Digital devices and continuous telemetry: opportunities for aligning psychiatry and neuroscience

Returning Individual Research Results from Digital Phenotyping in Psychiatry

Psychiatry is rapidly adopting digital phenotyping and artificial intelligence/machine learning tools to study mental illness based on tracking participants' locations, online activity, phone and text message usage, heart rate, sleep, physical activity, and more. Existing ethical frameworks for return of individual research results (IRRs) are inadequate to guide researchers for when, if, and how to return this unprecedented number of potentially sensitive results about each participant's real-world behavior. To address this gap, we convened an interdisciplinary expert working group, supported by a National Institute of Mental Health grant. Building on established guidelines and the emerging norm of returning results in participant-centered research, we present a novel framework specific to the ethical, legal, and social implications of returning IRRs in digital phenotyping research. Our framework offers researchers, clinicians, and Institutional Review Boards (IRBs) urgently needed guidance, and the principles developed here in the context of psychiatry will be readily adaptable to other therapeutic areas.

Translational In Vivo Assays in Behavioral Biology

The failure of preclinical research to advance successful candidate medications in psychiatry has created a paradigmatic crisis in psychiatry. The Research Domain Criteria (RDoC) initiative was designed to remedy this situation with a neuroscience-based approach that employs multimodal and cross-species in vivo methodology to increase the probability of translational findings and, consequently, drug discovery. The present review underscores the feasibility of this methodological approach by briefly reviewing, first, the use of multidimensional and cross-species methodologies in traditional behavioral pharmacology and, subsequently, the utility of this approach in contemporary neuroimaging and electrophysiology research-with a focus on the value of functionally homologous studies in nonhuman and human subjects. The final section provides a brief review of the RDoC, with a focus on the potential strengths and weaknesses of its domain-based underpinnings. Optimistically, this mechanistic and multidimensional approach in neuropsychiatric research will lead to novel therapeutics for the management of neuropsychiatric disorders.

How Can Digital Mental Health Enhance Psychiatry?

The use of digital technologies is constantly growing around the world. The wider-spread adoption of digital technologies and solutions in the daily clinical practice in psychiatry seems to be a question of when, not if. We propose a synthesis of the scientific literature on digital technologies in psychiatry and discuss the main aspects of its possible uses and interests in psychiatry according to three domains of influence that appeared to us: 1) assist and improve current care: digital psychiatry allows for more people to have access to care by simply being more accessible but also by being less stigmatized and more convenient; 2) develop new treatments: digital psychiatry allows for new treatments to be distributed via apps, and practical guidelines can reduce ethical challenges and increase the efficacy of digital tools; and 3) produce scientific and medical knowledge: digital technologies offer larger and more objective data collection, allowing for more detection and prevention of symptoms. Finally, ethical and efficacy issues remain, and some guidelines have been put forth on how to safely use these solutions and prepare for the future.

Differential effects of the stress peptides PACAP and CRF on sleep architecture in mice

Stress produces profound effects on behavior, including persistent alterations in sleep patterns. Here we examined the effects of two prototypical stress peptides, pituitary adenylate cyclase-activating polypeptide (PACAP) and corticotropin-releasing factor (CRF), on sleep architecture and other translationally-relevant endpoints. Male and female mice were implanted with subcutaneous transmitters enabling continuous measurement of electroencephalography (EEG) and electromyography (EMG), as well as body temperature and locomotor activity, without tethering that restricts free movement, body posture, or head orientation during sleep. At baseline, females spent more time awake (AW) and less time in slow wave sleep (SWS) than males. Mice then received intracerebral infusions of PACAP or CRF at doses producing equivalent increases in anxiety-like behavior. The effects of PACAP on sleep architecture were similar in both sexes and resembled those reported in male mice after chronic stress exposure. Compared to vehicle infusions, PACAP infusions decreased time in AW, increased time in SWS, and increased rapid eye movement sleep (REM) time and bouts on the day following treatment. In addition, PACAP effects on REM time remained detectable a week after treatment. PACAP infusions also reduced body temperature and locomotor activity. Under the same experimental conditions, CRF infusions had minimal effects on sleep architecture in either sex, causing only transient increases in SWS during the dark phase, with no effects on temperature or activity. These findings suggest that PACAP and CRF have fundamentally different effects on sleep-related metrics, and provide new insights into the mechanisms by which stress disrupts sleep.

