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

Abstract

Brain development has largely been studied through unimodal analysis of neuroimaging data, providing independent results for structural and functional data. However, structure clearly impacts function and vice versa, pointing to the need for performing multimodal data collection and analysis to improve our understanding of brain development, and to further inform models of typical and atypical brain development across the lifespan. Ultimately, such models should also incorporate genetic and epigenetic mechanisms underlying brain structure and function, although currently this area is poorly specified. To this end, we are reporting here a multi-site, multi-modal dataset that captures cognitive function, brain structure and function, and genetic and epigenetic measures to better quantify the factors that influence brain development in children originally aged 9-14 years. Data collection for the Developmental Chronnecto-Genomics (Dev-CoG) study (http://devcog.mrn.org/) includes cognitive, emotional, and social performance scales, structural and functional MRI, diffusion MRI, magnetoencephalography (MEG), and saliva collection for DNA analysis of single nucleotide polymorphisms (SNPs) and DNA methylation patterns. Across two sites (The Mind Research Network and the University of Nebraska Medical Center), data from over 200 participants were collected and these children were re-tested annually for at least 3 years. The data collection protocol, sample demographics, and data quality measures for the dataset are presented here. The sample will be made freely available through the collaborative informatics and neuroimaging suite (COINS) database at the conclusion of the study.

Keywords: Adolescence; Epigenetics; FMRI; Genetics; MRI; Magnetoencephalography; Neurodevelopment; Resting-state.

Figure 1.

A depiction of the Chronnectome.

Figure 2.

Motion in fMRI and MEG across sessions and sites.

Figure 3.

There was a nonsignificant trend for an increase in estimated total intracranial volume (eTIV) with age.

Figure 4.

Demographic measures compared between NM and NE. SES – socioeconomic status, PAQ – physical activity questionnaire, MFQ – moods and feelings questionnaire.

Figure 5.

Parental questionnaires relating to child behavior. Connors Inattention and Hyperactivity – two summary measures from the Connors parent questionnaire to assess ADHD. CBCL – child behavior checklist

Figure 6.

NIH Toolbox measures obtained from the NM and NE sites. DCCS – Dimensional change card sort, SWM – spatial working memory, FICA – Flanker inhibitory control and attention, Oral Reading – Oral reading and recognition, PSM – picture sequence memory, Pic Vocab – picture vocabulary.

Figure 7.

Joint component that was significantly positively correlated with age (r = 0.248). DTI component clusters are shown above and example regions showing regional specificity – significant signal shown in left cingulate cortex (left panel) and no signal represented by this component in left postcentral gyrus (right panel). MEG timecourses were derived from beamformer regions that showed significant activation during a working memory task (Embury et al. 2019).