Priorities for Strategy 2.2
Updated: January 2019
Identify clinically useful biomarkers and behavioral indicators that predict change across the trajectory of illness
The goals of this strategy are to identify, early in the development of mental illnesses, biomarkers and behavioral indicators with high predictive value to guide the use of preventive interventions across diverse populations and contexts. NIMH invites applications that aim to identify early biological, behavioral, and environmental risk and protective factors and their underlying mechanisms, to serve as novel intervention targets. The Institute also invites applications to develop biomarkers and assessment tools with high specificity to predict illness onset, course, and intervention response across diverse populations. NIMH encourages research applications that include biomarkers for stratification purposes; examine novel potential risk and resilience factors and the complex relationships between risk and mental illness; focus on defining directed intervention targets or health outcomes; and, examine factors that can predict, with high probability, disease development or progression.
- Identify early biological and environmental risk and protective factors and their underlying mechanisms to serve as novel intervention targets.
Priority areas include:
- Identifying pre- or early symptomatic (prodromal) manifestations of core domains of function that predate and predict the onset and/or course of mental illnesses and delineate the pathophysiological trajectory leading to mental illnesses, particularly during infancy, early childhood, and adolescence.
- Examining mechanisms of sequential, additive, and/or interactive combinations of risk and protective factors that span modalities and levels of analysis (e.g., genetic, brain, neurobiological, environmental, and behavioral) and predict movement along the illness trajectory.
- Using multiple modalities and standardized methods to identify robust mediators, moderators, and predictors of intervention response throughout the life course and across the trajectory of illness.
- Identifying novel intervention targets based on knowledge of neurobehavioral mechanisms and trajectories, and the optimal time points for intervention.
- Develop biomarkers and assessment tools to predict illness onset, course, and intervention response across diverse populations.
Priority areas include:
- Identifying and validating highly sensitive and specific biomarkers and/or behavioral indicators for functional trajectories, particularly for risk, prodrome, onset, progression, recovery, and relapse phases of illness.
- Developing biosignatures of clinically relevant and validated biomarkers and behavioral indicators.
- Developing fine-grained, objective, and quantitative behavioral assessment tools that can be used in all individuals across the lifespan to evaluate dysfunction in domains relevant to mental illnesses (e.g., positive/negative valence, cognition, social processes, arousal/regulatory processes), including laboratory-based measures as well as passive assessment of ecologically valid behaviors in the real world.
- Developing and validating passively acquired measures of naturalistic behaviors that index subjective states that cannot themselves be objectively measured (such as facial and vocal expression to index mood states, or natural language processing of speech content to index disrupted thought process).
- Developing evidence-based risk assessment instruments that encompass multiple domains, are sensitive to developmental stage, and have high predictive power for the onset or recurrence of mental illness.
- Developing, testing, and refining tools and methodologies (e.g., dynamic computational approaches such as machine learning forecasting) that can harness and integrate multimodal panels of clinical, behavioral, and biological risk factors among prodromal or high-risk groups for personalized risk and trajectory prediction and intervention to minimize negative functional outcomes and prevent the onset of chronic conditions.