Faculty Bibliography

Many children in immigrant households endure unique stressors shaped by national, state, and local immigration policies and enforcement activity in the United States. Qualitative studies find that during times of heightened immigration enforcement, children as young as 3 years of age show signs of behavioral distress related to national anti-immigrant sentiment and the possibility of losing a parent. Using multiple sources of data from 168 racially and ethnically diverse families of children in pre-Kindergarten, the present study examined variability in perceived levels of immigration enforcement threat by parental immigrant status and ethnicity. This study examined associations between immigration enforcement threat and child mental health, self-regulation, and executive functioning and whether parent immigrant status or child gender moderates these associations. We found substantial variability in perceived immigration threat, with immigrant parents and Latinx parents reporting significantly greater levels of immigration threat compared to nonimmigrant parents and non-Latinx parents. Immigration enforcement threat was associated with greater child separation anxiety and overanxious behaviors, and lower self-regulation among boys and girls and among children of immigrant and U.S.-born parents. In contrast to our hypothesis, immigration enforcement threat was associated with higher self-regulation according to independent assessor ratings. Educators and healthcare providers working with young children from immigrant and Latinx households should be aware of the disproportionate stress experienced by immigrant and Latinx families due to a xenophobic sociopolitical climate marked by heightened immigration enforcement threat and racist, anti-immigrant rhetoric. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.

Introduction/UNASSIGNED:Interpersonal violent (IPV) experiences when they begin in childhood and continue in various forms during adulthood often lead to chronic post-traumatic stress disorder (PTSD) that is associated in multiple studies with hypocortisolism and lower percentage of methylation of the promoter region of the gene coding for the glucocorticoid receptor (NR3C1). This prospective, longitudinal study examined the relationship of NR3C1 methylation among mothers with IPV-related PTSD and their toddlers and then looked at the relationship of maternal NR3C1 methylation and child psychopathology at school age. Methods/UNASSIGNED:structured clinical interview when their children were ages 12-42 months (mean age 26.7 months, SD 8.8). Their children's psychopathology in terms of internalizing symptoms and externalizing behaviors was evaluated using the Child Behavior Checklist at ages 5-9 years (mean age 7 years, SD 1.1). Percentage of methylation for the NR3C1 gene promoter region was assessed from DNA extracted from maternal and child saliva using bisulfite pyrosequencing. Data analysis involved parametric and non-parametric correlations and multiple linear and logistic regression modeling. Results/UNASSIGNED:Logistic regression models using child NR3C1 methylation as the dependent variable and maternal NR3C1 methylation and PTSD group status as predictors, as well as the interaction indicated that all three of these significantly predicted child NR3C1 methylation. These findings remained significant when controlling for child age, sex and maternal child abuse history. Overall, maternal NR3C1 methylation when children were toddlers was negatively and significantly associated with child externalizing behavior severity at school age. Discussion/UNASSIGNED:We found that correlations between mothers and their children of NR3C1 methylation levels overall and at all individual CpG sites of interest were significant only in the IPV-PTSD group. The latter findings support that NR3C1 methylation in mothers positively and statistically significantly correlates with NR3C1 methylation in their children only in presence of IPV-PTSD in the mothers. This maternal epigenetic signature with respect to this glucocorticoid receptor is significantly associated with child behavior that may well pose a risk for intergenerational transmission of violence and related psychopathology.

Violence-related post-traumatic stress disorder (PTSD) in the context of war and terrorism has become an increasingly pressing public health issue relevant to refugee children and families. PTSD and related psychopathology in children can adversely affect all domains of development and, in particular, interfere with learning and socialization. When the experience of violent trauma and related loss is shared with the entire family, resulting impairment and distress may prevent caregivers from being psychologically available to process their traumatized children's emotional communication and otherwise meet their children's developmental needs. When children suffer from PTSD, it may be impossible to put their experience and related thoughts and feelings into words, let alone a coherent narrative. The latter difficulty can be even more pronounced when the child displays dissociative symptoms, possibly signaling a dissociative subtype of PTSD. Thus, the narrative within the child's play during psychotherapy becomes all the more important as an indicator of the child's internal world. This case report is an example both of evaluation and of psychotherapy that is both psychodynamic and trauma-informed with a 10-year-old Afghani boy who suffered the violent loss of his father at age of 3 years, leading to his immigration to Switzerland. This paper addresses the question of how the psychotherapist can accompany the child through the elaboration of his trauma and how the therapist can contribute to the co-construction of a coherent narrative of the child's experience and to the restoration of an intersubjective connection between the traumatized child and caregiver.

Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient's own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple data domains (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this paper, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplemental materials for this article are available online.