Adolescent Brain Cognitive Development (ABCD) Community MRI Collection and Utilities

Abstract

The Adolescent Brain Cognitive Development Study (ABCD), a 10 year longitudinal neuroimaging study of the largest population based and demographically distributed cohort of 9-10 year olds (N=11,877), was designed to overcome reproducibility limitations of prior child mental health studies. Besides the fantastic wealth of research opportunities, the extremely large size of the ABCD data set also creates enormous data storage, processing, and analysis challenges for researchers. To ensure data privacy and safety, researchers are not currently able to share neuroimaging data derivatives through the central repository at the National Data Archive (NDA). However, sharing derived data amongst researchers laterally can powerfully accelerate scientific progress, to ensure the maximum public benefit is derived from the ABCD study. To simultaneously promote collaboration and data safety, we developed the ABCD-BIDS Community Collection (ABCC), which includes both curated processed data and software utilities for further analyses. The ABCC also enables researchers to upload their own custom-processed versions of ABCD data and derivatives for sharing with the research community. This NeuroResource is meant to serve as the companion guide for the ABCC. In section we describe the ABCC. Section II highlights ABCC utilities that help researchers access, share, and analyze ABCD data, while section III provides two exemplar reproducibility analyses using ABCC utilities. We hope that adoption of the ABCC’s data-safe, open-science framework will boost access and reproducibility, thus facilitating progress in child and adolescent mental health research.

Reproducible brain-wide association studies require thousands of individuals

“…In addition to data from the ABCD 2.0 release, we used the ABCD reproducible matched samples 51 (ARMS), available in ABCD collection 3165, that divided individuals from the full behavioural sample (n = 11,572) into discovery (n = 5,786) and replication (n = 5,786) sets, which were matched across 9 variables: site location, age, sex, ethnicity, grade, highest level of parental education, handedness, combined family income, and prior exposure to anaesthesia. Family members (that is, sibling pairs, twins and triplets) were kept together in the same set and the two sets were matched to include equal numbers of single participants and family members.…”

“…The final discovery and replication sets did not differ in mean framewise displacement (difference in means = 0.002, t = 0.60, P = 0.55) or total frames included (difference in means = 6.4, t = 0.94, P = 0.35). The participant lists for ARMS samples can be found in the ABCD-BIDS Community Collection (ABCD collection 3165) for community use 51 .…”

“…ABCD and UKB MRI data processing was completed with the freely available ABCD-BIDS pipeline 51 (https://github.com/DCAN-Labs/ abcd-hcp-pipeline). Data were downloaded and converted to the BIDS format using ABCD-Dicom2BIDS (https://github.com/DCAN-Labs/ abcd-dicom2bids).…”

A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

“…This latter function can be of particular use if you need to create a dataframe that combines measures that are stored in separate text files (e.g., a data frame with age, race, study site, and head motion). Note, ABCD data are available in multiple formats, including raw data files as well as community-processed output ( Feczko et al, 2021 ), but the data output discussed in the present paper refers to the curated output.…”

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

“…We sought to delineate the relationship between functional topography and individual differences in cognition by conducting a replication and extension of Cui et al 16 in two large, matched samples of youth from the Adolescent Brain Cognitive Development℠ (ABCD) Study [39][40][41] (total n=6,972). Using spatially-regularized non-negative matrix factorization, 80 we identified personalized functional brain networks that captured inter-individual heterogeneity in functional topography while maintaining interpretability.…”