Using brain development patterns to spot early substance-use risk and COVID-19 effects in kids
Application of a Bayesian strategy to ABCD: Identification of substance use risk and COVID-19 effects on neurodevelopment
This project uses brain and behavior patterns from children and teens to find who may be more likely to start using substances and how the COVID-19 pandemic changed their brain development.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project ID | NIH-11261143 on NIH RePORTER |
What this research studies
If you or your child are part of a long-term brain development study, this work looks at your existing brain scans and behavioral data to find typical growth paths and when a child moves away from those patterns. The team applies Bayesian machine-learning and time-series methods to the ABCD study's repeated brain and behavioral measurements to map normal versus at-risk trajectories. They will also compare data from before and during the COVID-19 pandemic to see how pandemic-related changes relate to substance-use vulnerability. Results will be shared as models that researchers and clinicians can use to better spot early risk.
Who could benefit from this research
Good fit: Ideal candidates are children and adolescents (roughly 9–20 years old) who are enrolled in the ABCD study or similar longitudinal brain-development cohorts and who experienced adolescence during the COVID-19 pandemic.
Not a fit: Adults outside the adolescent age range or people not enrolled in ABCD-like longitudinal studies are unlikely to see direct benefits from this specific analysis.
Why it matters
Potential benefit: If successful, this could help spot children at higher risk earlier so families and clinicians can target prevention and support before problems start.
How similar studies have performed: Prior ABCD and other brain-imaging studies have linked development patterns to later substance use, but applying Bayesian hierarchical time-series to quantify pandemic-related deviations is a newer approach with limited prior precedent.
Where this research is happening
New Haven, United States
- Yale University — New Haven, United States (Active)
Researchers
- Principal investigator: Yip, Sarah — Yale University
- Study coordinator: Yip, Sarah
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.