Scientific Meeting » NIMH Novel Target Discovery and Psychosocial Intervention Development Workshop

NIMH Novel Target Discovery and Psychosocial Intervention Development Workshop

Date/Time:

poster image for NIMH Novel Target Discovery and Psychosocial Intervention Development Workshop

Location: Neuroscience Center
6001 Executive Boulevard
Conference Room A1/A2
Bethesda, MD
WebEx

Sponsored by: NIMH Division of Translational Research

On February 24-25, 2020, the Division of Translational Research will host a workshop titled, Novel Target Discovery and Psychosocial Intervention Development. Despite tremendous advances in basic neuroscience and behavioral science, there is still a major need for more effective treatments for many people living with mental illness. Part of NIMH’s mission is to speed up the pace of discovery of new validated targets, such as biological, behavioral, and clinical markers of specific mental illnesses, to help aid in the development of new therapeutic treatments for mental illness.

The overarching goal of the workshop is to accelerate research that supports the identification of novel targets to develop and improve non-pharmacological interventions (such as cognitive, behavioral, and psychosocial approaches).

Interdisciplinary researchers from basic, translational, and clinical investigation teams will present on their experience in identifying a novel target and developing interventions to impact the target and clinical outcomes. They will discuss their rationales for selecting the mechanistic affective/cognitive/social processes they chose to target with their interventions, and the properties of those processes that convinced them that they had potential as intervention targets. The discussion that follows will explore the utility of cross-disciplinary team work for the successful translation of basic findings to clinical application. Participants will represent a range of intervention types and targets, as well as intervention needs across the lifespan.

Registration and Parking

This event is open without prior registration to all NIH staff and the public. Parking is available at a nominal fee. A government-issued photo identification card (such as an NIH ID or driver's license) is required to enter the building. The event will be available via WebEx.

More Information:

The WebEx information will be posted the week before the workshop. Check back soon for more updates.

Original Article

Video » Depression Mental Health Minute

Depression Mental Health Minute

 

Watch on YouTube.

Transcript

[music]

>> NARRATOR: Got 60 seconds? Take a mental health minute to learn about depression disorders.

They cause symptoms that affect how you feel…How you think…And how you handle daily life.

Depression should be treated by health professionals. Don’t try to handle it alone or treat it with alcohol or illegal drugs.

Treatments include psychotherapy or talk therapy…

Medication…

And sometimes brain stimulation therapy.

These treatments may also be used in combination with one another.

If you think you might have depression, talk to a trusted friend, family member, or health care provider.

The earlier treatment begins, the more effective it is.

Learn more about depression through the NIMH website…And take 60 seconds to share this mental health minute to help someone else.

Original Article

Scientific Meeting » The NIMH Director’s Innovation Speaker Series – A Myth of Convenience: The Law Lag and Scientific Progress

The NIMH Director’s Innovation Speaker Series – A Myth of Convenience: The Law Lag and Scientific Progress

Date/Time:

Location: Neuroscience Center
6001 Executive Boulevard
Bethesda, MD
Videocast

Sponsored by:
NIMH Division of Extramural Affairs

The NIMH Director’s Innovation Speaker Series - A Myth of Convenience: The Law Lag and Scientific Progress

On February 13, 2020, Sheila Jasanoff, Ph.D., J.D., will present “A Myth of Convenience: The Law Lag and Scientific Progress,” as part of the NIMH Director’s Innovation Speaker Series.

One of the most persistent stories that have grown up around innovation in science and technology is that law and policy inevitably lag behind advances in knowledge and its applications. By now close to a century old, the notion that society’s moral work inevitably trails, and worse, impedes scientific progress has gained a powerful hold on the cultural imagination. It provides a ready rationale for self-regulation in science. Using examples from genetic engineering and gene patenting to CRISPR, Professor Jasanoff will argue that the law lag narrative is descriptively inaccurate and normatively unjustifiable. In this era of rapid changes across the life sciences and technologies, we need a more sophisticated understanding of the relations between facts and values, or science and law. Those understandings, in turn, should guide future approaches toward deliberation and governance for new biotechnologies.

