Faculty Bibliography

Objectives: The goal of this study is to present the results of one of the largest evaluations of an intervention model for children in foster care aimed to improve care within the services system. This Bridging the Way Home program created processes by which a defined trauma-informed intervention model [trauma systems therapy (TST)] could inform the work of all those involved in the care of a foster child (i.e., clinical and nonclinical providers, foster parents). An overarching aim of this project was to determine whether such concerted care could drive improvements in outcomes. Methods: In this Clinical Perspectives session, we will detail all elements of the Bridging the Way Home program. We will begin with a presentation of the clinical model used in this program, TST, and how it was adapted for foster children. We then will detail how the Bridging the Way Home program was implemented in Kansas, including the process of training all providers and foster parents to provide this care, launching the care teams, and monitoring the quality of care. This implementation trained approximately 430 providers and 516 foster parents. We then will present the evaluation approach that aimed to insert sufficient scientific rigor without sacrificing external validity so that the results would be as generalizable as possible. Results: The evaluation revealed that implementing a trauma-informed approach within a large, complex service system can be achieved successfully, and findings indicate that doing so results in improved mental health and placement stability for the children served. Conclusions: Results indicate that improvements inmentalhealthoutcomesand placement stability were not the result of the delivery of TST by any one individual but, rather, by the concerted provision of care by all those around the child

INTRODUCTION: The purpose of this study was to examine predictors of risk for and the transition between acute stress disorder (ASD) and posttraumatic stress disorder (PTSD) in a longitudinal sample of youth with severe injuries admitted to the hospital. These data would assist with treatment and discharge planning. METHODS: Youth were assessed for ASD during the initial hospital stay and were followed-up over an 18-month period for PTSD (n = 151). Youth were classified into four groups, including Resilient (ASD-, PTSD-), ASD Only (ASD+, PTSD-), PTSD Only (ASD-, PTSD+), and Chronic (ASD+, PTSD+). Demographic, psychiatric, social context, and injury-related factors were examined as predictors of diagnostic transition. RESULTS: The results of multivariate analysis of variance and pairwise comparisons found that peritraumatic dissociation, gender, and socioeconomic status were significant predictors after controlling for multiple testing. DISCUSSION: Results suggest that both within-child and contextual factors contribute to the longitudinal response to trauma in children. Clinicians should consider early screening and discharge planning, particularly for children most at risk.

Background: This presentation details the application of algorithms related to Complex Systems Science/Network Science, Causal Discovery, and Machine Learning Predictive Analytics and Intervention Modeling to understand the emergence and sustenance of Posttraumatic Stress Disorder (PTSD) in acutely traumatized children. There is a great need to develop new computational approaches to understand risk for PTSD given its complex etiology. Our application of these approaches is dedicated to identify children who are at highest risk for PTSD and to identify promising prevention and treatment targets. The research that will be presented examines risk and intervention targets with a longitudinal data set on acutely injured children. Methods: The data set is comprised of information on 163 children aged 7-18 collected as part of a National Institute of Mental Health funded study (R01 MH063247) on risk factors for PTSD in children hospitalized with injuries. The basic design follows: injured children were assessed within hours or days after their hospitalization and reassessed 3 months and 1 year following discharge. The data set includes variables measured during the hospitalization period and at each follow-up and includes domains such as early childhood development, demographics, school and social function, family stress, parent symptoms and functioning, psychosocial stress, qualities and magnitude of injury, candidate genes, neuroendocrine response, psychophysiologic response, and child symptoms and functioning. PTSD was measured with the UCLA PTSD Reaction Index. We apply a unique computational approach called the Complex Systems-Causal Network (CS-CN) method designed to discover sets of variables related to psychiatric disorders that together possess well-known properties of Complex Adaptive Systems (e.g. efficiency of information transfer, modularity, power-law scaling, robustness) and, if such properties are demonstrated, the variables that disproportionally contribute to the systems robust qualities are determined. We then apply Machine Learning Predictive Analytics with Causal Discovery Feature Selection and Intervention Modeling (Pearl's 'Do Calculus') to determine if PTSD can be predicted from variables measured around the time of the trauma, if any of the predictive variables have causal influence on the development of PTSD, and the effect on PTSD if intervention is modeled on any of the discovered causal variables. Results: The CS-CN method revealed a network of 110 variables and 166 bivariate relations that had strong adaptive properties compared with 1000 permutations of a random network. The variables that most contributed to its adaptive properties were CRHR1 gene, FKBP5 gene, age, socioeconomic status, and acute anxiety. Machine Learning analyses revealed an accurate and reliable predictive model for PTSD from variables measured at the time of trauma (AUC =.78) and modeling the influence of change (i.e. Judea Pearl's 'Do Calculus') in several 'remediable' causal variables (e.g. acute pain, pulse rate, anxiety, parent's symptoms of acute stress) led to reduction in PTSD symptoms. Conclusions: New computational methods can lead to reliable and accurate predictive models for PTSD and identify promising prevention and treatment targets

