Faculty Bibliography

Youth with autism spectrum disorder (ASD) are psychiatrically hospitalized at high rates. Though specialized psychiatric units are effective, few specialized units exist. The ASD Care Pathway (ASD-CP) was developed as a scalable approach to improving care in general psychiatric units through staff training and a package of autism-specific intervention strategies. An evaluation of the effectiveness of the ASD-CP in a public hospital child psychiatric service compared 18 months (n = 17) versus 18 months (n = 20) post implementation. Average length of hospital stay decreased 40% (22.4-13.4 days) and use of crisis interventions decreased 77% (holds/restraints; 0.65/day to 0.15/day), though each result only approached statistical significance (p = 0.07; 0.057). This study provides preliminary evidence for improved outcomes after implementation of an ASD-CP.

Suicide is a serious public health concern in the US, especially for those served in outpatient behavioral health. Over the past decade, there has been a dramatic increase in US suicide rates, and a significant proportion of those dying by or attempting suicide were treated in outpatient behavioral healthcare within the prior year. In response, the US Action Alliance released the National Strategy for Suicide Prevention in 2012, a key tenet of which is the "Zero Suicide" (ZS) model. ZS provides resources for administrators and providers to create a systematic approach to quality improvement for suicide prevention in healthcare systems via seven essential elements (Lead, Train, Identify, Engage, Treat, Transition, Improve). In this paper, we describe the ZS model, as well as our operationalization of the model in an NIMH-funded study in ~170 free-standing New York State outpatient behavioral health clinics, serving >80,000 patients. This study is the largest implementation and evaluation of the ZS approach ever conducted in outpatient behavioral health. Evaluation of ZS implementation in "real-world" clinical settings will provide crucial insight regarding broader dissemination and inform how to best adopt empirically-supported care for suicidal patients in outpatient behavioral health, thereby reducing tragic and preventable loss of life.

Many empathy tasks lack ecological validity due to their use of simplistic stimuli and static analytical approaches. Empathic accuracy tasks overcome these limitations by using autobiographical emotional video clips. Usually, a single measure of empathic accuracy is computed by correlating the participants' continuous ratings of the narrator's emotional state with the narrator's own ratings. In this study, we validated a modified empathic accuracy task. A valence-independent rating of the narrator's emotional intensity was added to provide comparability between videos portraying different primary emotions and to explore changes in neural activity related to variations in emotional intensity over time. We also added a new neutral control condition to investigate general emotional processing. In the scanner, 34 healthy participants watched 6 video clips of people talking about an autobiographical event (2 sad, 2 happy and 2 neutral clips) while continuously rating the narrator's emotional intensity. Fluctuation in perceived emotional intensity correlated with activity in brain regions previously implicated in cognitive empathy (bilateral superior temporal sulcus, temporoparietal junction, and temporal pole) and affective empathy (right anterior insula and inferior frontal gyrus). When emotional video clips were compared to neutral video clips, we observed higher activity in similar brain regions. Empathic accuracy, on the other hand, was only positively related to activation in regions that have been implicated in cognitive empathy. Our modified empathic accuracy task provides a new method for studying the underlying components and dynamic processes involved in empathy. While the task elicited both cognitive and affective empathy, successful tracking of others' emotions relied predominantly on the cognitive components of empathy. The fMRI data analysis techniques developed here may prove valuable in characterising the neural basis of empathic difficulties observed across a range of psychiatric conditions.

Concomitant cannabis and nicotine use is more prevalent than cannabis use alone; however, to date, most of the literature has focused on associations of isolated cannabis and nicotine use limiting the generalizability of existing research. To determine differential associations of concomitant use of cannabis and nicotine, isolated cannabis use and isolated nicotine use on brain network connectivity, we examined systems-level neural functioning via independent components analysis (ICA) on resting state networks (RSNs) in cannabis users (CAN, n = 53), nicotine users (NIC, n = 28), concomitant nicotine and cannabis users (NIC + CAN, n = 26), and non-users (CTRL, n = 30). Our results indicated that the CTRL group and NIC + CAN users had the greatest functional connectivity relative to CAN users and NIC users in 12 RSNs: anterior default mode network (DMN), posterior DMN, left frontal parietal network, lingual gyrus, salience network, right frontal parietal network, higher visual network, insular cortex, cuneus/precuneus, posterior cingulate gyrus/middle temporal gyrus, dorsal attention network, and basal ganglia network. Post hoc tests showed no significant differences between (1) CTRL and NIC + CAN and (2) NIC and CAN users. These findings of differential associations of isolated vs. combined nicotine and cannabis use demonstrate an interaction between cannabis and nicotine use on RSNs. These unique and combined mechanisms through which cannabis and nicotine influence cortical network functional connectivity are important to consider when evaluating the neurobiological pathways associated with cannabis and nicotine use.

Brain tumor patients often experience functional deficits that extend beyond the tumor site. While resting-state functional MRI (rsfMRI) has been used to map such functional connectivity changes in brain tumor patients, the interplay between abnormal tumor vasculature and the rsfMRI signal is still not well understood. Therefore, there is an exigent need for new tools to elucidate how the blood‑oxygenation-level-dependent (BOLD) rsfMRI signal is modulated in brain cancer. In this initial study, we explore the utility of a preclinical model for quantifying brain tumor-induced changes on the rsfMRI signal and resting-state brain connectivity. We demonstrate that brain tumors induce brain-wide alterations of resting-state networks that extend to the contralateral hemisphere, accompanied by global attenuation of the rsfMRI signal. Preliminary histology suggests that some of these alterations in brain connectivity may be attributable to tumor-related remodeling of the neurovasculature. Moreover, this work recapitulates clinical rsfMRI findings from brain tumor patients in terms of the effects of tumor size on the neurovascular microenvironment. Collectively, these results lay the foundation of a preclinical platform for exploring the usefulness of rsfMRI as a potential new biomarker in patients with brain cancer.

