatic load (Rogosch et al., 2011), including cortisol production, brain development, and connectivity in various regions (Aiyer et al., 2014;Cará et al., 2019;Demir-Lira et al., 2016;Goetschius et al., 2020;Peckins et al., 2012). There is some work suggesting that allostatic load leads to chronic inflammation of the neuroimmune network which could lead to both physical and mental health outcomes (Nusslock & Miller, 2016). ...
... In addition to temperament characteristics, family economic stress can also impact on child's emotional and behavioural problems. Studies have shown that children from low-SES families exhibit elevated levels of internalizing and externalizing symptoms (Bøe et al., 2012;Gard et al., 2020). Given that children who exhibit a symptom profile of being high on both internalizing and externalizing dimensions are severely maladjusted (Willner et al., 2016), would these children's familial SES be much lower than children with other configurations (i.e. ...
![Fig. 3. Path model testing associations among early instability, other types of childhood adversity, and adolescent structural networks. Associations between instability and global efficiency remained (b* [SE] = 0.183 [0.077], p = .017) even after adjusting for other types of early adversity (i.e., harsh parenting, neglect, food insecurity). Additionally, harsh parenting was also associated with greater transitivity (b* [SE] = 0.312 [0.142], p = .029). Model was adjusted for demographic covariates (gender, ethnoracial identity, birth city, puberty, economic hardship) and had excellent fit (CFI = 0.986; TLI = 0.968; RMSEA = 0.036; SRMR = 0.042). Standardized coefficients are shown, and dotted path lines indicate non-significant estimated paths.](https://www.researchgate.net/publication/370667373/figure/fig2/AS:11431281164822055@1685903052567/Path-model-testing-associations-among-early-instability-other-types-of-childhood_Q320.jpg)
![Fig. 4. Early household instability indirectly related to depression at young adulthood via adolescent structural network efficiency. Childhood instability was related to greater structural network efficiency (b*[SE] = 0.192 [0.077], p = .013), which in turn related to greater depressive symptoms at young adulthood (b*[SE] = 0.523 [0.168], p = .002). Global efficiency indirectly explains the association between instability and depression (b*[SE] = 0.100 [0.049], p = .042). Model had excellent fit (CFI = 0.987; TLI = 0.957; RMSEA = 0.035; SRMR = 0.039) and was adjusted for all covariates (gender, ethnoracial identity, birth city, puberty, economic hardship, harsh parenting, neglect, food insecurity). Standardized coefficients are shown, and dotted path lines indicate non-significant estimated paths.](https://www.researchgate.net/publication/370667373/figure/fig3/AS:11431281164831942@1685903052647/Early-household-instability-indirectly-related-to-depression-at-young-adulthood-via_Q320.jpg)
![Neural networks derived during an emotion processing task. (A) S‐GIMME derived group level, subgroup level, and illustrative individual‐level connections. Nodes shown are as follows: amygdala (Am; gray), dorsal anterior cingulate cortex (dAC; yellow), dorsomedial prefrontal cortex (dm; green), insula (Ins; blue), orbitofrontal cortex (OF; dark red), subgenual anterior cingulate cortex (sg; dark blue), and ventral striatum (VS; purple). Eighty (N = 80) individuals were clustered into Subgroup A, whereas 94 (N = 94) individuals were clustered into Subgroup B. Group‐level paths (connections present in at least 75% of the entire sample) are shown in black; subgroup paths (connections present in at least 50% of individuals in each subgroup) are shown in red (Subgroup A) and blue (Subgroup B). Thresholds were default parameters used in connectivity and subgrouping estimation based on large‐scale simulations. All connections were positive on average, in exception for left dorsomedial prefrontal cortex (dm) to right insula (Ins) Subgroup B path (all average path estimates reported in Table S8). (B) Network density (i.e., the proportion of actual contemporaneous connections from the number of possible connections in a network) for each individual in Subgroup A (red) and Subgroup B (blue). Network density was significantly greater in Subgroup A compared with Subgroup B (MA = .36, SDA = .05; MB = .30, SDB = .04; t(147.36) = 8.47, p < .001). (C) Person‐specific network maps (i.e., individual‐level functional connectivity estimated for each individual in the sample) for one individual in Subgroup A (red) and another individual in Subgroup B (blue). L. and R. indicate left/right hemisphere. Subgroup A individual had a more heterogeneous network, with more connections beyond group‐ and subgroup‐level connections, whereas Subgroup B individual had a more homogenous network, with fewer connections overall but more similar connections to the group‐ and subgroup‐level patterns. All edges shown were contemporaneous, and figures were created using customized R codes and circlize package (Gu, Gu, Eils, Schlesner, & Brors, 2014) [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/366674143/figure/fig1/AS:11431281254722803@1719266596047/Neural-networks-derived-during-an-emotion-processing-task-A-S-GIMME-derived-group_Q320.jpg)
