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The Developmental Chronnecto-Genomics (Dev-CoG) study: A multimodal study on the developing brain.

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  • 1The Mind Research Network a division of Lovelace Biomedical Research Institute, Albuquerque, NM, United States.

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|October 11, 2020
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Summary
This summary is machine-generated.

This study introduces a multimodal dataset for tracking child brain development, integrating brain imaging, cognitive, and genetic data to understand influencing factors. The data will be publicly available to advance developmental neuroscience research.

Keywords:
AdolescenceEpigeneticsFMRIGeneticsMRIMagnetoencephalographyNeurodevelopmentResting-state

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Genetics

Background:

  • Traditional brain development studies often use unimodal neuroimaging, limiting understanding of structure-function relationships.
  • Integrating structural and functional brain data is crucial for accurate models of typical and atypical development.
  • Incorporating genetic and epigenetic factors is essential but currently under-specified in developmental models.

Purpose of the Study:

  • To present a novel multi-site, multimodal dataset (Developmental Chronnecto-Genomics, Dev-CoG) for studying child brain development.
  • To capture comprehensive data including cognitive, emotional, social, structural, functional, and genetic measures.
  • To facilitate research into the factors influencing brain development from childhood through adolescence.

Main Methods:

  • Collected data from over 200 children (aged 9-14 years) across two sites, with annual re-testing for at least 3 years.
  • Utilized structural MRI, functional MRI, diffusion MRI, magnetoencephalography (MEG), cognitive/emotional/social scales, and saliva for genetic analysis (SNPs, DNA methylation).
  • Ensured data quality and documented protocols, with plans for public data release via the COINS database.

Main Results:

  • The Dev-CoG study successfully collected a rich, longitudinal, multimodal dataset on child brain development.
  • Detailed protocols, demographics, and quality control measures for the dataset are presented.
  • The dataset encompasses a wide range of measures crucial for understanding brain development.

Conclusions:

  • The Dev-CoG dataset provides a valuable resource for investigating the interplay of structure, function, and genetics in brain development.
  • Availability of this data will foster advanced research into typical and atypical developmental trajectories.
  • Future research can leverage this resource to build more comprehensive models of human brain development across the lifespan.