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

2022; Nature Portfolio; Volume: 9; Issue: 1 Linguagem: Inglês

10.1038/s41597-022-01695-7

2052-4463

Adam Richie-Halford, Matthew Cieslak, Lei Ai, Sendy Caffarra, Sydney Covitz, Alexandre R. Franco, Iliana I. Karipidis, John Kruper, Michael P. Milham, Bárbara Avelar‐Pereira, Ethan Roy, Valerie J. Sydnor, Jason D. Yeatman, Nicholas J. Abbott, John A. E. Anderson, B. Gagana, MaryLena Bleile, Peter S. Bloomfield, Vince Bottom, Josiane Bourque, Rory Boyle, Julia K. Brynildsen, Navona Calarco, Jaime J. Castrellon, Natasha Chaku, Bosi Chen, Sidhant Chopra, Emily B. J. Coffey, Nigel Colenbier, Daniel Cox, James Elliott Crippen, Jacob J. Crouse, Szabolcs Dávid, Benjamin De Leener, Gwyneth Delap, Zhi‐De Deng, Jules R. Dugré, Anders Eklund, Kirsten Ellis, Arielle Ered, Harry Farmer, Joshua Faskowitz, Jody E. Finch, Guillaume Flandin, Matthew W. Flounders, Leon Fonville, Summer Frandsen, Dea Garic, Patricia Garrido-Vásquez, Gabriel González‐Escamilla, Shannon E. Grogans, Mareike Grotheer, David C. Gruskin, Guido I. Guberman, Edda B. Haggerty, Younghee Hahn, Elizabeth H. Hall, Jamie L. Hanson, Yann Harel, Bruno Hebling Vieira, Meike D. Hettwer, Harriet Hobday, Corey Horien, Fan Huang, Zeeshan M. Huque, Anthony R. James, Isabella Kahhalé, Sarah L. H. Kamhout, Arielle S. Keller, Harmandeep Singh Khera, Gregory Kiar, Peter Alexander Kirk, Simon H. Kohl, Stephanie A. Korenic, Cole Korponay, Alyssa K. Kozlowski, Nevena Kraljević, Alberto Lazari, Mackenzie J. Leavitt, Zhaolong Li, Giulia Liberati, Elizabeth S. Lorenc, Annabelle Julina Lossin, Leon D. Lotter, David M. Lydon‐Staley, Christopher R. Madan, Neville Magielse, Hilary A. Marusak, Julien Mayor, Amanda L. McGowan, Kahini Mehta, Steven L. Meisler, Cleanthis Michael, Mackenzie E. Mitchell, Simon Morand‐Beaulieu, Benjamin T. Newman, Jared A. Nielsen, Shane M. O’Mara, Amar Ojha, Adam Omary, Evren Özarslan, Linden Parkes, Madeline Peterson, Adam Pines, Claudia Pisanu, Ryan Rich, Matthew D. Sacchet, Ashish Kumar Sahoo, Amjad Samara, Farah Sayed, Jonathan Thore Schneider, Lindsay S. Shaffer, Ekaterina Shatalina, Sara A. Sims, Skyler Sinclair, Jae W. Song, Griffin Stockton Hogrogian, Christian K. Tamnes, Ursula A. Tooley, Vaibhav Tripathi, Hamid B. Turker, Sofie L. Valk, Matthew B. Wall, Cheryl K. Walther, Yuchao Wang, Bertil Wegmann, Thomas Welton, Alex I. Wiesman, Andrew G. Wiesman, Mark Wiesman, Drew E. Winters, Ruiyi Yuan, Sadie J. Zacharek, Chris Zajner, Ilya Zakharov, Gianpaolo Zammarchi, Dale Zhou, Benjamin Zimmerman, Kurt Zoner, Theodore D. Satterthwaite, Ariel Rokem,

Functional Brain Connectivity Studies

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.