Faculty Bibliography

Objectives: Poor access to necessary outpatient psychiatric care and therapeutic support services increasingly drive youth with autism and their families to the emergency department (ED) to seek psychiatric care, yet EDs nationwide have not risen to meet this increased demand. Patients with autism often require 1:1 monitoring, require restraint and seclusion more frequently than their peers who do not suffer from autism, and overall can be costly and difficult for EDs to manage. The dearth of outpatient resources for patients with autism makes discharge from the ED difficult, yet few inpatient units will accept these complex patients. Insurance companies often reimburse poorly or not at all for ED visits for behavioral complaints. Unlike specialized inpatient psychiatric units that have contracted increased rates for their high-needs patients, EDs, in general, receive no additional reimbursement for caring for youth with autism, despite their complexity. EDs that seek to provide more specialized care for youth with autism face not only administrative challenges, including the challenges of cross-departmental collaboration within the institution (including psychiatry, pediatrics, emergency medicine, occupational therapy, and child life), but also the difficulty of collaboration across different service systems that assist patients with autism. This program will review the service system challenges faced by children with autism and their families and by those looking to improve emergency care for these youth as well as strategies to overcome these barriers. Methods: This presentation will review administrative and cross-service challenges that forces patients with autism into the ED and that complicate efforts to care for them there. The presentation then will identify strategies for overcoming these hurdles by using cross-systems collaboration and advocacy. Results: Participants will gain tools to more effectively implement enhanced services for youth with autism in the ED. Conclusions: Although many barriers to improving emergency psychiatric care for youth with autism exist, they are not insurmountable with strong administrative leadership and advocacy

Objectives: Most staff in a psychiatric emergency service, even CAPs, have little experience with the emergency evaluation, crisis management, and behavioral treatment of patients with autism. This presentation will teach participants about the evaluation and behavioral management of patients with autism in a child/adolescent psychiatric emergency setting through staff training to improve care for these patients. Methods: This presentation will cover autism-specific evaluation strategies and tools geared to a psychiatric emergency setting for children and adolescents. This evaluation, as well as behavioral interventions and staff trainings, part of a larger Autism Clinical Care Pathway, will be described in depth. Results: Knowledge of specific evaluation concepts and strategies can be extremely useful in a child/adolescent psychiatric emergency setting, both in terms of acquiring a better understanding of patients' immediate treatment needs and strategies for behaviorally managing this population. Multidisciplinary staff training on the basics of autism, behavioral management, and staff collaboration is an important part of promoting improved care for youth with autism in a psychiatric emergency service setting. Conclusions: Given the difficulties managing patients with autism in an emergency setting, specific evaluation procedures and behavioral interventions can be beneficial. Moreover, this information can be conveyed through staff training in a way that is feasible and promotes safety

BACKGROUND: Parent training is recommended as the first-line treatment for attention-deficit/hyperactivity disorder (ADHD) in preschool children. The New Forest Parenting Programme (NFPP) is an evidence-based parenting program developed specifically to target preschool ADHD. OBJECTIVE: The objective of this trial is to investigate whether the NFPP can be effectively delivered for children referred through official community pathways in everyday clinical practice. METHODS: A multicenter randomized controlled parallel arm trial design is employed. There are two treatment arms, NFPP and treatment as usual. NFPP consists of eight individually delivered parenting sessions, where the child attends during three of the sessions. Outcomes are examined at three time points (T1, T2, T3): T1 (baseline), T2 (week 12, post intervention), and T3 (6 month follow/up). 140 children between the ages of 3-7, with a clinical diagnosis of ADHD, informed by the Development and Well Being Assessment, and recruited from three child and adolescent psychiatry departments in Denmark will take part. Randomization is on a 1:1 basis, stratified for age and gender. RESULTS: The primary endpoint is change in ADHD symptoms as measured by the Preschool ADHD-Rating Scale (ADHD-RS) by T2. Secondary outcome measures include: effects on this measure at T3 and T2 and T3 measures of teacher reported Preschool ADHD-RS scores, parent and teacher rated scores on the Strength & Difficulties Questionnaire, direct observation of ADHD behaviors during Child's Solo Play, observation of parent-child interaction, parent sense of competence, and family stress. Results will be reported using the standards set out in the Consolidated Standards of Reporting Trials Statement for Randomized Controlled Trials of nonpharmacological treatments. CONCLUSIONS: The trial will provide evidence as to whether NFPP is a more effective treatment for preschool ADHD than the treatment usually offered in everyday clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT01684644; https://clinicaltrials.gov/ct2/show/NCT01684644?term= NCT01684644&rank=1 (Archived by WebCite at http://www.webcitation/6eOOAe8Qe).