New and emerging approaches to treat psychiatric disorders

Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.

Home-cage behavior in the Stargazer mutant mouse

In many childhood-onset genetic epilepsies, seizures are accompanied by neurobehavioral impairments and motor disability. In the Stargazer mutant mouse, genetic disruptions of Cacng2 result in absence-like spike-wave seizures, cerebellar gait ataxia and vestibular dysfunction, which limit traditional approaches to behavioral phenotyping. Here, we combine videotracking and instrumented home-cage monitoring to resolve the neurobehavioral facets of the murine Stargazer syndrome. We find that despite their gait ataxia, stargazer mutants display horizontal hyperactivity and variable rates of repetitive circling behavior. While feeding rhythms, circadian or ultradian oscillations in activity are unchanged, mutants exhibit fragmented bouts of behaviorally defined "sleep", atypical licking dynamics and lowered sucrose preference. Mutants also display an attenuated response to visual and auditory home-cage perturbations, together with profound reductions in voluntary wheel-running. Our results reveal that the seizures and ataxia of Stargazer mutants occur in the context of a more pervasive behavioral syndrome with elements of encephalopathy, repetitive behavior and anhedonia. These findings expand our understanding of the function of Cacng2.

Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research.

Post-traumatic stress disorder: clinical and translational neuroscience from cells to circuits

Post-traumatic stress disorder (PTSD) is a maladaptive and debilitating psychiatric disorder, characterized by re-experiencing, avoidance, negative emotions and thoughts, and hyperarousal in the months and years following exposure to severe trauma. PTSD has a prevalence of approximately 6-8% in the general population, although this can increase to 25% among groups who have experienced severe psychological trauma, such as combat veterans, refugees and victims of assault. The risk of developing PTSD in the aftermath of severe trauma is determined by multiple factors, including genetics - at least 30-40% of the risk of PTSD is heritable - and past history, for example, prior adult and childhood trauma. Many of the primary symptoms of PTSD, including hyperarousal and sleep dysregulation, are increasingly understood through translational neuroscience. In addition, a large amount of evidence suggests that PTSD can be viewed, at least in part, as a disorder that involves dysregulation of normal fear processes. The neural circuitry underlying fear and threat-related behaviour and learning in mammals, including the amygdala-hippocampus-medial prefrontal cortex circuit, is among the most well-understood in behavioural neuroscience. Furthermore, the study of threat-responding and its underlying circuitry has led to rapid progress in understanding learning and memory processes. By combining molecular-genetic approaches with a translational, mechanistic knowledge of fear circuitry, transformational advances in the conceptual framework, diagnosis and treatment of PTSD are possible. In this Review, we describe the clinical features and current treatments for PTSD, examine the neurobiology of symptom domains, highlight genomic advances and discuss translational approaches to understanding mechanisms and identifying new treatments and interventions for this devastating syndrome.

An Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint

Background

Psychiatry has long needed a better and more scalable way to capture the dynamics of behavior and its disturbances, quantitatively across multiple data channels, at high temporal resolution in real time. By combining 24/7 data—on location, movement, email and text communications, and social media—with brain scans, genetics, genomics, neuropsychological batteries, and clinical interviews, researchers will have an unprecedented amount of objective, individual-level data. Analyzing these data with ever-evolving artificial intelligence could one day include bringing interventions to patients where they are in the real world in a convenient, efficient, effective, and timely way. Yet, the road to this innovative future is fraught with ethical dilemmas as well as ethical, legal, and social implications (ELSI).

Objective

The goal of the Ethics Checklist is to promote careful design and execution of research. It is not meant to mandate particular research designs; indeed, at this early stage and without consensus guidance, there are a range of reasonable choices researchers may make. However, the checklist is meant to make those ethical choices explicit, and to require researchers to give reasons for their decisions related to ELSI issues. The Ethics Checklist is primarily focused on procedural safeguards, such as consulting with experts outside the research group and documenting standard operating procedures for clearly actionable data (eg, expressed suicidality) within written research protocols.

