{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Richie-Halford A"],"funding":["NIMH NIH HHS","U.S. Department of Health & Human Services | NIH | National Institute of Mental Health","U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering"],"pagination":["616"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9556519"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_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 "],"journal":["Scientific data"],"pubmed_title":["An analysis-ready and quality controlled resource for pediatric brain white-matter research."],"pmcid":["PMC9556519"],"funding_grant_id":["R01MH120482","R01 MH120482","1R01EB027585-01","1RF1MH121868-01"],"pubmed_authors":["Hogrogian GS","Sinclair S","Brynildsen JK","Caffarra S","Gonzalez-Escamilla G","Richie-Halford A","Sydnor VJ","Lydon-Staley DM","Crouse JJ","Sacchet MD","Ozarslan E","Cox DJ","Wiesman AI","Wiesman AG","Kohl SH","Castrellon JJ","Yuan R","Schneider JT","Korenic SA","Korponay C","Colenbier N","Calarco N","Peterson M","Lossin AJ","Madan CR","Kozlowski AK","Wiesman M","Shatalina E","Hahn Y","Tooley UA","Garrido-Vasquez P","Faskowitz J","Grotheer M","Pisanu C","Gruskin DC","Coffey EBJ","Zoner K","Fonville L","Grogans SE","Rokem A","Vieira BH","Deng ZD","Kraljevic N","Hanson JL","Parkes L","Sayed F","Karipidis II","Sims SA","Song JW","Roy E","Bourque J","Liberati G","Keller AS","Khera HS","Zhou D","O'Mara SM","Winters DE","Zajner C","Huque ZM","Frandsen SB","Kiar G","Newman BT","Yeatman JD","Rich RR","Ai L","Wall MB","Farmer H","Kirk PA","Shaffer LS","Welton T","Zimmerman B","Anderson JAE","Bleile M","Cieslak M","Abbott NJ","Ered A","Eklund A","Dugre JR","Guberman GI","Franco AR","Ojha A","Huang F","Leavitt MJ","Zakharov I","Kruper J","Chopra S","Avelar-Pereira B","Haggerty EB","Horien C","Michael C","Finch JE","Gagana B","Flounders MW","Zammarchi G","Marusak HA","Hobday H","Wang Y","Hall EH","Delap G","Harel Y","Zacharek SJ","Ellis K","Hettwer MD","Morand-Beaulieu S","Tamnes CK","Wegmann B","Bottom V","Chen B","Lorenc ES","Garic D","Valk SL","Magielse N","Bloomfield PS","Mayor J","Li Z","Lotter LD","Walther CK","Boyle R","James AR","Satterthwaite TD","Crippen JE","Lazari A","Milham M","McGowan AL","Pines AR","Turker HB","Meisler SL","Fibr Community Science Consortium","Kamhout SLH","Omary A","Leener B","Mitchell ME","Nielsen JA","Chaku N","Samara A","David S","Mehta KP","Covitz S","Flandin G","Kahhale I","Tripathi V","Sahoo AK"],"additional_accession":[]},"is_claimable":false,"name":"An analysis-ready and quality controlled resource for pediatric brain white-matter research.","description":"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 ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2025-04-26T09:45:13.631Z","creation":"2024-10-15T20:44:06.421Z"},"accession":"S-EPMC9556519","cross_references":{"pubmed":["36224186"],"doi":["10.1038/s41597-022-01695-7"]}}