An analysis-ready and quality controlled resource for pediatric brain white-matter researchLifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Childrens Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA; Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA.
Stanford University, Graduate School of Education, Stanford, California, 94305, USA; Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA; University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland.
University of Washington, Department of Psychology, Seattle, Washington, 98195, USA.
Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA; Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA.
University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland.
Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Childrens Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA; Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Childrens Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
University of Washington, Department of Psychology, Seattle, Washington, 98195, USA; University of Washington, eScience Institute, Seattle, Washington, 98195, USA.
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Rekke forfattare: 1392022 (engelsk)Inngår i: Scientific Data, E-ISSN 2052-4463, Vol. 9, nr 1Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]
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.
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Nature Publishing Group , 2022. Vol. 9, nr 1
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Identifikatorer
URN: urn:nbn:se:liu:diva-189363DOI: 10.1038/s41597-022-01695-7ISI: 000866490900002PubMedID: 36224186OAI: oai:DiVA.org:liu-189363DiVA, id: diva2:1704783
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