Faculty Bibliography

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response.

Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition associated with impaired function and increased risk of poor outcomes in children, young people and adults with the condition. Currently approved pharmacological treatments for ADHD include a range of stimulant (methylphenidate, amphetamine) and nonstimulant (atomoxetine, guanfacine, clonidine) medications. All have been shown to be effective in treating the symptoms of ADHD and improving other functional outcomes including quality of life, academic performance, rates of accidents and injuries, and do not appear to be associated with significant adverse outcomes or side effects. In this chapter, we review medications for ADHD by summarising the mechanisms of action of each of the two main classes of compounds (stimulants and nonstimulants), the formulations of the most commonly prescribed medications within each class, their efficacy in treating ADHD symptoms and other outcomes, and other factors that influence treatment decisions including side effects and tolerability, comorbidities and medical history. We conclude with a summary of the treatment decisions made by clinicians and suggest some next steps for research. Further research is needed to understand the mechanisms of action of these medications and how exactly they improve symptoms, and to examine their effects on commonly occurring comorbidities.

Sexual minority men (SMM) are at increased risk for mental health problems due to effects of sexual minority stigma (e.g., internalized homonegativity (IH)). Both IH and emotion dysregulation are contributors to sexual compulsivity; however, the role of feelings of sexual shame have not been examined in this association. A sample of 982 HIV-negative SMM completed online surveys (Mage = 42.4, SD = 13.74). Path analyses indicated significant direct effects of IH on sexual shame (β = 0.44, p < .001), emotion dysregulation (β = 0.19, p < .001), and sexual compulsivity (β = 0.22, p < .001). Modeled simultaneously, the association between sexual shame and sexual compulsivity (β = 0.26, p < .001) was significant, as was the association between emotion dysregulation and sexual compulsivity (β = 0.27, p < .001). Finally, an indirect effect of IH on sexual compulsivity through both sexual shame (p < .001) and emotion dysregulation (p < .001) was significant, and the association between IH and sexual compulsivity was reduced to non-significant (β = 0.01, p = .74). Targeting feelings of sexual shame and emotion dysregulation in clinical interventions may help reduce the negative health impact of sexual compulsivity among SMM.

Climate change has become a global emergency, which mental health effects are increasingly being described and understood. Children and adolescents, especially those in low income countries and minority communities, are particularly vulnerable to experience the worst impacts of climate change now and in the coming decades. Our group of early career mental health clinicians and researchers in nine culturally and socioeconomic different countries across three continents initiated a global, online discussion about the effects of climate change on the mental health of children and adolescents, based on literature and our professional experience. We identified a paucity of research and psychiatric education on the topic, and a need to advance global and local efforts in this direction. We also identified three main domains of mental health impact of climate change: direct, indirect, and through physical conditions. Our work offers a preliminary, up-to-date overview of the consequences of climate change on the mental health of children and adolescents, and provides recommendations to advance policies, public health efforts, research, education, and clinical care in the emerging area of 'Climate Psychiatry'.

Current clinical literature and supporting animal literature have shown that repeated and profound early-life adversity, especially when experienced within the caregiver-infant dyad, disrupts the trajectory of brain development to induce later-life expression of maladaptive behavior and pathology. What is less well understood is the immediate impact of repeated adversity during early life with the caregiver, especially since attachment to the caregiver occurs regardless of the quality of care the infant received including experiences of trauma. The focus of the present manuscript is to review the current literature on infant trauma within attachment, with an emphasis on animal research to define mechanisms and translate developmental child research. Across species, the effects of repeated trauma with the attachment figure, are subtle in early life, but the presence of acute stress can uncover some pathology, as was highlighted by Bowlby and Ainsworth in the 1950s. Through rodent neurobehavioral literature we discuss the important role of repeated elevations in stress hormone corticosterone (CORT) in infancy, especially if paired with the mother (not when pups are alone) as targeting the amygdala and causal in infant pathology. We also show that following induced alterations, at baseline infants appear stable, although acute stress hormone elevation uncovers pathology in brain circuits important in emotion, social behavior, and fear. We suggest that a comprehensive understanding of the role of stress hormones during infant typical development and elevated CORT disruption of this typical development will provide insight into age-specific identification of trauma effects, as well as a better understanding of early markers of later-life pathology.

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.