Sheila Jasanoff is the Pforzheimer Professor of Science and Technology Studies at the John F. Kennedy School of Government at Harvard University. She is affiliated with the Department of the History of Science and Harvard Law School. Previously, she was Professor of Science Policy and Law at Cornell University and founding chair of Cornell’s Department of Science and Technology Studies. At Harvard, she founded and directs the Kennedy School’s Program on Science, Technology, and Society (STS). In 2002, she founded the Science and Democracy Network, an international community of STS scholars dedicated to improving scholarly understanding of the relationships among science, technology, law, and political power.

Professor Jasanoff’s research centers on the interactions of law, science, and politics in democratic societies. She is particularly concerned with the construction of public reason in various cultural contexts and with the role of science and technology in national and global institutions. She has written more than 120 articles and book chapters on these topics and has authored and edited more than fifteen books.

Registration and Parking

This event is open without prior registration to all NIH staff and the public. Parking is available at a nominal fee. A government-issued photo identification card (such as an NIH ID or driver's license) is required to enter the building. This event will be available over the NIH videocast at https://videocast.nih.gov.

Background

The NIMH Director’s Innovation Speaker Series was started to encourage broad, interdisciplinary thinking in the development of scientific initiatives and programs, and to press for theoretical leaps in science over the continuation of incremental thinking. Innovation speakers are encouraged to describe their work from the perspective of breaking through existing boundaries and developing successful new ideas, as well as working outside their initial area of expertise in ways that have pushed their fields forward. We encourage discussions of the meaning of innovation, creativity, breakthroughs, and paradigm-shifting.

More Information:

The videocast will be available on the day of the lecture at https://videocast.nih.gov.

Sign Language Interpreters will be provided. Individuals with disabilities who need reasonable accommodations to participate in this program should contact Dawn Smith 301-451-3957 and/or the Federal Relay (1-800-877-8339).

Original Article

Scientific Meeting » Transportation and Mobility Options to Support Postschool Transition for Youth with Autism

Transportation and Mobility Options to Support Postschool Transition for Youth with Autism

Date/Time:

On February 11, 2020, the National Center for Mobility Management and the Federal Transit Administration, U.S. Department of Transportation, are providing a free webinar on transportation and mobility services for youth with autism transitioning out of high school. The webinar is intended to help users leverage mobility resources and develop connections with transportation providers and services. The webinar is sponsored by the National Autism Coordinator and the Office of Autism Research Coordination, National Institute of Mental Health.

Presenters:

Danielle Nelson, MPH
Senior Program Analyst
U.S. Department of Transportation, Federal Transit Administration

Judy L. Shanley, Ph.D.
Assistant Vice President, Education & Youth Transition
Easterseals Director, National Center for Mobility Management

Genelle C. Thomas, M.A.
Director of National Initiatives
Partners for Youth with Disabilities

Austin Carr
Self-Advocate
Boston, MA

Webinar Details

Registration is not necessary.

Date: Tuesday, February 11, 2020
Time: 3:00 – 4:00 PM ET

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Meeting number (access code): 627 670 838
Host key: 545904
Meeting password: SupportASD

JOIN FROM A VIDEO SYSTEM OR APPLICATION
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Original Article

Science News » Neural Signature Identifies People Likely to Respond to Antidepressant Medication

Researchers have discovered a neural signature that predicts whether individuals with depression are likely to benefit from sertraline, a commonly prescribed antidepressant medication. The findings, published in Nature Biotechnology, suggest that new machine learning techniques can identify complex patterns in a person’s brain activity that correlate with meaningful clinical outcomes. The research was funded by the National Institute of Mental Health (NIMH), part of the National Institutes of Health.

“There is a great need in psychiatry today for objective tests that can inform treatment and go beyond some of the limitations of our diagnostic system. Our findings are exciting because they reflect progress made toward this clinical goal, and they also show the potential of bringing sophisticated data analytic methods to psychiatry,” explained senior author Amit Etkin, M.D., Ph.D., a professor of psychiatry and behavioral sciences at Stanford University and CEO of Alto Neuroscience, Los Altos, California.