Methods

We explored the ELSI of digital health research in psychiatry, with a particular focus on what we label “deep phenotyping” psychiatric research, which combines the potential for virtually boundless data collection and increasingly sophisticated techniques to analyze those data. We convened an interdisciplinary expert stakeholder workshop in May 2020, and this checklist emerges out of that dialogue.

Results

Consistent with recent ELSI analyses, we find that existing ethical guidance and legal regulations are not sufficient for deep phenotyping research in psychiatry. At present, there are regulatory gaps, inconsistencies across research teams in ethics protocols, and a lack of consensus among institutional review boards on when and how deep phenotyping research should proceed. We thus developed a new instrument, an Ethics Checklist for Digital Health Research in Psychiatry (“the Ethics Checklist”). The Ethics Checklist is composed of 20 key questions, subdivided into 6 interrelated domains: (1) informed consent; (2) equity, diversity, and access; (3) privacy and partnerships; (4) regulation and law; (5) return of results; and (6) duty to warn and duty to report.

Conclusions

Deep phenotyping research offers a vision for vastly more effective care for people with, or at risk for, psychiatric disease. The potential perils en route to realizing this vision are significant; however, and researchers must be willing to address the questions in the Ethics Checklist before embarking on each leg of the journey.

Developing and Validating an Individualized Clinical Prediction Model to Forecast Psychotic Recurrence in Acute and Transient Psychotic Disorders: Electronic Health Record Cohort Study

Acute and transient psychotic disorders (ATPDs) include short-lived psychotic episodes with a high probability of developing psychotic recurrences. Clinical care for ATPD is currently limited by the inability to predict outcomes. Real-world electronic health record (EHR)-based retrospective cohort study STROBE/RECORD compliant included all individuals accessing the South London and Maudsley NHS Trust between 2006 and 2017 and receiving a first diagnosis of ATPD (F23, ICD-10). After imputing missing data, stepwise and LASSO Cox regression methods employing a priori predictors (n = 23) were compared to develop and internally validate an individualized risk prediction model to forecast the risk of psychotic recurrences following TRIPOD guidelines. The primary outcome was prognostic accuracy (area under the curve [AUC]). 3018 ATPD individuals were included (average age = 33.75 years, 52.7% females). Over follow-up (average 1042 ± 1011 days, up to 8 years) there were 1160 psychotic recurrences (events). Stepwise (n = 12 predictors) and LASSO (n = 17 predictors) regression methods yielded comparable prognostic accuracy, with an events per variable ratio >100 for both models. Both models showed an internally validated adequate prognostic accuracy from 4 years follow-up (AUC 0.70 for both models) and good calibration. A refined model was adapted in view of the new ICD-11 criteria on 307 subjects with polymorphic ATPD, showing fair prognostic accuracy at 4 years (AUC: stepwise 0.68; LASSO 0.70). This study presents the first clinically based prediction model internally validated to adequately predict long-term psychotic recurrence in individuals with ATPD. The model can be automatable in EHRs, supporting further external validations and refinements to improve its prognostic accuracy.

Psychiatry in the Digital Age: A Blessing or a Curse?

Social distancing and the shortage of healthcare professionals during the COVID-19 pandemic, the impact of population aging on the healthcare system, as well as the rapid pace of digital innovation are catalyzing the development and implementation of new technologies and digital services in psychiatry. Is this transformation a blessing or a curse for psychiatry? To answer this question, we conducted a literature review covering a broad range of new technologies and eHealth services, including telepsychiatry; computer-, internet-, and app-based cognitive behavioral therapy; virtual reality; digital applied games; a digital medicine system; omics; neuroimaging; machine learning; precision psychiatry; clinical decision support; electronic health records; physician charting; digital language translators; and online mental health resources for patients. We found that eHealth services provide effective, scalable, and cost-efficient options for the treatment of people with limited or no access to mental health care. This review highlights innovative technologies spearheading the way to more effective and safer treatments. We identified artificially intelligent tools that relieve physicians from routine tasks, allowing them to focus on collaborative doctor-patient relationships. The transformation of traditional clinics into digital ones is outlined, and the challenges associated with the successful deployment of digitalization in psychiatry are highlighted.