BACKGROUND:Asset-based economic empowerment interventions, which take an integrated approach to building human, social, and economic capital, have shown promise in their ability to reduce HIV risk for young people, including adolescent girls, in sub-Saharan Africa. Similarly, community and family strengthening interventions have proven beneficial in addressing mental health and behavioral challenges of adolescents transitioning to adulthood. Yet, few programs aimed at addressing sexual risk have applied combination interventions to address economic stability and mental health within the traditional framework of health education and HIV counseling/testing. This paper describes a study protocol for a 5-year, NIMH-funded, cluster randomized-controlled trial to evaluate a combination intervention aimed at reducing HIV risk among adolescent girls in Uganda. The intervention, titled Suubi4Her, combines savings-led economic empowerment through youth development accounts (YDA) with an innovative family strengthening component delivered via Multiple Family Groups (MFG). METHODS:Suubi4Her will be evaluated via a three-arm cluster randomized-controlled trial design (YDA only, YDA + MFG, Usual Care) in 42 secondary schools in the Central region of Uganda, targeting a total of 1260 girls (ages 15-17 at enrollment). Assessments will occur at baseline, 12, 24, and 36 months. This study addresses two primary outcomes: 1) change in HIV risk behavior and 2) change in mental health functioning. Secondary exploratory outcomes include HIV and STI incidence, pregnancy, educational attainment, financial savings behavior, gender attitudes, and self-esteem. For potential scale-up, cost effectiveness analysis will be employed to compare the relative costs and outcomes associated with each study arm. CONCLUSIONS:Suubi4Her will be one of the first prospective studies to examine the impact and cost of a combination intervention integrating economic and social components to reduce known HIV risk factors and improve mental health functioning among adolescent girls, while concurrently exploring mental health as a mediator in HIV risk reduction. The findings will illuminate the pathways that connect economic needs, mental health, family support, and HIV risk. If successful, the results will promote holistic HIV prevention strategies to reduce risk among adolescent girls in Uganda and potentially the broader sub-Saharan Africa region. TRIAL REGISTRATION/BACKGROUND:Clinical Trials NCT03307226 (Registered: 10/11/17).

Statistical learning-extracting regularities in the environment-may underlie complex social behavior. 124 children, 56 with autism and 68 typically developing, ages 2-8 years, completed a novel visual statistical learning task on an iPad. Averaged together, children with autism demonstrated less learning on the task compared to typically developing children. However, multivariate classification analyses characterized individual behavior patterns, and demonstrated a subset of children with autism had similar learning patterns to typically developing children and that subset of children had less severe autism symptoms. Therefore, statistically averaging data resulted in missing critical heterogeneity. Variability in statistical learning may help to understand differences in autism symptoms across individuals and could be used to tailor and inform treatment decisions.

The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.

The olfactory sense is a particularly challenging domain for cognitive science investigations of perception, memory, and language. Although many studies show that odors often are difficult to describe verbally, little is known about the associations between olfactory percepts and the words that describe them. Quantitative models of how odor experiences are described in natural language are therefore needed to understand how odors are perceived and communicated. In this study, we develop a computational method to characterize the olfaction-related semantic content of words in a large text corpus of internet sites in English. We introduce two new metrics: olfactory association index (OAI, how strongly a word is associated with olfaction) and olfactory specificity index (OSI, how specific a word is in its description of odors). We validate the OAI and OSI metrics using psychophysical datasets by showing that terms with high OAI have high ratings of perceived olfactory association and are used to describe highly familiar odors. In contrast, terms with high OSI have high inter-individual consistency in how they are applied to odors. Finally, we analyze Dravnieks's (1985) dataset of odor ratings in terms of OAI and OSI. This analysis reveals that terms that are used broadly (applied often but with moderate ratings) tend to be olfaction-unrelated and abstract (e.g., "heavy" or "light"; low OAI and low OSI) while descriptors that are used selectively (applied seldom but with high ratings) tend to be olfaction-related (e.g., "vanilla" or "licorice"; high OAI). Thus, OAI and OSI provide behaviorally meaningful information about olfactory language. These statistical tools are useful for future studies of olfactory perception and cognition, and might help integrate research on odor perception, neuroimaging, and corpus-based linguistic models of semantic organization.

A mixed methods study was conducted to examine the implementation process of 26 urban school-based mental health clinics that took part in a training and implementation support program for an evidence-based school trauma intervention. Implementation process was observed using the Stages of Implementation Completion (SIC) measure. Qualitative interviews were conducted with clinic leaders in order to gain insight into clinic processes related to the SIC. Results showed that almost all of the clinics engaged in some activities related to pre-implementation (engagement, feasibility, and readiness), but only 31% of the sites formally started delivering the program to youth. Completing more pre-implementation activities, particularly those related to readiness, predicted program start-up. Qualitative analysis comparing those that implemented the program to those that did not revealed critical differences in decision-making processes, leadership strategies, and the presence of local champions for the program. This study documented the patterns of clinic behavior that occurs as part of large-scale training efforts, suggests some unique challenges that occur in schools, and highlights the importance of engaging in particular implementation activities (i.e., readiness planning, stakeholder consensus and planning meetings) as part of program start-up. Findings indicate that pre-implementation and readiness-related consultation should be employed as part of broad-scale implementation and training efforts.