Major depression is one of the most common mental disorders, affecting about 7% of adults in the U.S. in 2017, but the symptoms experienced can vary from person to person. While some may experience many of the characteristic features—including persistent sad mood, feelings of hopelessness, loss of pleasure, and decreased energy—others may experience only a few. There are several evidence-based options available for treating depression, but determining which treatment is likely to work best for a specific person can be a matter of trial and error.

Previous research has suggested that specific components of brain activity, as measured by resting-state electroencephalography (EEG), could yield insight into how people will respond to certain treatments. However, researchers have yet to develop predictive models that can differentiate between response to antidepressant medication and response to placebo and that can also predict outcomes for individual patients. Both features are essential for the neural signature to have clinical relevance.

Etkin, co-senior author Madhukar H. Trivedi, M.D., a professor of psychiatry at the University of Texas Southwestern Medical Center, Dallas, and first author Wei Wu, Ph.D., an instructor at Stanford University, California, drew on insights from neuroscience, clinical science, and bioengineering to build an advanced predictive model. The researchers developed a new machine learning algorithm specialized for analyzing EEG data called SELSER (Sparse EEG Latent SpacE Regression). They hypothesized that this algorithm might be able to identify robust and reliable neural signatures of antidepressant treatment response.

The researchers used SELSER to analyze data from the NIMH-funded Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care (EMBARC) study, a large randomized clinical trial of the antidepressant medication sertraline, a widely available selective serotonin reuptake inhibitor (SSRI). As part of the study, participants with depression were randomly assigned to receive either sertraline or placebo for eight weeks. The researchers applied SELSER to participants’ pre-treatment EEG data, examining whether the machine learning technique could produce a model that predicted participants’ depressive symptoms after treatment.

SELSER was able to reliably predict individual patient response to sertraline based on a specific type of brain signal, known as alpha waves, recorded when participants had their eyes open. This EEG-based model outperformed conventional models that used either EEG data or other types of individual-level data, such as symptom severity and demographic characteristics. Analyses of independent data sets, using several complementary methods, suggested that the predictions made by SELSER may extend to broader clinical outcomes beyond sertraline response.

In one independent data set, the researchers found that the EEG-based SELSER model predicted greater improvement for participants who had shown partial response to at least one antidepressant medication compared with those who had not responded to two or more medications, in line with the patients’ clinical outcomes. Another independent data set showed that participants who were predicted by SELSER to show little improvement with sertraline were more likely to respond to treatment involving a specific type of non-invasive brain stimulation called transcranial magnetic stimulation (in combination with psychotherapy).

Work is now underway to further replicate these findings in large, independent samples to determine the value of SELSER as a diagnostic tool. According to Etkin, Trivedi, Wu, and colleagues, the present research highlights the potential of machine learning for advancing a personalized approach to treatment in depression.

“While work remains before the findings in our study are ready for routine clinical use, the fact that EEG is a low-cost and accessible tool makes the translation from research to clinical practice more possible in the near term. I hope our findings are part of a tipping point in the field with respect to the impact of machine learning and objective testing,” Etkin concluded.

Reference

Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E.,…Etkin, A. (2020). Antidepressant-responsive brain signature in major depression defined by electroencephalography. Nature Biotechnology. doi: 10.1038/s41587-019-0397-3

Clinical Trial:

NCT01407094

Grants:

MH092221, MH092250, MH103324, MH116506

About the National Institute of Mental Health (NIMH): The mission of the NIMH is to transform the understanding and treatment of mental illnesses through basic and clinical research, paving the way for prevention, recovery and cure. For more information, visit the NIMH website.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit the NIH website.