Nucleus Accumbens Medium Spiny Neuron Subtypes Differentially Regulate Stress-Associated Alterations in Sleep Architecture

BACKGROUND

Stress is implicated in the pathophysiology of major depression and posttraumatic stress disorder. These conditions share core features, including motivational deficits, heighted anxiety, and sleep dysregulation. Chronic stress produces these same features in rodents, with some individuals being susceptible or resilient, as seen in humans. While stress-induced neuroadaptations within the nucleus accumbens are implicated in susceptibility-related dysregulation of motivational and emotional behaviors, their effects on sleep are unclear.

METHODS

We used chemogenetics (DREADDs [designer receptors exclusively activated by designer drugs]) to examine the effects of selective alterations in activity of nucleus accumbens medium spiny neurons expressing dopamine D₁ receptors (D1-MSNs) or dopamine D₂ receptors (D2-MSNs) on sleep-related end points. Mice were implanted with wireless transmitters enabling continuous collection of data to quantify vigilance states over a 20-day test period. Parallel cohorts were examined in behavioral tests assessing stress susceptibility.

RESULTS

D1- and D2-MSNs play dissociable roles in sleep regulation. Stimulation of inhibitory or excitatory DREADDs expressed in D1-MSNs exclusively affects rapid eye movement sleep, whereas targeting D2-MSNs affects slow wave sleep. The combined effects of D1-MSN inhibition and D2-MSN activation on sleep resemble those of chronic social defeat stress. Alterations in D1-MSN function also affect stress susceptibility in social behavior tests. Elevation of CREB (cAMP response element-binding protein) within D1-MSNs is sufficient to produce stress-like effects on rapid eye movement sleep.

CONCLUSIONS

In addition to regulation of motivational and emotional behaviors, the nucleus accumbens also influences sleep, an end point with high translational relevance. These findings provide a neural basis for comorbidity in key features of stress-related illness.

The relationship between conventional clinical assessments and momentary assessments of symptoms and functioning in schizophrenia spectrum disorders: A systematic review

BACKGROUND

Symptoms and functioning are critical dimensions in those with schizophrenia and are typically measured using validated conventional clinical assessments. Researchers and clinicians have begun to use real-time digital methods, such as ecological momentary assessment (EMA), to assess symptoms and functioning in the moment and outside of traditional hospital and laboratory settings, which may yield more naturalistic data. Although digital methods have advantages, it is unclear whether these momentary assessments capture core aspects of symptoms and functioning.

OBJECTIVE

This systematic literature review aimed to evaluate the association between conventional clinical and momentary-based assessments of functioning and symptoms in individuals with schizophrenia.

METHODS

Studies were included if they met the following criteria: (1) written or translated into English; (2) peer-reviewed; (3) included primary quantitative data; (4) 60% of the clinical sample included persons with schizophrenia spectrum disorders; (5) included a clinical assessment of functioning and/or symptoms; (6) included active momentary assessment and/or passive data; and (7) assessed the relationship between the momentary and conventional clinical assessments.

RESULTS

A total of 49 studies (87 analyses) were included. Conventional clinical assessments of functioning and positive, negative, and depressive symptoms were related to momentary assessments of these symptom domains. Passive data was beneficial for assessing negative symptoms, but research is warranted for other domains.

CONCLUSIONS

The reviewed studies highlight the utility of EMA methodologies to collect detailed data on symptoms and functioning. Such data is being used to develop more sophisticated models of schizophrenia to enhance our understanding of important mechanisms and develop targeted interventions.

Toward dynamic phenotypes and the scalable measurement of human behavior

Precision psychiatry demands the rapid, efficient, and temporally dense collection of large scale and multi-omic data across diverse samples, for better diagnosis and treatment of dynamic clinical phenomena. To achieve this, we need approaches for measuring behavior that are readily scalable, both across participants and over time. Efforts to quantify behavior at scale are impeded by the fact that our methods for measuring human behavior are typically developed and validated for single time-point assessment, in highly controlled settings, and with relatively homogeneous samples. As a result, when taken to scale, these measures often suffer from poor reliability, generalizability, and participant engagement. In this review, we attempt to bridge the gap between gold standard behavioral measurements in the lab or clinic and the large-scale, high frequency assessments needed for precision psychiatry. To do this, we introduce and integrate two frameworks for the translation and validation of behavioral measurements. First, borrowing principles from computer science, we lay out an approach for iterative task development that can optimize behavioral measures based on psychometric, accessibility, and engagement criteria. Second, we advocate for a participatory research framework (e.g., citizen science) that can accelerate task development as well as make large-scale behavioral research more equitable and feasible. Finally, we suggest opportunities enabled by scalable behavioral research to move beyond single time-point assessment and toward dynamic models of behavior that more closely match clinical phenomena.

