Cross-Cutting Research Themes

Several significant research themes cut across and are integral to the Goals of the NIMH Strategic Plan for Research. These themes highlight areas where NIMH-funded science may have the greatest impact, bridge research gaps, and offer novel approaches to accelerate advances and promote equity in mental health research. This section summarizes these major themes that, along with the challenges and opportunities facing the mental health field, inspired this Strategic Plan for Research.

A Comprehensive Research Agenda

Excellent and comprehensive science requires an inclusive approach focused on varied topic areas, extending research participation and partnerships, and advancing the research agenda across multiple timeframes. To ensure there is the potential to improve clinical care over the short-, medium-, and long-term, study designs should engage diverse populations and perspectives, methods, tools, and models. Diversity in these areas of research, such as engaging multiple perspectives, enables us to address complex basic, translational, and applied questions, including those at the intersection of the brain, behavior, and community. All vertebrate animal and human studies should factor sex as a biological variable  into research designs and reporting. Depending on the research question, researchers should consider how genetic background will advance the quality and interpretability of the outcomes. Clinical research studies should include participants from diverse racial and ethnic backgrounds, and across gender identities , geographical context, socioeconomic status, disability, neurotype, and age—offering meaningfully representative samples best suited to address public health concerns and provide the most rigorous foundation to inform care and policy.

Prevention

NIMH has a developmentally focused, theory-based prevention research program that spans the life course from prenatal though late-life, at different levels of intervention (e.g., universal, selective, indicated, tiered), and in different settings (e.g., homes, schools, health care settings, communities). While the targeted developmental stage may change, the primary focus of interventions is on reducing risk and increasing protective factors that can modify proximal outcomes (e.g., parenting, self-regulation, skill development) and long-term, distal outcomes (e.g., depression, anxiety, suicide ideation and behaviors). Transition periods (e.g., biological, normative, social) offer important opportunities for the implementation of preventive interventions at different developmental stages. NIMH supports research focused on developing and testing scalable preventive interventions, including prevention trials conducted in a variety of contexts and settings where preventive services are offered, simulation modeling of alternative prevention strategies, and implementation research that tests strategies that can be used to promote the adoption and sustained implementation of effective preventive interventions in communities.

Global Mental Health

The interconnectedness of global societies calls for a concerted and collaborative effort, drawing upon the resources and expertise of diverse cultures and regions, to comprehensively address mental illnesses worldwide. NIMH's global mental health research programs aim to study diverse populations, understand the prevalence and impacts of disorders, develop effective interventions, enhance mental health services access and delivery, and explore the bidirectional relation between social determinants of mental health and mental health outcomes.

Global mental health programs provide an opportunity to improve our understanding of the factors impacting mental health across diverse cultures, socioeconomic statuses, and societal contexts. These efforts facilitate the integration of genetics, cultural backgrounds, societal and familial structures, and environmental factors into both basic and translational research on mental health. Such studies have the potential to transform interventions by identifying new targets and approaches to address the specific mental health needs of underserved populations, particularly those in low- and middle-income countries (LMICs). Global partnerships may result in reciprocal learning, or the multidirectional exchange, adaptation, and implementation of research advancements to improve outcomes for all.

NIMH's commitment extends to the implementation of cutting-edge tools and technologies aimed at enhancing mental health screening, assessment, prediction, prevention, and treatment across diverse systems of care. By leveraging these advancements, NIMH aims to strengthen sustainable research capacity in LMICs and the reciprocal learning opportunities that foster a community dedicated to global mental health research.

