Year: 2025
Type: data-paper
Abstract: Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold-standard brain segmentations. These segmentations are limited by the difficulty of infant brain segmentations, which require extensive neuroanatomical knowledge and are time-consuming in nature. Addressing this, we constructed the Baby Open Brains (BOBs) Dataset, an open source resource of manually curated and expert reviewed infant brain segmentations. Anatomical MRI data was segmented from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Images showed dramatic differences in myelination and intensities across 1-9 months, emphasizing the need for densely sampled gold-standard segmentations across early life. This dataset provides a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this dataset provides a vitally needed foundation for early-life large-scale studies such as HBCD.
Source: Scientific Data
Authors: Eric Feczko, Sally M. Stoyell, Lucille A. Moore, Dimitrios Alexopoulos, Maria Bagonis, Kenneth Barrett, Brad Bower, Addison Cavender, Taylor A. Chamberlain, Greg Conan, Trevor K. M. Day, Dhruman Goradia, Alice Graham, Lucas Heisler-Roman, Timothy J. Hendrickson, Audrey Houghton, Omid Kardan, Elizabeth A. Kiffmeyer, Erik G. Lee, Jacob T. Lundquist, Carina Lucena, Tabitha Martin, Anurima Mummaneni, Mollie Myricks, Pranav Narnur, Anders J. Perrone, Paul Reiners, Amanda R. Rueter, Hteemoo Saw, Martin Styner, Sooyeon Sung, Barry Tiklasky, Jessica L. Wisnowski, Essa Yacoub, Brett Zimmermann, Christopher D. Smyser, Monica D. Rosenberg, Damien A. Fair, Jed T. Elison
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Awards: Healthy Brain and Child Development National Consortium Data Coordinating Center, G1489520966, G1933976981, UNIVERSITY OF CHICAGO - to improve functional MRI data signal-to-noise and build models that use resting-state functional connectivity data collected early in life to pred..., G2569697410, G268108513, G285484772, G2898801580, G3674523700, Graduate Research Fellowship Program (GRFP), UNC/UMN Baby Connectome Project, G4412610992, G4436559511, G4692438929, Precision mapping of individualized executive networks in youth, G5534083359, G5790264296, G597442328, G6159100682, Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD, G6955144958, Infant Brain and Behavioral Signatures of Later Emerging Risk for Psychopathology, G843551848, G8867463127, 6/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT OHSU, Predoctoral Training of Neuroscientists
OpenAlex ID: W4413140499