Using the Online Psychotherapy Tool to Address Mental Health Problems in the Context of the COVID-19 Pandemic: Protocol for an Electronically Delivered Cognitive Behavioral Therapy Program

Background

The considerable rise of mental health challenges during the COVID-19 pandemic has had detrimental effects on the public health sector and economy. To meet the overwhelming and growing demand for mental health care, innovative approaches must be employed to significantly expand mental health care delivery capacity. Although it is not feasible to increase the number of mental health care providers or hours they work in the short term, improving their time efficiency may be a viable solution. Virtually and digitally delivering psychotherapy, which has been shown to be efficient and clinically effective, might be a good method for addressing this growing demand.

Objective

This research protocol aims to evaluate the feasibility and efficacy of using an online, digital, asynchronous care model to treat mental health issues that are started or aggravated by stressors associated with the COVID-19 pandemic.

Methods

This nonrandomized controlled trial intervention will be delivered through the Online Psychotherapy Tool, a secure, cloud-based, digital mental health platform. Participants will be offered a 9-week electronically delivered cognitive behavioral therapy program that is tailored to address mental health problems in the context of the COVID-19 pandemic. This program will involve weekly self-guided educational material that provides an overview of behavioral skills and weekly homework. Participants (N=80) will receive personalized feedback from and weekly interaction with a therapist throughout the course of the program. The efficacy of the program will be evaluated using clinically validated symptomology questionnaires, which are to be completed by participants at baseline, week 5, and posttreatment. Inclusion criteria includes the capacity to consent; a primary diagnosis of generalized anxiety disorder or major depressive disorder, with symptoms that started or worsened during the COVID-19 pandemic; the ability to speak and read English; and consistent and reliable access to the internet. Exclusion criteria includes active psychosis, acute mania, severe alcohol or substance use disorder, and active suicidal or homicidal ideation.

Results

This study received funding in May 2020. Ethics approval was received in June 2020. The recruitment of participants began in June 2020. Participant recruitment is being conducted via social media, web-based communities, and physician referrals. To date, 58 participants have been recruited (intervention group: n=35; control group: n=23). Data collection is expected to conclude by the end of 2020. Analyses (ie, linear regression analysis for continuous outcomes and binomial regression analysis for categorical outcomes) are expected to be completed by February 2021.

Conclusions

If proven feasible, this care delivery method could increase care capacity by up to fourfold. The findings from this study can potentially influence clinical practices and policies and increase accessibility to care during the COVID-19 pandemic, without sacrificing the quality of care.

Trial Registration

ClinicalTrials.gov NCT04476667; https://clinicaltrials.gov/ct2/show/NCT04476667

International Registered Report Identifier (IRRID)

DERR1-10.2196/24913

Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: external replication in 2,430,333 US patients

The real-world impact of psychosis prevention is reliant on effective strategies for identifying individuals at risk. A transdiagnostic, individualized, clinically-based risk calculator to improve this has been developed and externally validated twice in two different UK healthcare trusts with convincing results. The prognostic performance of this risk calculator outside the UK is unknown. All individuals who accessed primary or secondary health care services belonging to the IBM® MarketScan® Commercial Database between January 2015 and December 2017, and received a first ICD-10 index diagnosis of nonorganic/nonpsychotic mental disorder, were included. According to the risk calculator, age, gender, ethnicity, age-by-gender, and ICD-10 cluster diagnosis at index date were used to predict development of any ICD-10 nonorganic psychotic disorder. Because patient-level ethnicity data were not available city-level ethnicity proportions were used as proxy. The study included 2,430,333 patients with a mean follow-up of 15.36 months and cumulative incidence of psychosis at two years of 1.43%. There were profound differences compared to the original development UK database in terms of case-mix, psychosis incidence, distribution of baseline predictors (ICD-10 cluster diagnoses), availability of patient-level ethnicity data, follow-up time and availability of specialized clinical services for at-risk individuals. Despite these important differences, the model retained accuracy significantly above chance (Harrell's C = 0.676, 95% CI: 0.672-0.679). To date, this is the largest international external replication of an individualized prognostic model in the field of psychiatry. This risk calculator is transportable on an international scale to improve the automatic detection of individuals at risk of psychosis.

