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Transforming the understanding
and treatment of mental illnesses.

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Goal 2: Examine Mental Illness Trajectories Across the Lifespan

Grandmother laughing with granddaughter and daughter on sofa

Many mental illnesses present in childhood, adolescence, and young and late adulthood, yet mental illnesses are likely the late behavioral manifestations of changes that began years earlier. These early alterations may influence the course of brain and behavioral development and aging and establish the trajectories of mental illnesses. To better understand these trajectories, we need to develop a comprehensive picture of typical and atypical brain and behavioral development and maturation across the lifespan. At the same time, novel biomarkers and behavioral indicators hold promise for identifying who is at risk at the earliest possible point, signaling when development or aging is going off course, and producing the best individual outcomes for preventive and treatment interventions. We also need to understand the factors contributing to risk of, resilience to, and protection from development of mental illness.

Multi-level approaches will be critical in clarifying the complex relationships between biological and behavioral processes, social determinants of health, and environmental influences. At the level of individual factors, examining biological and behavioral processes across the lifespan will transform our understanding of the origins and progression of mental illnesses. Research to identify the earliest markers or signs that distinguish typical from atypical brain and/or functional development and maturation will be instrumental in predicting illness trajectories and outcomes later in life. NIMH encourages deep characterization across multiple levels of investigation (e.g., molecules, cells, circuits, networks, behavior) and integrative approaches to correlate their developmental patterns.

At the level of contextual factors, examining how social determinants and environmental factors impact risk and resilience to mental illnesses, burden of disease, access to care, and mental health outcomes will advance our understanding of mental health disparities. NIMH encourages community-engaged approaches to help identify the mechanisms by which social determinants of health and environmental factors impact mental health and ensure that the identification and interpretation of findings reflect the priorities and experiences of diverse populations participating in and impacted by research.

As we chart developmental and aging trajectories across the lifespan, it is important to identify sensitive periods—critical timepoints to intervene to reduce risk for and to prevent the onset of mental illnesses and improve outcomes. These sensitive periods may be meaningful because they represent stages of rapid brain maturation, physiological or epigenetic processes, and significant life experiences. Interpreting trajectories and identifying sensitive periods for intervention will be enhanced through a life course perspective that considers developmental events as they are impacted by the complex interplay of individual and contextual factors. Unraveling these complexities will be key to developing novel therapeutic and prevention strategies that hold the promise of reducing both the burdens of mental illness and disparities in mental health outcomes.

The following Objectives further define this Goal:

Multi-generational family enjoying each other’s company on the sofa

Across the lifespan, the brain and the cognitive, behavioral, and affective functioning it supports undergo dramatic changes, in part as a result of diverse experiences. Yet, our understanding of how different experiences interact with biology and psychological development interact to affect brain development and maturation—and, ultimately, healthy and maladaptive behavioral outcomes—is still incomplete. Discoveries through basic and translational research further our fundamental understanding of how mental illnesses develop early or later in life and through the course of illness. By characterizing the trajectories of typical and atypical brain, cognitive, affective, and social development across the lifespan and in diverse populations and contexts, we can identify factors that protect from, increase risk for, or give rise to mental illnesses. Greater understanding is needed to determine periods when the brain is at increased sensitivity to biological and environmental influences and optimal periods for intervention. Exposures during sensitive periods may include viruses and toxins; stressful life events or adversity; environmental factors such as light, temperature, or noise; and, social factors such as violence, discrimination, or harassment based on race, ethnicity, sexual orientation, gender, or disability. It is important to also consider the dynamic and non-linear nature of brain development and aging, account for cumulative effects of risk and protective factors, simultaneously evaluate multiple domains of function, and incorporate life course perspectives.

To better understand developmental and aging trajectories and the progression of mental illnesses, NIMH will support research that employs the following Strategies:

Interest areas include:

  1. Characterizing the interdependence and functional development of simultaneously occurring, yet unevenly progressing, trajectories in different brain regions and circuits across the lifespan.
  2. Determining the biological (e.g., epigenetic processes, neuroinflammation, allostatic load, and hormonal changes in puberty, pregnancy, and menopause) and psychological mechanisms by which experience, social determinants, and natural and built environment affect neural and behavioral development.
  3. Examining individual differences and the inter-relatedness of biological, behavioral, and environmental (including natural and built, social, cultural, and structural) contributors to heterogeneity in risk for and resilience from mental illnesses across the lifespan, trajectories of illnesses, prevention, and treatment interventions. Studies may use multi-level modeling to incorporate an intersectionality framework in mental health research.
  4. Developing novel statistical, computational, and analytical techniques to integrate behavioral, genomic, multi-modal neuroimaging, clinical, environmental, and other data types across repeated assessments and across independent datasets.
  5. Utilizing digital health technologies (e.g., biosensors, wearable devices, analysis of social media use patterns, digital diaries or prompts) to measure behavior, mood, and physiology in real-world environments, including social interactions and social networks.
  6. Understanding the impact of widespread and complex syndemics (e.g., COVID-19, the youth mental health crisis) on increasing risk for mental illness or exacerbating clinical symptoms and poor outcomes for individuals with mental illnesses.

