BRAIN Initiative: Brain Behavior Quantification and Synchronization (R61/R33 Clinical Trial Optional)

RFA-MH-22-240

Achieving higher-resolution quantification of complex behavior in more naturalistic environments in humans is especially crucial for understanding higher-order cognitive functions, whose associated brain activity may be readily available to train and control closed-loop neuromodulatory devices designed to treat complex behavioral disorders and interrupt maladaptive behaviors before they occur (such as self-harm, substance misuse, etc.). Long-term objectives of this research are 1) to advance understanding of how the brain gives rise to complex behaviors; 2) to answer questions related to brain-behavior associations; and 3) to enable the development of interventions (e.g., closed-loop methodologies) for complex neurobehavioral, neurodegenerative, neurodevelopmental or communication disorders.

This RFA focuses on the development of cutting-edge tools that can transition from lab-based settings to naturalistic or home-based environments. Tools may be developed to explore a broad spectrum of naturally occurring behavior, across multiple environments, in both health and disease states. Such tools should seek to innovate technological approaches (e.g., photonics, light detection and ranging [LiDAR]) to measure and integrate multiple behavioral dimensions (e.g., body kinetics, vocalizations) and capture responses to different acute and/or longer-lasting environmental challenges. Novel behavioral measurement tools and analytical approaches should be compatible for use in individuals across the lifespan, in NIH-designated U.S. health disparity populations, in diverse sociocultural settings, and in a range of disease states.

Research that is appropriate for this RFA includes (but is not limited to):

• Development of hardware and/or software tools that advance novel methods to capture and quantify multiple dimensions of behavior in real time
• Development of hardware and/or software tools to advance environmental sensing (e.g., Internet of Things [IoT]) and/or to improve integration of contextual measures with measures of behavior
• Novel application and/or utilization of existing smart hardware technologies (e.g., phones, wearable technology) to capture dynamic behavior and/or to integrate behavioral and physiological measures at the same time scale
• Development of less obtrusive, ambulatory devices that are wireless (e.g., no backpack), that have longer term and high storage capacity (e.g., memory or power consumption that allows for sampling across days as opposed to intermittently) to achieve a higher temporal resolution and/or usage across temporal scales (e.g., from milliseconds to days)
• Development and validation of reliable tools that can passively obtain objective measures that accurately reflect or predict subjective or internal mental states
• Development of novel approaches that integrate passive measures of behavior with subjective reports of individuals’ internal states (e.g., subjective mood or cognitive state using ecological momentary assessments [EMAs])
• Development of novel approaches to integrate multiple data modalities and/or data streams (e.g., integration of peripheral biophysiological measures with complex behaviors)
• Development of novel analytic tools and approaches (e.g., ML/AI methods) focused on behavioral quantification and/or novel conceptual or computational frameworks that incorporate integration/synchronization of multi-modal data streams

Combining existing tools in a novel way (e.g., multimodal integration) to improve behavioral classification with advanced precision or temporal resolution is encouraged. The use of concomitant invasive and non-invasive approaches is encouraged. Studies that include environmental sensing and those that enable studies across the lifespan, in populations and communities that experience disparities in health outcomes (see NIH-designated U.S. health disparity populations), in both health and disease states, are strongly encouraged. Studies that incorporate multidisciplinary teams that include behavioral scientists, neuroscientists, and computer/data science and engineering are strongly encouraged.

Quality assurance processes for data collection, annotation, open source/open sharing, and dissemination are required. Because the responsible collection, storage, and use of these data can raise potential ethical challenges, demonstration that these considerations have been incorporated into the application is required. To facilitate the integration of these ethical considerations into the proposed research, the inclusion of an ethicist on the research team is also required.

Application budgets are not limited but need to reflect the actual needs of the proposed project.

30 days prior to the application due date

February 17, 2023

Lizzy Ankudowich, Ph.D., National Institute of Mental Health (NIMH), Telephone: 301-480-8187, Email: lizzy.ankudowich@nih.gov