Sleep as a translationally-relevant endpoint in studies of autism spectrum disorder (ASD)

Sleep has numerous advantages for aligning clinical and preclinical (basic neuroscience) studies of neuropsychiatric illness. Sleep has high translational relevance, because the same endpoints can be studied in humans and laboratory animals. In addition, sleep experiments are conducive to continuous data collection over long periods (hours/days/weeks) and can be based on highly objective neurophysiological measures. Here, we provide a translationally-oriented review on what is currently known about sleep in the context of autism spectrum disorder (ASD), including ASD-related conditions, thought to have genetic, environmental, or mixed etiologies. In humans, ASD is frequently associated with comorbid medical conditions including sleep disorders. Animal models used in the study of ASD frequently recapitulate dysregulation of sleep and biological (diurnal, circadian) rhythms, suggesting common pathophysiologies across species. As our understanding of the neurobiology of ASD and sleep each become more refined, it is conceivable that sleep-derived metrics may offer more powerful biomarkers of altered neurophysiology in ASD than the behavioral tests currently used in humans or lab animals. As such, the study of sleep in animal models for ASD may enable fundamentally new insights on the condition and represent a basis for strategies that enable the development of more effective therapeutics.

The Opioid Crisis and the Future of Addiction and Pain Therapeutics

Opioid misuse and addiction are a public health crisis resulting in debilitation, deaths, and significant social and economic impact. Curbing this crisis requires collaboration among academic, government, and industrial partners toward the development of effective nonaddictive pain medications, interventions for opioid overdose, and addiction treatments. A 2-day meeting, The Opioid Crisis and the Future of Addiction and Pain Therapeutics: Opportunities, Tools, and Technologies Symposium, was held at the National Institutes of Health (NIH) to address these concerns and to chart a collaborative path forward. The meeting was supported by the NIH Helping to End Addiction Long-Term^SM (HEAL) Initiative, an aggressive, trans-agency effort to speed scientific solutions to stem the national opioid crisis. The event was unique in bringing together two research disciplines, addiction and pain, in order to create a forum for crosscommunication and collaboration. The output from the symposium will be considered by the HEAL Initiative; this article summarizes the scientific presentations and key takeaways. Improved understanding of the etiology of acute and chronic pain will enable the discovery of novel targets and regulatable pain circuits for safe and effective therapeutics, as well as relevant biomarkers to ensure adequate testing in clinical trials. Applications of improved technologies including reagents, assays, model systems, and validated probe compounds will likely increase the delivery of testable hypotheses and therapeutics to enable better health outcomes for patients. The symposium goals were achieved by increasing interdisciplinary collaboration to accelerate solutions for this pressing public health challenge and provide a framework for focused efforts within the research community. SIGNIFICANCE STATEMENT: This article summarizes key messages and discussions resulting from a 2-day symposium focused on challenges and opportunities in developing addiction- and pain-related medications. Speakers and attendees came from 40 states in the United States and 15 countries, bringing perspectives from academia, industry, government, and healthcare by researchers, clinicians, regulatory experts, and patient advocates.

Digital Phenotyping With Mobile and Wearable Devices: Advanced Symptom Measurement in Child and Adolescent Depression

