An analysis-ready and quality controlled resource for pediatric brain white-matter research
The Fibr Community Science Consortium
(2022) Scientific data, volume 9, issue 1
(Article)
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
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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.
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Keywords: Brain/diagnostic imaging, Child, Diffusion Magnetic Resonance Imaging/methods, Humans, Image Processing, Computer-Assisted/methods, Neuroimaging, White Matter/diagnostic imaging, Information Systems, Education, Library and Information Sciences, Statistics and Probability, Computer Science Applications, Statistics, Probability and Uncertainty, Dataset, Journal Article, Research Support, N.I.H., Extramural
ISSN: 2052-4463
Publisher: Nature Research
Note: Funding Information: We would like to thank Anisha Keshavan for useful discussions of community science and web-based quality control and for her work on SwipesForScience. We would like to thank Adina S. Wagner, Yaroslav O. Halchenko, and Michael Hanke for their guidance in creating the HBN-POD2 Datalad dataset. We thank Samuel Buck Johnson for useful discussions of data quality for the HBN structural MRI data. This manuscript was prepared using a limited access dataset obtained from the Child Mind Institute Biobank, The Healthy Brain Network dataset. This manuscript reflects the views of the authors and does not necessarily reflect the opinions or views of the Child Mind Institute. This work was supported via BRAIN Initiative grant 1RF1MH121868-01 from the National Institutes of Mental Health. Additional support was provided by grant 1R01EB027585-01 from the National Institutes of Biomedical Imaging and Bioengineering (PI: Eleftherios Garyfallidis). Additional support was provided by R01MH120482 and the Penn/CHOP Lifespan Brain Institute. Funding Information: We would like to thank Anisha Keshavan for useful discussions of community science and web-based quality control and for her work on SwipesForScience. We would like to thank Adina S. Wagner, Yaroslav O. Halchenko, and Michael Hanke for their guidance in creating the HBN-POD2 Datalad dataset. We thank Samuel Buck Johnson for useful discussions of data quality for the HBN structural MRI data. This manuscript was prepared using a limited access dataset obtained from the Child Mind Institute Biobank, The Healthy Brain Network dataset. This manuscript reflects the views of the authors and does not necessarily reflect the opinions or views of the Child Mind Institute. This work was supported via BRAIN Initiative grant 1RF1MH121868-01 from the National Institutes of Mental Health. Additional support was provided by grant 1R01EB027585-01 from the National Institutes of Biomedical Imaging and Bioengineering (PI: Eleftherios Garyfallidis). Additional support was provided by R01MH120482 and the Penn/CHOP Lifespan Brain Institute. Publisher Copyright: © 2022, The Author(s).
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