Background: Entropy has a fundamental relationship with information and the functioning of all computational systems. Entropy is defined as the number of states available to a system. A system with low entropy has access to fewer states than does one with high entropy. A system with low entropy is more ordered and more predicable than a system with high entropy. Since entropy is related to the functioning of computational systems, there is an emerging theoretical and empirical literature about its role in brain function and dysfunction. We present the results of three integrated studies applying resting state fMRI entropy measurement to understand intelligence, personality, and psychopathology. Brain entropy is an index of an individual's access to brain states at a given time and is measured through the predictivity of brain state over time. Thus, we would expect to observe brain entropic differences between conditions known to be associated with high flexibility (e.g. high intelligence, creativity, novelty seeking) vs. conditions associated with high rigidity (e.g. anxiety, depression, Posttraumatic Stress). The three studies are: Brain entropy and intelligence in 926 adults from the Brain Genomic Superstruct Project, 2. Brain entropy and personality in 926 adults from the Brain Genomic Superstruct Project, and 3. Brain entropy and PTSD in 95 veterans from the NYU Cohen Veterans Data Set. Methods: Subjects: Study 1 (Entropy and Intelligence) and Study 2 (Entropy and Personality) were conducted with data from the Brain Genomics Superstruct Project (BGSP). The BGSP includes 1570 healthy adult participants between the ages of 18 and 35. The current study utilized data from the 926 participants who completed intelligence and personality assessments. Study 3 (Entropy and PTSD) was conducted with data from the NYU Cohen Veterans Data Set. This data set includes 95 combat veterans, 46 with PTSD and 49 without PTSD. fMRI Procedures: Brain Genomics Superstruct Project (BGSP). All MRI data were obtained with 3T Trio scanners (Siemens Healthcare, Erlangen, Germany) at Harvard University and Massachusetts General Hospital. MRI scans for each participant included a high resolution structural scan (T1-weighted multi-echo MPRAGE, TR = 2.2 sec, TE = 1.5/3.4/5.2/7.0 msec, slices = 144, resolution = 1.2 x 1.2 x 1.2 mm) and a resting-state functional scan sensitive to blood oxygenation level-dependent (BOLD) contrast (TR = 3.0 sec, TE = 30 msec, slices = 47, resolution = 3.0 x 3.0 x 3.0 mm, 120 measurements). NYU Cohen Veterans Data Set: All MRI data were obtained with a 3T Trio scanner (Siemens AG, Erlangen Germany). Anatomical images were acquired with magnetization prepared rapid gradient echo sequence with TE/TI/TR = 2.98/900/2300 ms, 256 x 240 matrix, 256 mm x 240 mm fieldof-view, flip angle = 9degree, slice thickness = 1 mm and total slice number = 191; resting state fMRI was obtained using an echo-planar imaging sequence (TR/TE = 2000/29 ms, flip angle = 90degree), 64 x 64 matrix, pixel size 3.125 mm x 3.125 mm, total slice number = 32, slice thickness = 3.5 mm (without gaps), total volume number = 200. fMRI Entropy Analysis: Brain entropy was calculated using the Brain Entropy Mapping Toolbox (BENtbx) (Wang et al, 2014) for MATLAB (MATLAB Release R2015b, The MathWorks Inc., Natick, MA, United States). The BENtbx utilizes Sample Entropy (SampEn). For a given time series, SampEn is a single number representing the predictability of the series. The entropy of highly predictable series is small, close to 0, indicating a lack of variation or disorder. The entropy of unpredictable series is large, indicating a high amount of variation or disorder. The Sample Entropy process first breaks a series into smaller sets of size m. For example, for m = 2, and the BOLD time series is broken into pairs of consecutive values. Each pair is then compared with every other pair to find the maximum distance (absolute value difference) between any number in the first pair and any number in the second pair. If the distance is less than the threshold r, the two pairs are considered a 'match.' This process is then repeated for sets of size m + 1. Sample Entropy is then the ratio: SampEn =-log A/B: Where, A = number of matches using sets of size m+1 and B = number of matches using sets of size m. For perfectly predictable series, A and B will be equal, and entropy will be 0. As disorder in a series increases, B will become greater than A, and the equation will yield an increasingly large positive number. Psychometric Measurement: Study 1: Intelligence was measured with the Shipley Estimated IQ, Vocabulary, and Matrix Reasoning scales. Study 2: Personality was measured for Behavioral Inhibition, Harm Avoidance, Risk Taking, and Novelty Seeking. Study 3: PTSD was measured with the Clinician Administered PTSD Scale (CAPS). Results: Study 1: Shipley Estimated IQ, Vocabulary, and Matrix Reasoning were all associated with higher brain entropy. In particular, Vocabulary was related to higher entropy in the L fusiform gyrus, inferior temporal gyrus, parahippocampal gyrus. Matrix Reasoning was associated with higher entropy in the bilateral superior, medial, inferior frontal gyrus, bilateral orbital gyrus, and R middle frontal gyrus. Study 2: Harm avoidance and Behavioral Inhibition were associated with lower entropy and Novelty Seeking and Risk Taking were associated with higher entropy. Study 3: PTSD was associated with lower entropy, particularly in the L hippocampus and parahippocampal gyrus, inferior and middle temporal lobes: and higher entropy in the R precuneus, and R parietal lobe. Conclusions: Brain entropy may provide a novel approach to understand intelligence, personality, and psychopathology such as PTSD

