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A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research
Published on: July 31, 2017
EEG-IP: an international infant EEG data integration platform for the study of risk and resilience in autism and
Stefon van Noordt1, James A Desjardins1,2, Scott Huberty1
1Montreal Neurological Institute, Azrieli Centre for Autism Research, McGill University, Montréal, Canada.
Insights
The International Infant EEG Data Integration Platform (EEG-IP) successfully created the largest standardized infant EEG dataset for autism biomarker discovery. This platform integrates multi-site data, overcoming previous limitations for early identification and intervention research.
Area of Science:
- Neuroscience
- Developmental Psychology
- Biomarker Research
Background:
- Autism spectrum disorder (ASD) biomarkers are crucial for early identification and targeted interventions.
- Current biomarker research is hindered by small sample sizes and the complex nature of ASD.
- Early brain development offers a critical window for intervention during periods of high plasticity.
Purpose of the Study:
- To establish a large-scale, multi-site, standardized infant electroencephalogram (EEG) dataset for autism biomarker discovery.
- To overcome challenges in data comparability across different research sites.
- To advance the development of reliable predictive and diagnostic biomarkers for autism.
Main Methods:
- The International Infant EEG Data Integration Platform (EEG-IP) was developed to pool and harmonize multi-site infant EEG data.
- Data from 432 infants (3-36 months) at risk for autism, including 1382 longitudinal EEG recordings and behavioral data, were integrated.
- The Brain Imaging Data Structure (BIDS)-EEG standard was applied for data harmonization, followed by a common signal processing pipeline for preprocessing.
Main Results:
- EEG-IP successfully created the largest multi-site, standardized infant EEG dataset to date.
- The platform integrated longitudinal EEG and behavioral data from a significant cohort of infants at risk for autism.
- A fully standardized dataset of pooled, harmonized, and pre-processed EEG data from multiple sites was produced.
Conclusions:
- The implementation of integrated solutions for multi-site infant data has shown both success and challenges.
- Challenges include signal source annotation, time synchronization, and independent component analysis (ICA) during preprocessing.
- Future opportunities involve validating analytical pipelines to replicate findings and test new hypotheses in autism research.
Background:
Establishing reliable predictive and diganostic biomarkers of autism would enhance early identification and facilitate targeted intervention during periods of greatest plasticity in early brain development. High impact research on biomarkers is currently limited by relatively small sample sizes and the complexity of the autism phenotype.
Methods:
EEG-IP is an International Infant EEG Data Integration Platform developed to advance biomarker discovery by enhancing the large scale integration of multi-site data. Currently, this is the largest multi-site standardized dataset of infant EEG data.
Results:
First, multi-site data from longitudinal cohort studies of infants at risk for autism was pooled in a common repository with 1382 EEG longitudinal recordings, linked behavioral data, from 432 infants between 3- to 36-months of age. Second, to address challenges of limited comparability across independent recordings, EEG-IP applied the Brain Imaging Data Structure (BIDS)-EEG standard, resulting in a harmonized, extendable, and integrated data state. Finally, the pooled and harmonized raw data was preprocessed using a common signal processing pipeline that maximizes signal isolation and minimizes data reduction. With EEG-IP, we produced a fully standardized data set, of the pooled, harmonized, and pre-processed EEG data from multiple sites.
Conclusions:
Implementing these integrated solutions for the first time with infant data has demonstrated success and challenges in generating a standardized multi-site data state. The challenges relate to annotation of signal sources, time, and ICA analysis during pre-processing. A number of future opportunities also emerge, including validation of analytic pipelines that can replicate existing findings and/or test novel hypotheses.
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