NIH…Turning Discovery Into Health®

Original Article

Concept Clearance » Assessing Outcomes of Health System Suicide Risk Screening Programs

Assessing Outcomes of Health System Suicide Risk Screening Programs

NAMHC Concept Clearance •

Presenter:

Michael Schoenbaum, Ph.D.
Division of Services and Intervention Research, and NIMH Suicide Research Team

Background:

The rising rates of suicide deaths and non-fatal suicide behaviors in the US are pressing public health challenges. Nearly half of suicide decedents visit emergency care in the year before death, around one-fifth in the month before death; and approximately 80% of suicide decedents have some type of health care encounter in the year before death. These rates underscore the importance and value of improving identification and treatment of suicide risk in emergency departments and other health care settings. In recent years, some US health systems have begun to expand suicide risk screening, up to and including screening in every patient-encounter. These programs have yielded information on rates of case-finding in various care settings and patient groups, but much less is known about key patient outcomes beyond the screening itself, such as fatal and non-fatal suicide behaviors.

Goal:

The goal of this initiative is to support assessment of suicide-related patient outcomes in health systems that implement wider suicide risk screening in their care settings. Such assessment involves health systems linking data they hold on their patient panels/populations, including suicide risk screening scores/results, to mortality data and to data on subsequent health care. Such linked data will enable analyses and reporting of key outcomes among patients served by health systems that have implemented wider suicide risk screening, and comparing these with the outcomes of patients served in prior periods and/or in settings with less extensive suicide risk screening.

Objectives:

A growing number of health systems in the US have initiatives to expand suicide risk screening. Some of these have reported on aspects of this work, such as the development, implementation and operation of their suicide risk screening program. However, limited data are available on health outcomes associated with such programs; nor on trajectories of health care use and costs for patients identified with different suicide risk screening scores. The former provides information on the clinical and public health benefits of suicide risk screening programs, while the latter provides information on the possible workflow and financial effects of such programs. This initiative aims to support linkage and analyses of data on health, health care and mortality of patient panels/populations, in relation to health system implementation of suicide risk screening programs, and in relation to patients’ suicide risk screening results. Analyses of interest include, but are not necessarily limited to:

  • The fraction of patients in particular care settings who were identified with suicide risk, before and after implementation of a suicide risk screening program.
  • Patterns of suicide death and other relevant types of mortality over particular periods of follow-up after an index clinical encounter, for patient groups defined by care setting and identified suicide risk.
  • Patterns of non-fatal suicide events, and other relevant types of injury (e.g., unintentional overdoses, non-overdose unintentional injury) over particular periods of follow-up after an index clinical encounter, for patient groups defined by care setting and identified suicide risk.
  • Patterns of emergency department and hospital use and costs over particular periods of follow-up after an index clinical encounter, for patient groups defined by care setting and identified suicide risk.

Outcomes

Findings would inform quality improvement programs focused on suicide prevention by health systems across the range of current suicide risk screening practices, from those that conduct minimal screening to those with more comprehensive or universal screening programs; inform possible revision of health care accreditation standards and other policies that could affect use of evidence-based suicide prevention practices; and identify key areas for future research to increase the effectiveness and efficiency of suicide prevention practices.

Original Article

Concept Clearance » HEAL Supplements to Improve the Treatment and Management of Common Co-Occurring Conditions and Suicide Risk in People Affected by the Opioid Crisis

HEAL Supplements to Improve the Treatment and Management of Common Co-Occurring Conditions and Suicide Risk in People Affected by the Opioid Crisis

NAMHC Concept Clearance •

Presenter:

Michael C. Freed, Ph.D., EMT-B
Division of Services and Intervention Research

Goal:

In April 2018, the NIH launched the Helping to End Addiction Long-termSM Initiative, or HEALSM Initiative, an aggressive, trans-agency effort to speed scientific solutions to stem the national opioid public health crisis. In response to this initiative, the National Institute of Mental Health (NIMH), in partnership with other NIH Institutes and Offices, proposes to supplement relevant studies to improve the treatment and management of common co-occurring conditions and suicide risk in people affected by the opioid crisis.

