An analysis-ready and quality controlled resource for pediatric brain white-matter research
Authors: Richie-Halford Adam , Cieslak Matthew , Ai Lei , Caffarra Sendy , Covitz Sydney , Franco Alexandre R. , Karipidis Iliana I. , Kruper John , Milham Michael , Avelar-Pereira Bárbara , Roy Ethan , Sydnor Valerie J. , Yeatman Jason D. ,   The Fibr Community Science Consortium, Abbott Nicholas J. , Anderson John A. E. , Gagana B. , Bleile MaryLena , Bloomfield Peter S. , Bottom Vince , Bourque Josiane , Boyle Rory , Brynildsen Julia K. , Calarco Navona , Castrellon Jaime J. , Chaku Natasha , Chen Bosi , Chopra Sidhant , Coffey Emily B. J. , Colenbier Nigel , Cox Daniel J. , Crippen James Elliott , Crouse Jacob J. , David Szabolcs , Leener Benjamin De , Delap Gwyneth , Deng Zhi-De , Dugre Jules Roger , Eklund Anders , Ellis Kirsten , Ered Arielle , Farmer Harry , Faskowitz Joshua , Finch Jody E. , Flandin Guillaume , Flounders Matthew W. , Fonville Leon , Frandsen Summer B. , Garic Dea , Garrido-Vásquez Patricia , Gonzalez-Escamilla Gabriel , Grogans Shannon E. , Grotheer Mareike , Gruskin David C. , Guberman Guido I. , Haggerty Edda Briana , Hahn Younghee , Hall Elizabeth H. , Hanson Jamie L. , Harel Yann , Vieira Bruno Hebling , Hettwer Meike D. , Hobday Harriet , Horien Corey , Huang Fan , Huque Zeeshan M. , James Anthony R. , Kahhale Isabella , Kamhout Sarah L. H. , Keller Arielle S. , Khera Harmandeep Singh , Kiar Gregory , Kirk Peter Alexander , Kohl Simon H. , Korenic Stephanie A. , Korponay Cole , Kozlowski Alyssa K. , Kraljevic Nevena , Lazari Alberto , Leavitt Mackenzie J. , Li Zhaolong , Liberati Giulia , Lorenc Elizabeth S. , Lossin Annabelle Julina , Lotter Leon D. , Lydon-Staley David M. , Madan Christopher R. , Magielse Neville , Marusak Hilary A. , Mayor Julien , McGowan Amanda L. , Mehta Kahini P. , Meisler Steven Lee , Michael Cleanthis , Mitchell Mackenzie E. , Morand-Beaulieu Simon , Newman Benjamin T. , Nielsen Jared A. , O’Mara Shane M. , Ojha Amar , Omary Adam , Özarslan Evren , Parkes Linden , Peterson Madeline , Pines Adam Robert , Pisanu Claudia , Rich Ryan R. , Sacchet Matthew D. , Sahoo Ashish K. , Samara Amjad , Sayed Farah , Schneider Jonathan Thore , Shaffer Lindsay S. , Shatalina Ekaterina , Sims Sara A. , Sinclair Skyler , Song Jae W. , Hogrogian Griffin Stockton , Tamnes Christian K. , Tooley Ursula A. , Tripathi Vaibhav , Turker Hamid B. , Valk Sofie Louise , Wall Matthew B. , Walther Cheryl K. , Wang Yuchao , Wegmann Bertil , Welton Thomas , Wiesman Alex I. , Wiesman Andrew G. , Wiesman Mark , Winters Drew E. , Yuan Ruiyi , Zacharek Sadie J. , Zajner Chris , Zakharov Ilya , Zammarchi Gianpaolo , Zhou Dale , Zimmerman Benjamin , Zoner Kurt , Satterthwaite Theodore D. , Rokem Ariel 
Published: October 2022

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.