![Node centrality across each ROI plotted for each subgroup. ***Bonferroni‐corrected p < .001, **Bonferroni‐corrected p < .01, *Bonferroni‐corrected p < .05. Left to right: amygdala (Amyg), dorsal anterior cingulate (dACC), dorsomedial prefrontal cortex (dmPFC), insula, orbitofrontal (OFC), subgenual anterior cingulate (sgACC), ventral striatum (VS). Hemispheres denoted by R. and L. Compared with Subgroup B (blue), Subgroup A (red) shows significantly greater node centrality, specifically in the left amygdala (L.Amyg), left striatum (L.VS), and right subgenual anterior cingulate (R.sgACC). In contrast, Subgroup B shows greater node centrality in the left dorsal anterior cingulate (L.dACC) and bilateral insula (R.Insula, L.Insula). p Values were Bonferroni‐corrected for multiple comparisons (Table S5) [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/366674143/figure/fig2/AS:11431281254678903@1719266619180/Node-centrality-across-each-ROI-plotted-for-each-subgroup-Bonferroni-corrected_Q320.jpg)
![Anxiety and depressive symptoms across three waves. (A) Illustration of timepoints and ages at each wave of data collection. (B) Anxiety and depression for each subgroup (A: more heterogeneous network with greater centrality in the amygdala, subgenual, and striatum and B: relatively sparser network with greater centrality in the insula and dorsal anterior cingulate) across each wave. Participants across subgroups did not differ in initial anxiety and depression at wave 1, but symptoms began to diverge at wave 2, which persisted through wave 3. For anxiety, this divergence was exacerbated by COVID‐19 at wave 3, whereas subgroup difference for depression during COVID‐19 remained similar to prepandemic difference. Each point represents mean values, and the bars indicate standard errors [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/366674143/figure/fig3/AS:11431281254696111@1719266633752/Anxiety-and-depressive-symptoms-across-three-waves-A-Illustration-of-timepoints-and_Q320.jpg)
![Differential effects of COVID‐19 economic adversity on anxiety and depression across neural‐based subgroups. Symptoms during COVID‐19 (wave 3) were elevated as a function of COVID‐19 economic adversity, especially for subgroup A. Subgroup–adversity interaction was significant for anxiety (b = .275, 95% CI = [.470, .080], p = .006), but not depression (b = .175, 95% CI = [−.026, .376], p = .088). Subgroup A slope is depicted in red and Subgroup B slope in blue. COVID‐19 adversity scores were mean‐centered to aid interpretation. LEFT: Subgroup–adversity interaction for anxiety symptoms. Subgroup A slope (b = .366, 95% CI = [.218, .514], p < .001); Subgroup B slope (b = .092, 95% CI = [−.035, .219], p = .154). RIGHT: Subgroup–adversity interaction for depressive symptoms. Subgroup A slope (b = .304, 95% CI = [.151, .457], p < .001); Subgroup B slope (b = .129, 95% CI = [−.001, .260], p = .053) [Color figure can be viewed at wileyonlinelibrary.com]](https://www.researchgate.net/publication/366674143/figure/fig4/AS:11431281254678914@1719266638142/Differential-effects-of-COVID-19-economic-adversity-on-anxiety-and-depression-across_Q320.jpg)
![Figure 4: Plot illustrating the interaction between childhood violence exposure and social deprivation (ages 3, 5, 9) in predicting the probability of white matter connectivity between the amygdala and orbitofrontal cortex (OFC -Brodmann's Area 47) in the right hemisphere (adolescence). The continuous moderator (social deprivation) has been plotted at a +/-1 standard deviation (SD) interval. A Johnson-Neyman interval shows that violence exposure and white matter connectivity are significantly, inversely correlated when social deprivation = 0.78 and greater. The range of social deprivation values (zero-centered where 0 is the mean) in the data are [-0.76 2.67]. This figure illustrates that at relatively high values of social deprivation, violence exposure and likelihood of amygdala-OFC connectivity are negatively correlated.](https://www.researchgate.net/publication/343906890/figure/fig3/AS:929126366068736@1598532396030/Plot-illustrating-the-interaction-between-childhood-violence-exposure-and-social_Q320.jpg)