Rationale:

In August 2019, the HEAL Initiative Multi-Disciplinary Working Group called for research studies that seek to improve the provision of care for people with common co-occurring conditions associated with the opioid crisis (e.g., people with mental health disorders, chronic pain, suicide risk, alcohol misuse/alcohol use disorder, and/or other substance use disorders). Clinicians in settings such as primary care routinely face patients with complex needs. Workflows and services need to be in place to help the modal patient, who often presents with some combination of treatable conditions, including opioid use disorder (OUD; or OUD risk), mental health conditions, suicide risk, chronic pain, alcohol use disorders, and other substance use disorders. Failure to adequately address co-occurring conditions may impede OUD treatment and increase OUD risk and other adverse outcomes such as suicide. A multi-pronged strategy that addresses co-occurring conditions may yield greater improvements in OUD outcomes as well as improved overall heath for treated individuals.

Supplements could enable investigators to evaluate the most effective OUD treatments, services, and prevention interventions for the significant number of people presenting with co-occurring mental health, pain, alcohol, and substance use conditions and who may also be at risk for suicide. NIH aims to support competitive supplements to improve the treatment and management of common co-occurring conditions and suicide risk in people affected by the opioid crisis. Supplement requests need not address all possible co-occurring conditions, but they must have clear and direct relevance to OUD and/or chronic pain.

High priority supplements will aim to:

  • Test and evaluate approaches to improve the treatment, management, and services for people with co-occurring conditions and suicide risk;
  • Examine the potential mechanisms through which co-occurring conditions impact service use (e.g., access, engagement), delivery/quality, and outcomes, in order to identify potentially targets for therapeutic and services intervention; and/or
  • Identify or validate putative change mechanisms that may account for secondary benefits of treatment (e.g., reduction of suicide risk following successful OUD treatment) and that can inform the development and testing of future prevention, treatment, or services interventions.

Original Article

Concept Clearance » Optimizing Multi-Component Service Delivery Interventions for People with Opioid Use Disorder, Co-Occurring Conditions, and/or Suicide Risk (HEAL)

Optimizing Multi-Component Service Delivery Interventions for People with Opioid Use Disorder, Co-Occurring Conditions, and/or Suicide Risk (HEAL)

NAMHC Concept Clearance •

Presenter:

Michael C. Freed, Ph.D., EMT-B
Division of Services and Intervention Research

Goal:

In April 2018, the NIH launched the Helping to End Addiction Long-termSM Initiative, or HEALSM Initiative, an aggressive, trans-agency effort to speed scientific solutions to stem the national opioid public health crisis. In response to this initiative, the National Institute of Mental Health (NIMH), in partnership with other NIH Institutes and Offices, intends to invite research that will optimize multi-component service delivery interventions for people with opioid use disorder (OUD) and co-occurring conditions, to include suicide risk. Here, the relative value of component services and interventions will be tested and evaluated to inform decisions about which components to implement broadly, which to de-implement, and/or how to sequence the implementation of components that are part of an overall service delivery package.

Rationale:

In August 2019, the HEAL Initiative Multi-Disciplinary Working Group called for research studies that seek to improve the provision of care for people with common co-occurring conditions associated with the opioid crisis (e.g., people with mental health disorders, chronic pain, suicide risk, alcohol misuse/alcohol use disorder, and/or other substance use disorders). A variety of approaches have been tested to address the complex needs of patients with co-occurring medical and psychiatric conditions, including multi-component interventions that coordinate treatment activities across multiple providers. For example, interventions delivered in primary care might include several service delivery elements (e.g., screening, evaluation, referral, and/or consultation) as well as specific preventive or therapeutic interventions (e.g., for OUD or for co-occurring interventions). Randomized Controlled Trials (RCTs) have evaluated the effectiveness of multi-element treatment packages, but these trials rarely provide information about the relative contribution of intervention components. Understanding the relative value of constituent components could result in leaner service delivery models for resource constrained environments.