With an estimated 75% of all mental disorders beginning in the first two decades of life,¹ childhood and adolescence are crucial developmental periods to identify and intercept the unfolding of mental health problems, their relationships with physical health, and the multiple, interwoven connections to the surrounding environment.² Because an individual's mental health is best conceptualized, captured, and treated by taking into account the network of physiological and social functions that constitute the context of individual experience, accessing and analyzing data on multiple health indicators simultaneously can accelerate prediction of disease progression. With the advent of new technologies, dense and extensive amounts of biopsychosocial readouts that can be translated into clinically relevant information have become available in real time, with the potential to revolutionize the practice of medicine. However, challenges to this more ecological and comprehensive approach to mental health measurement include the actual capacity of capturing, safely storing, and analyzing dense data sets (encompassing, for example, mood, cognitions, physical activity, sleep, social interactions) from multiple synchronized sources, and identifying which among multiple indicators ultimately prove useful to improve prediction of a deterioration in symptoms and of initiating early intervention. In this Translations article, we focus on digital phenotyping (DP), which relates to the capturing of the aforementioned relevant biopsychosocial data. This concept is rapidly growing and gaining relevance to child and adolescent psychiatry, and is connected with overarching data science themes of "big data" (extremely large data sets, including data from electronic medical records, imaging, genomics, and patients' smartphones),^3,4 in addition to "machine learning" (the science of getting computers to act without being explicitly programmed)⁵ and "precision medicine" (the practice of custom tailoring treatments to a patient's disease processes),⁶ which have all received attention in this journal. We will describe principles and current applications of DP, together with its potential to facilitate improved outcomes and its limits, using depression in children and adolescents as an illustrative example.

Quantifying the social symptoms of autism using motion capture

Autism Spectrum Disorder (ASD) is a remarkably heterogeneous condition where individuals exhibit a variety of symptoms at different levels of severity. Quantifying the severity of specific symptoms is difficult, because it either requires long assessments or observations of the ASD individual, or reliance on care-giver questionnaires, which can be subjective. Here we present a new technique for objectively quantifying the severity of several core social ASD symptoms using a motion capture system installed in a clinical exam room. We present several measures of child-clinician interaction, which include the distance between them, the proportion of time that the child approached or avoided the clinician, and the direction that the child faced in relation to the clinician. Together, these measures explained ~30% of the variance in ADOS scores, when using only ~5 minute segments of “free play” from the recorded ADOS assessments. These results demonstrate the utility of motion capture for aiding researchers and clinicians in the assessment of ASD social symptoms. Further development of this technology and appropriate motion capture measures for use in kindergartens and at home is likely to yield valuable information that will aid in quantifying the initial severity of core ASD symptoms and their change over time.

Improved Socio-Emotional and Behavioral Functioning in Students with Autism Following School-Based Smartglasses Intervention: Multi-Stage Feasibility and Controlled Efficacy Study

Background: Students with Autism Spectrum Disorder (ASD) commonly demonstrate prominent social communication deficits, symptoms of attention-deficit/hyperactivity disorder, and chronic irritability. These challenges hinder academic progress and frequently persist despite educational, behavioral, and medical interventions. An assistive smartglasses technology may aid these individuals, especially if the technology is efficacious in ecologically-valid school settings. This study explored the feasibility and efficacy of Empowered Brain, a computerized smartglasses intervention designed as a socio-emotional behavioral aid for students with ASD. Methods: This two-part six-week study involved four school children with ASD from a public elementary school. The study incorporated an initial three-week feasibility stage followed by a three-week controlled longitudinal efficacy stage. Both stages involved the use of a twice-daily socio-emotional intervention with the smartglasses. Educators completed pre-intervention and post-intervention Aberrant Behavioral Checklist (ABC) ratings at the start of the feasibility stage, and weekly during the efficacy stage. Primary outcome measures were improvements in the ABC subscales of irritability, hyperactivity, and social withdrawal. Results: Students in both feasibility and efficacy stages demonstrated improvements (decreases) in irritability, hyperactivity, and social withdrawal compared to a baseline period and control periods, respectively. Participants in the controlled efficacy stage demonstrated decreased ABC subscale scores of 90% for irritability, 41.6% for hyperactivity, and 45.6% for social withdrawal. An intervention exposure-response improvement in irritability and hyperactivity was found during the efficacy stage. Educators rated the technology as superior or vastly superior compared to other assistive technologies. Conclusion: A substantial number of school children with ASD demonstrate chronic and impairing cognitive and behavioral challenges. This study provides evidence that Empowered Brain, a smartglasses-based socio-emotional aid for autism, is both feasible and efficacious in improving symptoms of social withdrawal, irritability, and hyperactivity in students with autism. The improvement is demonstrated as part of a longitudinal school-based intervention. Further studies involving larger samples and incorporation of randomized controlled trial methodology are underway to further elucidate the impact of this technology.