Environmental Exposures

Numerous factors in the environment can influence the development of mental illnesses. The environment includes natural and built components, individual factors such as the microbiome, and social factors such as family interactions, peer relationships, and social determinants of health. Social determinants may include structural racism, housing instability, food insecurity, socioeconomic status, and others. These environmental factors, which vary within and across populations and settings, can affect biological systems important in regulating functions of the body and mental processes. We are making significant strides toward understanding how environmental factors affect brain development and shape behavior. For example, as part of the Adolescent Brain Cognitive Development℠ Study (ABCD Study®) , which has enrolled nearly 12,000 children across the country, researchers are examining how biology and environment interact and relate to developmental outcomes, such as physical health and mental health. NIMH also continues to vigorously support efforts to study the biological and psychological impacts of trauma, mechanisms of prenatal risk, and numerous other environmental factors that may contribute to mental illnesses. In collaboration with the NIH Helping to End Addiction Long-term® (HEAL) Initiative , NIMH supports the HEALthy Brain and Child Development (HBCD) Study  to understand the impact of prenatal and postnatal exposure to drugs and other adverse environmental conditions on brain development and risk of substance use, mental disorders, and other adverse outcomes. NIMH is also participating in the NIH Climate Change and Health Initiative , an urgent, cross-cutting effort to reduce health threats from climate change and build health resilience in individuals, communities, and nations around the world, especially among those at highest risk.

Comorbidities

Comorbidities—the co-occurrence of mental and/or other physical disorders, including substance use disorders—may affect both the development and clinical course of mental illnesses through their effects on basic biological processes. For example, some treatments for people with HIV may affect inflammation in the CNS, metabolism, and the microbiome—factors that also impact the development of mental illnesses. Examining the interactions between mental illnesses and co-occurring conditions will provide additional insight into the causes and facilitators of mental illnesses, as well as provide pathways to improve the provision of interventions and services to ultimately prevent and treat mental illness and comorbidities and achieve better outcomes for people with mental illnesses. NIMH works with other NIH institutes and centers to support research to address treatable medical comorbidities linked to premature mortality among people with serious mental illness. For example, through the NIH HEAL Initiative® , NIMH leads a portfolio of research to help improve the provision of services for people with co-occurring opioid use disorder and mental disorders and/or suicide risk.

Translation

Engaging Novel Frameworks for Studying Mental Disorders. High rates of psychiatric comorbidity and heterogeneity of symptoms occur when patients are characterized using solely the current diagnostic categories, which rely on self-reported or observable symptoms. To extend research beyond diagnostic boundaries, NIMH’s evolving Research Domain Criteria (RDoC) framework focuses on fundamental psychological/biological systems (such as cognitive or sensorimotor processes) and integrates many approaches and levels of information to understand function and dysfunction in mental illnesses. These levels of information span from cellular to behavioral measures, with attention to developmental trajectories and the impact of environmental and social factors. The RDoC framework is also well-suited to computational approaches, such as those described below, which incorporate multiple multimodal sources of information to improve mental health outcomes. Beyond improving research sample characterization using integrative, functionally based measures, this approach holds promise for uncovering mechanisms of mental illnesses, identifying putative therapeutic targets, and paving the way for novel, personalized preventive and treatment interventions. In keeping with its potential to promote precision psychiatry, the RDoC framework served as the basis for a recent funding initiative to identify new clinical signatures that can be used for individual-level prediction and clinical decision-making. This initiative, called Individually Measured Phenotypes to Advance Computational Translation in Mental Health (IMPACT-MH), encourages research that uses scalable behavioral measures and computational methods, in addition to traditional clinical information, to help predict patient outcomes.

Advancing Interventions. Historically, novel prevention and treatment development has been slow, expensive, and high risk. To facilitate progress across the basic-to-clinical research pipeline, NIMH employs an experimental therapeutics approach to clinical trials requiring studies to define intervention targets and milestones. With NIMH’s experimental therapeutics approach, studies not only evaluate the clinical effect of an intervention, but also generate information about the mechanisms contributing to a disorder or an intervention response.

