Faculty Bibliography

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

Improvements in the quality of mental health care in the United States depend on the successful implementation of evidence-based treatments (EBT's) in typical settings of care. Unfortunately, there is little evidence that EBT's are used in ways that would approximate their established fidelity standards in such settings. This article describes an approach to more successful implementation of EBT's via a collaborative process between intervention developers and intervention users (e.g. providers, administrators, consumers) called Lead-user Innovation. Lead-user Innovation democratizes the implementation process by integrating the expertise of lead-users in the delivery, adaptation, innovation and evaluation of EBT's.

BACKGROUND: The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. METHODS: ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. RESULTS: Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. CONCLUSIONS: In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

The primary aim of this study was to assess the prevalence of posttraumatic stress disorder (PTSD) in young children hospitalized for burns. A secondary aim was to assess partial PTSD in this population. PTSD diagnosis and symptoms were evaluated utilizing both the diagnostic interview for children and adolescents (DICA-P) module and the PTSD semistructured interview and observational report (PTSDSSI). PTSD symptomatology was assessed from parent interviews at 1 month after discharge from a major pediatric burn center. Four of the 42 participants who completed the DICA-P met full criteria for a diagnosis of PTSD (10%).Of the 39 who also completed the PTSDSSI, 1 (3%) met full criteria for PTSD diagnosis on that measure. Twenty-seven percentage of subjects met partial criteria on the DICA-P vs 16% on the PTSDSSI, without impairment. Furthermore, 67% of subjects met DICA-P criteria for the re-experiencing cluster and 54% met the PTSDSSI re-experiencing criteria. Although only a small percentage met full PTSD diagnostic criteria by either measure, a high percentage of young children with burns manifested some posttraumatic symptoms 1 month after discharge. Because PTSDSSI diagnosis is strongly linked to the diagnostic and statistical manual-5 criteria for "PTSD in children 6 years and younger," these results may offer clues to current diagnoses of PTSD in young children. Future research is needed to improve care by determining the risk factors and course of PTSD to further refine the diagnostic criteria for identifying children most in need of intervention, such as those hospitalized for burn injuries.

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