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An analysis-ready and quality controlled resource for pediatric brain white-matter research

Richie-Halford, Adam; Cieslak, Matthew; Ai, Lei; Caffarra, Sendy; Covitz, Sydney; Franco, Alexandre R.; Karipidis, Iliana I.; Kruper, John; Milham, Michael; Avelar-Pereira, Bárbara; Roy, Ethan; Sydnor, Valerie J.; Yeatman, Jason D.; The Fibr Community Science Consortium; Satterthwaite, Theodore; Rokem, Ariel


Adam Richie-Halford

Matthew Cieslak

Lei Ai

Sendy Caffarra

Sydney Covitz

Alexandre R. Franco

Iliana I. Karipidis

John Kruper

Michael Milham

Bárbara Avelar-Pereira

Ethan Roy

Valerie J. Sydnor

Jason D. Yeatman

The Fibr Community Science Consortium

Theodore Satterthwaite

Ariel Rokem



We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.


Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S., Franco, A. R., …Rokem, A. (2022). An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific Data, 9(1), Article 616.

Journal Article Type Article
Acceptance Date Sep 12, 2022
Online Publication Date Oct 12, 2022
Publication Date Oct 12, 2022
Deposit Date Oct 15, 2022
Publicly Available Date Oct 18, 2022
Journal Scientific data
Electronic ISSN 2052-4463
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 9
Issue 1
Article Number 616
Public URL
Publisher URL


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