This concept aims to support practice-relevant research that could identify essential components of multi-component service packages for OUD and co-occurring conditions that are associated with improved outcomes. It is expected that research projects would examine the effects of multiple practice-relevant independent variables and employ efficient designs that are powered to examine the effects of individual components. Approaches could include factorial designs and their derivatives (e.g., fractional factorial or partial factorial), Multiphase Optimization Strategy, interrupted time series designs, or other quasi-experimental approaches, where randomization may not be possible.

Original Article

Concept Clearance » Standards to Define Experiments Related to the BRAIN Initiative

Standards to Define Experiments Related to the BRAIN Initiative

NAMHC Concept Clearance •

Presenter:

Gregory K. Farber, Ph.D.
Office of Technology Development and Coordination

Goal:

The goal of this concept is to support researchers in the development of standards that describe experimental protocols that are being conducted as part of the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative. It is expected that the researchers will solicit community input at all stages of the process. It is recommended that the first step of standard development will involve sharing data between different key groups in the experimental community in order to ensure that the developing standard will include the approaches to data collection utilized by the groups collecting the data. The developed standard is expected to be made widely available.

Rationale:

The BRAIN Initiative informatics infrastructure program is managed for the BRAIN Initiative by NIMH. The program has three components: data archives, data standards, and software for data analysis or data integration. The data archive component of the infrastructure is now complete. NIMH has funded archives for each of the instrument/device types that the BRAIN Initiative is supporting. There are still some needs in the data standards area as well as in the integration/analysis software area.

Original Article

Concept Clearance » BRAIN Initiative Fellows: Ruth L. Kirschstein National Research Service Award (NRSA) Individual Postdoctoral Fellowship

BRAIN Initiative Fellows: Ruth L. Kirschstein National Research Service Award (NRSA) Individual Postdoctoral Fellowship

NAMHC Concept Clearance •

Presenter:

Ashlee Van’t Veer, Ph.D.
Division of Neuroscience and Basic Behavioral Science

Goal:

This is a re-issue of the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative Fellows: Ruth L. Kirschstein National Research Service Award (NRSA) Individual Postdoctoral Fellowship (F32) funding opportunity announcement. The purpose of this fellowship is to support promising applicants during their mentored postdoctoral training under the guidance of outstanding faculty sponsors. Applicants for the BRAIN Initiative Fellows F32 program are expected to propose a research project and training plan in a scientific area relevant to one or more of the goals of the BRAIN Initiative, including neuroethics. The F32 Program aims to provide applicants with training using cutting-edge tools, theories, and/or approaches that will prepare them to launch independent research careers in areas that will advance the goals of the BRAIN Initiative.

Rationale:

NIH is one of several federal agencies involved in the BRAIN Initiative. Planning for the NIH component of the BRAIN initiative is guided by the long-term scientific plan, BRAIN 2025: A Scientific Vision. Educational goals include acquisition of quantitative skills; the appropriate use and integration of newly developed tools, technologies and methods developed under the BRAIN Initiative; and, consideration of the ethical implications of neuroscience research. A special focus is training in quantitative neuroscience, i.e. theory and statistics for biologists, and exposing physicists, engineers and statisticians to experimental neuroscience. The BRAIN 2025 Report strongly encourages scientists to cross traditional areas of expertise to conduct interdisciplinary research and acknowledges the need to attract investigators and faculty recruits to neuroscience from quantitative disciplines, e.g., statistics, computer science, physics, mathematics, and engineering. The BRAIN 2025 Report also emphasizes the need to consider the ethical implications of neuroscience research. In human neuroscience research, unique ethical issues are arising because new neurotechnologies are being employed that affect the human brain. In addition to grounding all neuroscience research training in consideration of ethical issues, it is necessary to invest in training individuals who will be the next generation of leaders in neuroethics research.

This initiative aims to solicit applications from early-stage postdoctorates to acquire mentored research training using cutting-edge tools, theories, and/or approaches in one of the seven high-priority areas of the BRAIN Initiative, including neuroethics. Given the expressed need to bring those trained in quantitative disciplines to neuroscience research, applications from individuals obtaining terminal doctorates in quantitative disciplines are encouraged.

Original Article