4 lines
No EOL
3.3 KiB
Text
4 lines
No EOL
3.3 KiB
Text
"Project #","Project Title","Research Focus Area","Research Program","Administering IC(s)",Institution(s),Investigator(s),Location(s),"Year Awarded",Summary
|
||
1R01DA059415-01,"Integrating Eye-Tracking and ECG Methodologies for Remote Infant Neurocognitive Assessments in the Home","Enhanced Outcomes for Infants and Children Exposed to Opioids","Virtual Assessments to Understand Developmental Trajectories of Substance Use Exposure",NIDA,"NEW YORK UNIVERSITY","BRITO, NATALIE HIROMI","New York, NY",2023,"Use of remote data collection in developmental research can make it easier for families to participate in such research and increase sociodemographic diversity of participants. The goal of this project is to validate remote methods for testing early cognitive development, particularly attention and memory skills, in 4-, 8-, and 12-month-old infants from traditionally underrepresented populations in neuroscience research. The project will integrate multiple types of data to improve remote measurement of infant cognition within the home and will help expand understanding of developmental trajectories and mechanisms across diverse environments and contexts."
|
||
1R01DA059423-01,"Automated Assessment of Maternal Sensitivity to Infant Distress: Leveraging Wearable Sensors for Substance Use Disorder Prevention and Research","Enhanced Outcomes for Infants and Children Exposed to Opioids","Virtual Assessments to Understand Developmental Trajectories of Substance Use Exposure",NIDA,"UNIVERSITY OF TEXAS AT AUSTIN","DE BARBARO, KAYA","Austin, TX",2023,"High-quality parent-infant interactions set the stage for secure parent-child attachment, self-reliance, and children’s ability to flexibly solve problems and “bounce back” from difficulties. This constellation of behaviors reduces the risk of developing substance use disorders later in life. This project will develop algorithms that use data from wearable sensors, trained separately for English- and Spanish-speaking families, to assess the quality of early mother-infant interactions objectively, automatically, and remotely in natural home environments, with the goal of developing tools to facilitate identification and prevention of early risks for substance use disorders."
|
||
1R01DA059422-01,"Validation of a Virtual Still Face Procedure and Deep Learning Algorithms to Assess Infant Emotion Regulation and Infant-Caregiver Interactions in the Wild","Enhanced Outcomes for Infants and Children Exposed to Opioids","Virtual Assessments to Understand Developmental Trajectories of Substance Use Exposure",NIDA,"UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN","MCELWAIN, NANCY L (contact); HASEGAWA-JOHNSON, MARK ALLAN","Champaign, IL",2023,"Both an infant’s ability to regulate their emotions and infant-parent interactions are critical to healthy brain and behavioral development. Accurate assessment of these factors for research in laboratory settings is technically difficult and burdensome for participants. Next-generation methods that can be used at home, including wearable sensors and machine learning approaches, promise to make it easier to assess infants with prenatal substance exposures. This project will use remote sensing technologies and machine learning to characterize dynamic real-time infant emotion regulation and infant-caregiver interactions throughout the day and in the home." |