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Multi-site EEG studies in early infancy: Methods to enhance data quality.
Abigail Dickinson1, Madison Booth2, Manjari Daniel1
1Center for Autism Research and Treatment, Semel Institute for Neuroscience, University of California, Los Angeles, CA, USA.
Developmental Cognitive Neuroscience
|August 20, 2024
Summary
Early brain differences in autism spectrum disorder (ASD) are detectable before symptoms appear. This study successfully integrated electroencephalography (EEG) into infant research, showing high data retention for early ASD detection.
Area of Science:
- Neuroscience
- Developmental Psychology
- Pediatric Research
Background:
- Autism spectrum disorder (ASD) is associated with early brain differences that precede observable symptoms.
- Understanding these neural precursors is vital for advancing developmental pathways and enabling earlier identification of ASD.
- Electroencephalography (EEG) is a scalable and well-tolerated tool for studying neural activity in infants, making it suitable for early ASD research.
Purpose of the Study:
- To describe the protocol for collecting longitudinal, high-density EEG data from infants at high familial risk for ASD.
- To report interim feasibility and data quality results from integrating EEG into a multi-site study.
- To identify key factors influencing data attrition and provide practical guidance for future research.
Main Methods:
- Integration of high-density EEG into a multi-site MRI study (Infant Brain Imaging Study Network).
- Collection of longitudinal EEG data from infants with a higher familial likelihood of developing ASD across five sites.
- Evaluation of feasibility through successful EEG paradigm collection rates and assessment of data quality based on artifact removal and retention rates.
Main Results:
- Preliminary analysis indicates low data loss, with average in-session loss rates of 4.16% and quality control loss rates of 11.66%.
- The overall task-free data retention rate, considering both in-session and quality control factors, was 84.16%.
- High consistency in data retention and quality was observed across all participating sites.
Conclusions:
- The established EEG protocol is feasible and yields high-quality data for studying early neural differences in infants at risk for ASD.
- The findings provide valuable insights into sources of data attrition, informing strategies for optimizing data collection in similar longitudinal infant studies.
- This approach supports the advancement of understanding developmental pathways and the potential for earlier identification of ASD.
