Faculty Bibliography

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R² values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

This study examined how experiences of service denial and discrimination in three health care settings-doctors' offices, emergency rooms, and mental health clinics-might contribute to attempted suicide among transgender adults. Mechanisms of this relationship were examined, including treatment receipt and the use of substances to cope with mistreatment. Perceived emotional social support was also tested as a potential protective factor against the deleterious effects of service denial and discrimination on treatment receipt, substance use, and attempted suicide. The analysis included 4190 respondents from the National Transgender Discrimination Survey. Structural equation modeling was employed to test hypothesized relationships. Being denied a greater number of services and discriminated against in more settings were associated with lower levels of treatment receipt. Service denial was also correlated with increased rates of coping-motivated substance use and elevated rates of attempted suicide. Treatment receipt mediated the relationships between service denial/discrimination and substance use. Substance use mediated the relationship between treatment receipt and attempted suicide. Higher levels of support were protective to treatment receipt when denied services in one setting, but no longer retained protective effects when denied in two or three settings. Results have critical implications for service access and delivery and policies that protect transgender help-seekers in the health care system.

Signaled active avoidance (SigAA) is the key experimental procedure for studying the acquisition of instrumental responses toward conditioned threat cues. Traditional analytic approaches (e.g., general linear model) often obfuscate important individual differences, although individual differences in learned responses characterize both animal and human learning data. However, individual differences models (e.g., latent growth curve modeling) typically require large samples and onerous computational methods. Here, we present an analytic methodology that enables the detection of individual differences in SigAA performance at a high accuracy, even when a single animal is included in the data set (i.e., n = 1 level). We further show an online software that enables the easy application of our method to any SigAA data set.

Treatment response heterogeneity poses serious challenges for selecting treatment for many diseases. To better understand this heterogeneity and to help in determining the best patient-specific treatments for a given disease, many clinical trials are collecting large amounts of patient-level data prior to administering treatment in the hope that some of these data can be used to identify moderators of treatment effect. These data can range from simple scalar values to complex functional data such as curves or images. Combining these various types of baseline data to discover "biosignatures" of treatment response is crucial for advancing precision medicine. Motivated by the problem of selecting optimal treatment for subjects with depression based on clinical and neuroimaging data, we present an approach that both (1) identifies covariates associated with differential treatment effect and (2) estimates a treatment decision rule based on these covariates. We focus on settings where there is a potentially large collection of candidate biomarkers consisting of both scalar and functional data. The validity of the proposed approach is justified via extensive simulation experiments and illustrated using data from a placebo-controlled clinical trial investigating antidepressant treatment response in subjects with depression.

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force created the TASK3 working groups to create common data elements (CDEs) for various aspects of preclinical epilepsy research studies, which could help improve standardization of experimental designs. This article concerns the parameters that can be measured to assess the physiologic condition of the animals that are used to study rodent models of epilepsy. Here we discuss CDEs for physiologic parameters measured in adult rats and mice such as general health status, temperature, cardiac and respiratory function, and blood constituents. We provide detailed CDE tables and case report forms (CRFs), and with this companion manuscript we discuss the monitoring of different aspects of physiology of the animals. The CDEs, CRFs, and companion paper are available to all researchers, and their use will benefit the harmonization and comparability of translational preclinical epilepsy research. The ultimate hope is to facilitate the development of biomarkers and new treatments for epilepsy.

Structural and functional elements of biological systems are highly conserved across vertebrates. Many neurological and psychiatric conditions affect both humans and animals. A cross-species approach to the study of brain and behaviour can advance our understanding of human disorders via the identification of unrecognized natural models of spontaneous disorders, thus revealing novel factors that increase vulnerability or resilience, and via the assessment of potential therapies. Moreover, diagnostic and therapeutic advances in human neurology and psychiatry can often be adapted for veterinary patients. However, clinical and research collaborations between physicians and veterinarians remain limited, leaving this wealth of comparative information largely untapped. Here, we review pain, cognitive decline syndromes, epilepsy, anxiety and compulsions, autoimmune and infectious encephalitides and mismatch disorders across a range of animal species, looking for novel insights with translational potential. This comparative perspective can help generate novel hypotheses, expand and improve clinical trials and identify natural animal models of disease resistance and vulnerability.

Common data elements (CDEs) are becoming more common as more areas of preclinical research have generated CDEs. Herein we provide an overview of the progress to date in generating CDEs for preclinical epilepsy research. Currently there are CDEs that have been developed for Physiology (in vivo), Behavior, Pharmacology, and Electroencephalography (EEG). Together the CDEs and methodologic considerations associated with these CDEs are laid out in consecutive manuscripts published in Epilepsia Open, each describing CDEs for their respective topic area. In addition to the overview of progress for the 4 subjects, core characteristics (Core CDEs) are described and explained. Data collection using a case report form (CRF) is described, and considerations that are involved in using the CDEs and CRFs are discussed.

In this clinical review we summarise what in our view have been some the most important advances in the past two decades, in terms of diagnostic definition, epidemiology, genetics and environmental causes, neuroimaging/cognition and treatment of attention-deficit/hyperactivity disorder (ADHD), including: (1) the most recent changes to the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases; (2) meta-analytic evidence showing that, after accounting for diagnostic methods, the rates of ADHD are fairly consistent across Western countries; (3) the recent finding of the first genome-wide significant risk loci for ADHD; (4) the paradigm shift in the pathophysiological conceptualisation of ADHD from alterations in individual brain regions to a complex dysfunction in brain networks; (5) evidence supporting the short-term efficacy of ADHD pharmacological treatments, with a different profile of efficacy and tolerability in children/adolescents versus adults; (6) a series of meta-analyses showing that, while non-pharmacological treatment may not be effective to target ADHD core symptoms, some of them effectively address ADHD-related impairments (such as oppositional behaviours for parent training and working memory deficits for cognitive training). We also discuss key priorities for future research in each of these areas of investigation. Overall, while many research questions have been answered, many others need to be addressed. Strengthening multidisciplinary collaborations, relying on large data sets in the spirit of Open Science and supporting research in less advantaged countries will be key to face the challenges ahead.

BACKGROUND:Sleep problems are common and impairing in individuals with autism spectrum disorders (ASD). Evidence synthesis including both subjective (ie, measured with questionnaires) and objective (ie, quantified with neurophysiological tools) sleep alterations in youth with ASD is currently lacking. OBJECTIVE:We conducted a systematic review and meta-analysis of subjective and objective studies sleep studies in youth with ASD. METHODS:FINDINGS: From a pool of 3359 non-duplicate potentially relevant references, 47 datasets were included in the meta-analyses. Subjective and objective sleep outcome measures were extracted from 37 and 15 studies, respectively. Only five studies were based on comorbidity free, medication-naïve participants. Compared with typically developing controls, youth with ASD significantly differed in 10/14 subjective parameters and in 7/14 objective sleep parameters. The average quality score in the Newcastle-Ottawa Scale was 5.9/9. DISCUSSION AND CLINICAL IMPLICATIONS/UNASSIGNED:A number of subjective and, to a less extent, objective sleep alterations might characterise youth with ASD, but future studies should assess the impact of pharmacological treatment and psychiatric comorbidities.