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: Multiple sources of evidence, including functional neuroimaging, have supported models of autism spectrum disorder (ASD) as a condition characterized by abnormal connections among brain regions. Yet, both the complexity of the brain connectome and the striking heterogeneity of ASD have hampered efforts to specify the nature of putative dysconnections. For example, while resting state functional resonance imaging (R-fMRI) studies have substantiated the dysconnection model of ASD, notable variation exists in findings of hyper vs. hypo connections53, 61 and on the extent of the circuitry involved. To address these challenges, the Autism Brain Imaging Data Exchange (ABIDE) in 2012 openly shared 1112 R-fMRI scans of individuals with ASD and typical controls (TC). Initial analyses of this dataset-now termed ABIDE I-demonstrated the feasibility of analyzing such an aggregate, and weighed in on the controversy regarding the contribution of hypo-vs. hyperconnections (Di Martino et al, 2014). Results from whole-brain intrinsic functional connectivity (iFC) analyses of 360 ASD vs. 403 TC revealed that both hypo and hyperconnectivity existed in ASD, but they varied as a function of the circuits involved. Hypoconnectivity mostly encompassed cortico-cortical circuits, while hyperconnectivity was primarily involved on sensory motor circuits anchored on subcortical regions (e.g., thalamus). Here, we aim to explore the extent to which these findings can be replicated in ABIDE II-a recently released multi-site dataset of over 1000 new independent datasets (ASD N = 487; TC N = 557). Methods: Consistent with the initial ABIDE I study, we selected R-fMRI and corresponding structural imaging data from males with 1) full-scale IQ (FIQ) within 2 s.d. of the overall new sample mean (111 +/- 15), 2) anatomical images providing near full brain coverage and successful registration, and 3) functional data motion within 2 s.d. of the sample mean (i.e., mean framewise displacement [FD] 0.08 +/- 0.46 mm). Finally, we included only data from sites with FIQ available for at least 75%/diagnostic group and with at least 10 participants per diagnostic group after the above exclusions. This yielded a total of 637 individual datasets across 13 sites (N = 298 ASD and N = 339 TC; groups matched for M age; M age across data = 16 +/- 10 yrs). Standard preprocessing including removal of 24 motion derivatives and nuisance regression using Compcor was conducted via the Configurable Pipeline for the Analysis of Connectomes. We examined whole-brain iFC of the parcellation units defined by the Harvard-Oxford Atlas. To do so, we extracted the mean time series for each of the 110 units from the preprocessed time series data in MNI space and calculated whole-brain iFC matrices using Pearson's correlations. Group comparisons accounted for age, FIQ, site, mean FD and global mean connectivity. Multiple comparisons were controlled for by applying the false discovery rate (qo0.05). Results: Similar to prior findings obtained in ABIDE I, analyses in ABIDE II suggested that both hypo-and hyperconnectivity coexisted in ASD, and varied as a function of the circuit involved. The number of connections exhibiting ASD-related hyperconnectivity in the ABIDE sample was similar to that previously found in ABIDE I. A similar localization to subcortical connections was also revealed. In particular, iFC between each, the thalamus and globus pallidus, and primary parietal sensorimotor regions was significantly increased in ASD relative to TC. While findings of ASD-related hypoconnectivity for the ABIDE II sample were similar to those previously reported for ABIDE I with respect to their localization to cortico-cortical iFC, they were notably less prominent. Conclusions: Leveraging ABIDE II we were able to identify an appropriately sized replication subsample to examine the reproducibility of the ASD-related iFC patterns previously observed using ABIDE I. Findings of subcortical hyperconnectivity with sensorimotor circuitry, were robustly replicated. They highlight the relevance of sensorimotor processes in ASD, which are commonly underexplored in the literature. In contrast, findings regarding hypoconnectivity, a prominent theme in the literature, were notably less robust, though similar in spatial distribution. Future efforts would benefit from parsing potential sources of heterogeneity in this novel resource, which may have limited the extent reproducibility of prior findings

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

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: Methylphenidate and atomoxetine are commonly prescribed for treating attention deficit hyperactivity disorder (ADHD). However, their therapeutic neural mechanisms remain unclear. METHOD: After baseline evaluation including cognitive testing of the Cambridge Neuropsychological Test Automated Battery (CANTAB), drug-naive children with ADHD (n = 46), aged 7-17 years, were randomly assigned to a 12-week treatment with methylphenidate (n = 22) or atomoxetine (n = 24). Intrinsic brain activity, including the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo), was quantified via resting-state functional magnetic resonance imaging at baseline and week 12. RESULTS: Reductions in inattentive symptoms were related to increased fALFF in the left superior temporal gyrus and left inferior parietal lobule for ADHD children treated with methylphenidate, and in the left lingual gyrus and left inferior occipital gyrus for ADHD children treated with atomoxetine. Hyperactivity/impulsivity symptom reductions were differentially related to increased fALFF in the methylphenidate group and to decreased fALFF in the atomoxetine group in bilateral precentral and postcentral gyri. Prediction analyses in the atomoxetine group revealed negative correlations between pre-treatment CANTAB simple reaction time and fALFF change in the left lingual gyrus and left inferior occipital gyrus, and positive correlations between pre-treatment CANTAB simple movement time and fALFF change in bilateral precentral and postcentral gyri and left precuneus, with a negative correlation between movement time and the fALFF change in the left lingual gyrus and the inferior occipital gyrus. CONCLUSIONS: Our findings suggest differential neurophysiological mechanisms for the treatment effects of methylphenidate and atomoxetine in children with ADHD.

The present longitudinal study examined cultural adaptation (i.e., acculturation and enculturation) and its correlates in a sample of 189 Mexican and Dominican immigrant women. Acculturation and enculturation were measured within the domains of language competence, identity and cultural knowledge at two time points over a one-year period. Across groups and domains, cultural adaptation was generally stable over time; only American cultural knowledge showed change, and only for MA women. Several correlates of cultural adaptation were identified. For Mexican women, living in poverty and in immigrant-dense neighborhoods was associated with lower acculturation. For Dominican women, age at immigration was the most robust correlate and was associated with more acculturation and less enculturation, though poverty and neighborhood characteristics emerged as significant for Dominican women too. Findings are consistent with the notion of cultural adaptation as a complex construct that is influenced by cultural context as well as individual immigrant characteristics.