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach-the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

This article examines key setting events and personal factors that are associated with support for either non-violent activism or violent activism among Somali refugee young adults in the United States. Specifically, this article examines the associations of trauma, stress, symptoms of posttraumatic stress disorder (PTSD), posttraumatic growth (PTG), strength of social bonds, and attitudes towards legal and non-violent vs. illegal and violent activism. Structured interviews were conducted with a sample of Somali refugee males ages 18-25 living in the northeastern United States (N=79). Data were analyzed using multiple linear regressions and path analysis. Greater exposure to personal trauma was associated with greater openness to illegal and violent activism. PTSD symptoms mediated this association. Strong social bonds to both community and society moderated this association, with trauma being more strongly associated with openness to illegal and violent activism among those who reported weaker social bonds. Greater exposure to trauma, PTG, and stronger social bonds were all associated with greater openness to legal non-violent activism.

Introduction: The objective of this study was to use two interventions to decrease PTSD and PTSD symptomatology in young burned children as measured by the Posttraumatic Stress Disorder Semi-Structured Interview and Observational Report, or PTSDSSI (Sheeringa et al., 1994; 2003). The PTSDSSI was an appropriate instrument for evaluating PTSD in young children because its use elsewhere informed the DSM 5 diagnostic subtype, "PTSD in Children 6 Years and Younger" (APA, 2013). The PTSDSSI also requires parent observation of symptoms appropriate for young children-such as children's posttraumatic play, distressing dreams, withdrawal and irritibility; with a maximal score of 38. The Intervention group in this study received either the DEF (Distress, Emotional Support, and Family Functioning protocol) or the DEF+COPE (Creating Opportunities for Parent Empowerment program). Outcomes of child stress were compared with a Non-Intervention, control group. Methods: Children aged 0-5 years old admitted for an acute burn or for reconstructive surgery and their families, who speak English or Spanish, were eligible. The DEF consists of an initial meeting with caregivers to identify distress or support from an interview and offer clinical referrals for the family, and can be found online on nctsnet.org. The COPE (Melnyk, 2004) provides a workbook to increase parents' knowledge of typical behaviors and emotions children display in hospital and advice on how to participate more directly in child's care. Parents completed the PTSDSSI after their child's admission and at 6 month follow-up, to measure child stress. The 6-month change scores of the PTSDSSI were analyzed for three symptom clusters: re-experiencing(B), numbing/avoidance(C), and hyperarousal(D). Results: Although results did not reach statistical significance in this small sample, there was a clear trend of larger decreases in PTSD symptoms for children in the intervention group as compared to children in the control group. Conclusions: Both forms of intervention appear to have aided in the reduction child stress as measured by the PTSDSSI. Future studies with larger samples should explore both types of intervention. Applicability of Research to Practice: The psychosocial interventions outlined have clear benefits that should be taken into consideration when providing care to children with burns. (Figure presented)