Interest areas include:

  1. Conducting longitudinal studies that track changes in behavior, cognition, and affect; brain maturation, psychosocial and physical development; and, environmental exposures, to characterize the progression from early signs of alterations to subsequent impairment in these domains of functioning. NIMH encourages the use of large and diverse samples, adequately sampled for underserved, minoritized, and underrepresented groups and with sufficient power to examine mediators and moderators including structural, systemic, and interpersonal racism and discrimination.
  2. Identifying the biological mechanisms (e.g., molecular, cellular, circuit) involved in healthy and dysfunctional neurodevelopmental and aging trajectories throughout life, including sex and gender differences in incidence, age of onset, and severity of mental illnesses.
  3. Identifying and characterizing sensitive periods for brain, cognitive, social, and affective development and aging during which core facets of functioning (e.g., RDoC constructs, including social and environmental influences) can be targeted for optimal intervention to prevent, pre-empt, and/or effectively treat mental illnesses across diverse populations.
  4. Translating knowledge about sensitive periods and their critical mechanisms to manipulate developmental and aging trajectories of neural circuits and associated behaviors, to prevent or minimize illness trajectories and promote optimal outcomes, or to provide evidence to inform policies directed at social and environmental determinants of health associated with mental illness risk and severity.

Teenage girl talking to a female counselor

The best time to address a mental illness is before the appearance of symptoms. Preventive interventions will rely on biomarkers and other indicators that give health care providers the ability to predict the onset of illness for individuals, not just populations, at risk. Currently, the mental health field lacks predictors that could inform a diagnosis, guide intervention, or predict response to intervention and the future course of illness. Further, understanding the mechanisms involved in risk and protective factors may shed light on novel intervention targets. Targets can include molecular processes; synaptic- and circuit-level regions or networks; neural systems; intrapersonal factors (e.g., psychological, cognitive, emotional, behavioral); interpersonal processes; and, environmental factors. We need to identify clinically useful biomarkers, behavioral indicators, and measures of social-environmental exposures with high predictive value to guide the use of preventive interventions across diverse populations, environments, and developmental and aging processes.

To lay the foundation for predicting outcomes and optimizing preventive and treatment interventions, NIMH will support research that employs the following Strategies:

Interest areas include:

  1. Identifying early manifestations of core functional domains (see RDoC framework) particularly during infancy, early childhood, adolescence, and other life periods of rapid change (e.g., menopause transition, aging) that predict the onset and course of mental illnesses.
  2. Examining neurobehavioral mechanisms of sequential, additive, and/or interactive combinations of risk, resilience, and protective factors that span modalities and units of analysis and predict progression along the illness trajectory.
  3. Identifying novel intervention targets based on knowledge of neurobehavioral, psychological, and contextual mechanisms and trajectories, and the optimal time points for intervention.
  4. Demonstrating that putative targets are mutable and potentially modifiable.
  5. Using qualitative and mixed methods approaches to identify risk and protective factors, especially community and cultural strengths, that could be engaged by preventive and treatment interventions.

Interest areas include:

  1. Identifying specific, clinically relevant, life stage appropriate, culturally appropriate, and validated biomarkers of risk, onset, progression, recovery, and relapse phases of illnesses.
  2. Using multiple modalities and standardized methods to identify robust mediators, moderators, and predictors of resilience, illness course, and differential trajectories.
  3. Harnessing computational approaches to define and refine biomarkers, and to demonstrate potential clinical utility.
  4. Utilizing mobile and digital health technologies to capture dynamic changes in mood, behavior, and physiology in real-world environments and identify and promptly address increased clinical symptoms or risk for harm.
  5. Capitalizing on big data and computational approaches to identify the existence and potential drivers of mental health disparities and to quantify population-attributable risk for social-environmental factors.
  6. Developing fine-grained, objective, and quantitative behavioral assessment tools in animals and humans to evaluate dysfunction in domains relevant to the trajectories of mental illnesses.
  7. Developing evidence-based risk assessment instruments that encompass multiple domains, are sensitive to developmental and aging stages, and have high predictive power for the onset or recurrence of mental illnesses.
  8. Developing, testing, and refining tools and methodologies that integrate multimodal clinical, behavioral, and biological risk factors to prevent the onset of chronic conditions and optimize outcome.


Progress for Goal 2

Charting mental illness trajectories to determine when, where, and how to intervene.