Accelerating Public Health Impact. The translation of new interventions into routine practice and population-level benefits has also been far too slow. To accelerate the adoption and implementation of evidence-based interventions and strategies into routine mental health care across a variety of settings, NIMH invests in studies that anticipate real-world implementation during intervention development. Additionally, NIMH encourages engagement with decision makers and end users throughout the research process, and attends to pragmatic questions about implementation like financing, scalability, and sustainability. This is especially important when considering the challenges of delivering care to underserved communities and in low-resource settings.

Computational Approaches

Computational approaches  are aimed at developing mathematical and modeling frameworks to improve the understanding, prevention, and treatment of mental illnesses. Computational approaches can allow us to mechanistically describe and empirically test how high-level behavioral phenotypes emerge from complex neurobiological processes at the micro-, meso-, and macro-scale levels of the brain. For example, computational models can put into explicit mathematical terms testable hypotheses regarding how alterations in genes might causally affect circuit function through disruptions of neuronal and synaptic dynamics. Similarly, computational models can suggest how circuit dysfunction impacts neural development and plasticity, and how that dysfunction manifests in behavior leading to progressive, chronic disorders. In addition to modeling frameworks incorporating biophysical realism, more data-driven computational approaches can take advantage of large datasets, categorizing brain dysfunction in ways that can lead to better diagnoses, improved biomarkers, and tailored preventive and treatment interventions. Using big data and theoretical approaches in clinical research can help bridge the gap between integrative multi-omics (e.g., epigenomics, transcriptomics, proteomics), neuroimaging, and digital phenotyping to traverse the complex path from genomics to therapeutics. Using computational approaches to assist in the development of explanatory theoretical models that integrate information across diverse experimentally tested domains (such as biological, psychological, social, and cultural environments; see the RDoC framework) can help define a critical path forward for understanding mechanisms and advancing new treatments. Within clinical research, computational methods (e.g., data mining, machine learning, predictive analytics) may also be used to analyze electronic health records or other clinical data to identify modifiable risk factors, derive quantitative predictions to inform the optimal timing of interventions, and evaluate the outcomes of treatment trajectories. For instance, mathematical modeling of system dynamics may predict which patients might respond best to which of the many existing treatments for depression, optimizing treatment selection, improving implementation, and speeding recovery. These and other computational advances hold the promise of objective, data-driven diagnosis and treatment recommendations for serious mental illnesses, contributing to NIMH’s effort to bring precision psychiatry to patients. NIMH is dedicated to supporting computational approaches that integrate knowledge gained at genetic, molecular, cellular, circuit, behavioral, and health care system levels, and ask high-impact basic neuroscience, translational, and service and intervention questions.

Harnessing the Power of Data

Advances in data acquisition and the availability of aggregated, harmonized datasets, coupled with new computational modelling tools like machine learning, are revolutionizing the efficiency with which researchers turn data into knowledge. These advances will ultimately help us better understand the complex factors affecting prevention and treatment outcomes and will optimize mental health care quality and effectiveness. NIMH is committed to providing modernized data infrastructure for use by the research community, capitalizing on advances in data science and information technology; setting policies to address storing data efficiently and securely for productive and ethical data use; and, connecting with the NIH data ecosystem and other data systems to make NIMH data usable to the broader community, consistent with findability, accessibility, interoperability, and reusability (FAIR) principles. In addition to providing the infrastructure, NIMH is working with other major mental health research funders around the world to establish a set of measures  that all mental health researchers may use as they collect data. This effort is expected to allow researchers to better integrate data from different laboratories. Further, the development, validation, optimization, and expansion of digital mental health tools will improve our understanding of mental illnesses in real time, help track the course of illness, and improve mental health care. Use of novel data sources including social media activity, geospatial data, and weather maps can help close the gap between the availability and demand for mental health prevention and treatment services. NIH’s Data Sharing Policy  encourages widespread data sharing and collaborations with experts in other areas of science, including behavioral and social scientists, implementation scientists, bioethicists, engineers, informaticists, and computer scientists, to add significant value to research and accelerate the pace of discovery.

Research Workforce

Updated: May 2024