Goal 2: Examine Mental Illness Trajectories Across the Lifespan
Many mental illnesses first present in childhood, adolescence, or young 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 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 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, when development or aging is going off course, or which preventive and treatment interventions will produce the best outcomes for which individuals. We also need to understand the factors contributing to risk of, resilience to, and protection from development of mental illness.
Examining biological and behavioral processes in animals and humans across the lifespan, starting at the earliest possible point, will transform our understanding of the neurodevelopmental origins and progression of mental illnesses through late life. Research to identify the earliest markers or signs that distinguish typical from atypical brain and/or functional development will be instrumental in predicting illness trajectories and outcomes decades later. Equally important is how these markers differ in meaningful ways across individuals, developmental stages in the lifespan, diverse populations (e.g., age, sex, gender, race, ethnicity), varied experiences, social determinants, environmental factors, and their intersectionality.30
As we chart developmental and aging trajectories across time, 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. Further, to provide new therapeutic avenues to prevent and treat mental illnesses, we must identify a broad range of relevant factors, such as social and environmental influences (including but not limited to violence and trauma), and molecular-, cellular-, and system-level mechanisms affecting typical and atypical development.
The following Objectives further define this Goal:
Across the lifespan, the brain and the cognitive, behavioral, and affective functioning it supports undergo dramatic changes, in part as a result of myriad experiences. Yet, our understanding of how biology, psychological development, and experience interact to affect brain development — and, ultimately, social, behavioral, medical, and other 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 behavioral 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. Further research is needed to determine periods when the brain is at increased sensitivity to biological and environmental influences, and optimal periods for intervention. It is important to also consider the dynamic and non-linear nature of development and aging, simultaneously evaluate multiple domains of function, and incorporate maturational influences.
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:
- In diverse human populations, characterizing the interdependence and functional development of simultaneously occurring, yet unevenly progressing, trajectories in different brain regions and circuits across the lifespan.
- Determining the biological and psychological mechanisms by which experience and environment affect neural and behavioral development.
- Examining individual differences and the inter-relatedness of biological, behavioral, and environmental (including 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.
- Developing novel statistical, computational, and analytical techniques to integrate behavioral, genomic, multi-modal imaging, clinical, environmental, and other data types across repeated assessments and across independent data sets.
Interest areas include:
- Conducting longitudinal studies that track changes in behavior with changes in brain development, psychosocial development, and other normative maturational processes, to characterize the progression from early markers to subsequent impairment in domains of functioning. NIMH encourages the use of large and diverse samples, adequately sampled for underserved, minority, and underrepresented groups and with sufficient power to examine mediators and moderators including race, ethnicity, sex, gender, and sexual orientation.
- Identifying the biological mechanisms (e.g., molecular, cellular, circuit-level) involved in healthy and dysfunctional neurodevelopmental trajectories, including the functional consequences of sex and gender differences that have shown empirical associations to mental illnesses.
- Identifying and characterizing sensitive periods for brain, cognitive, social, and affective development during which core facets of functioning (e.g., RDoC constructs) can be targeted for optimal intervention to prevent, pre-empt, and/or effectively treat mental illnesses across diverse populations.
- Translating knowledge about sensitive periods and their critical mechanisms to manipulate developmental trajectories of neural circuits and associated behaviors to prevent or minimize disease trajectories and promote optimal outcomes.
The best time to address a mental illness is before the appearance of symptoms. Preventive interventions will rely on biomarkers and other predictors 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; psychological, cognitive, emotional, or behavioral processes; and, environmental phenomena. We need to identify clinically useful biomarkers and behavioral indicators with high predictive value to guide the use of preventive interventions across diverse populations and environments.
To lay the foundation for predicting outcomes and prevention and treatment interventions, NIMH will support research that employs the following Strategies:
Interest areas include:
- Identifying early manifestations of core functional domains (see RDoC framework) particularly during infancy, early childhood, and adolescence that predict the onset and course of mental illnesses.
- Examining 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.
- Identifying novel intervention targets based on knowledge of neurobehavioral, psychological, and contextual mechanisms and trajectories, and the optimal time points for intervention.
Interest areas include:
- Identifying specific, clinically relevant, developmentally appropriate, and validated biomarkers (including neural and behavioral indicators) of risk, onset, progression, recovery, and relapse phases of illnesses.
- Using multiple modalities and standardized methods to identify robust mediators, moderators, and predictors of resilience, illness course, and differential trajectories.
- Harnessing computational approaches to define and refine biomarkers, and to demonstrate potential clinical utility.
- 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.
- 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.
- Developing, testing, and refining tools and methodologies that integrate multimodal panels of clinical, behavioral, and biological risk to prevent the onset of chronic conditions and optimize outcome.
Charting mental illness trajectories to determine when, where, and how to intervene.
Updated: July 2021