Search

Abstract 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.

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. Measurement(s) brain white matter Technology Type(s) Diffusion Weighted Magnetic Resonance Imaging Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location contiguous United States of America [ABSTRACT FROM AUTHOR]

The corrected membership list has replaced the incorrect version in the pdf and HTML versions of the article. Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01695-7, published online 12 October 2022 The list of members of the Fibr Community Science Consortium in the original version of the paper incorrectly omitted consortium member Christian K. Tamnes, University of Oslo, Norway. [Extracted from the article]

Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01695-7, published online 12 October 2022 In this article the affiliation details for Bárbara Avelar-Pereira were incorrectly given as "University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland" but should have been "Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA". These authors contributed equally: Adam Richie-Halford, Matthew Cieslak. [Extracted from the article]