BabyViSTA: Foundational MRI Resources for Early Developing Brain
Poster No:
1806
Submission Type:
Abstract Submission
Authors:
Ruolin Li1,2, Runjia Lin3,4, Sovesh Mohapatra5,2, Fengxia Wu1,6, Wentao Wu1,2, Tianjia Zhu1,2, Cheng-En Lee7, Shufang Tan1,8, Kay Sindabizera7, Minhui Ouyang1,9, Hao Huang10,9
Institutions:
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 2Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 3Children's Hospital of Philadelphia; Dalian University of Technology, Philadelphia, PA, 4School of Software, Dalian University of Technology, Dalian, China, 5University of Pennsylvania, Philadelphia, PA, 6Department of Anatomy and Neurobiology, Shandong University, China, Liaoning, China, 7Children's Hospital of Philadelphia, Philadelphia, PA, 8Graduate School of Education, Peking University, Beijing, China, 9Department of Radiology, University of Pennsylvania, Philadelphia, PA, 10Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA
First Author:
Ruolin Li
Department of Radiology, Children's Hospital of Philadelphia|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Department of Radiology, Children's Hospital of Philadelphia|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Co-Author(s):
Runjia Lin
Children's Hospital of Philadelphia; Dalian University of Technology|School of Software, Dalian University of Technology
Philadelphia, PA|Dalian, China
Children's Hospital of Philadelphia; Dalian University of Technology|School of Software, Dalian University of Technology
Philadelphia, PA|Dalian, China
Sovesh Mohapatra
University of Pennsylvania|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
University of Pennsylvania|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Fengxia Wu
Department of Radiology, Children's Hospital of Philadelphia|Department of Anatomy and Neurobiology, Shandong University, China
Philadelphia, PA|Liaoning, China
Department of Radiology, Children's Hospital of Philadelphia|Department of Anatomy and Neurobiology, Shandong University, China
Philadelphia, PA|Liaoning, China
Wentao Wu
Department of Radiology, Children's Hospital of Philadelphia|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Department of Radiology, Children's Hospital of Philadelphia|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Tianjia Zhu
Department of Radiology, Children's Hospital of Philadelphia|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Department of Radiology, Children's Hospital of Philadelphia|Department of Bioengineering, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Shufang Tan
Department of Radiology, Children's Hospital of Philadelphia|Graduate School of Education, Peking University
Philadelphia, PA|Beijing, China
Department of Radiology, Children's Hospital of Philadelphia|Graduate School of Education, Peking University
Philadelphia, PA|Beijing, China
Minhui Ouyang
Department of Radiology, Children's Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Department of Radiology, Children's Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Hao Huang
Department of Radiology, Children’s Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Department of Radiology, Children’s Hospital of Philadelphia|Department of Radiology, University of Pennsylvania
Philadelphia, PA|Philadelphia, PA
Introduction:
Infancy is marked by rapid and profound structural and functional brain changes. Age-specific multi-modal templates and atlases are essential to quantify these changes and improve precision in infant neuroimaging. This study presents BabyViSTA - a high-fidelity Volumetric-infant Surface-Tractography-consistent Atlas - for 3, 6, 12, and 24 months, offering population-averaged and single-subject datasets.
Methods:
Data acquisition and processing: 348 typically developing infants aged 0 to 24 months were recruited in this study, and 245 infants underwent brain MRI scans at a 3.0T Siemens Prisma MRI system. To construct age-specific templates, scans from 21 infants (2.5–4.4 months), 23 (5.5–7.5 months), 15 (9.1–14.7 months), and 17 toddlers (17–25 months) were used for the 3-, 6-, 12-, and 24-month templates, respectively. T1-weighted images (MPRAGE, 0.8 mm isotropic) and T2-weighted images (SPACE, 0.8 mm isotropic) were acquired. Diffusion MRI (dMRI) images were collected using multi-band EPI (1.2 mm isotropic). All images were preprocessed using the lab's internal pipeline.
Establishment of volumetric infant templates (Fig.1): The volumetric templates in Baby ViSTA were created by a seven-step procedure (Song, 2024; Feng 2019; Oishi 2011). Structural images were AC-PC aligned, co-registered with dMRI, and iteratively improved through affine transformations to produce the population-averaged-linear template. A representative single-subject template was then aligned to this linear template, followed by nonlinear transformations to create the population-averaged nonlinear template. Transformations applied to T1w images were extended to dMRI fields for cross-modality consistency. All templates were rigidly registered to MNI-152 space, preserving the original brain size.
Establishment of infant atlas and surface: A total of 124 gray and white matter structures were precisely labeled and refined by neuroanatomists for anatomical accuracy. Initial segmentations were generated using SynthSeg and manually corrected by research assistants. High-resolution white and pial surfaces, with 32k vertices per hemisphere, enhance anatomical fidelity. The short-range association fibers (SAFs) atlas were constructed using the STTAR tracing (Zhao 2021; Lin 2024) and clustering protocol, with reproducible clusters registered to the 12-month template and integrated into BabyViSTA.
Establishment of volumetric infant templates (Fig.1): The volumetric templates in Baby ViSTA were created by a seven-step procedure (Song, 2024; Feng 2019; Oishi 2011). Structural images were AC-PC aligned, co-registered with dMRI, and iteratively improved through affine transformations to produce the population-averaged-linear template. A representative single-subject template was then aligned to this linear template, followed by nonlinear transformations to create the population-averaged nonlinear template. Transformations applied to T1w images were extended to dMRI fields for cross-modality consistency. All templates were rigidly registered to MNI-152 space, preserving the original brain size.
Establishment of infant atlas and surface: A total of 124 gray and white matter structures were precisely labeled and refined by neuroanatomists for anatomical accuracy. Initial segmentations were generated using SynthSeg and manually corrected by research assistants. High-resolution white and pial surfaces, with 32k vertices per hemisphere, enhance anatomical fidelity. The short-range association fibers (SAFs) atlas were constructed using the STTAR tracing (Zhao 2021; Lin 2024) and clustering protocol, with reproducible clusters registered to the 12-month template and integrated into BabyViSTA.
·Fig.1. Overview of BabyViSTA construction. Resulting single-subject and population-averaged templates in MNI152 space, displayed across four different ages (3m, 6m, 12m, 20m) and seven contrasts.
Results:
Fig. 2 showcases the registration of structural, diffusion, and functional data using linear and nonlinear methods, enabling the integration of additional analysis resources. This framework establishes a robust foundation for comprehensive brain studies. Leveraging this resource, the short-association fibers atlas is revealed in infant brains for the first time. Two reproducible clusters on the 12-month template that connect the left supramarginal gyrus (SMG) and superior parietal gyrus (SPG) were shown.
·Fig. 2. Illustration of the application of BabyViSTA in brain imaging studies.
Conclusions:
This study introduces BabyViSTA - age-specific, multi-modal MRI templates as an initial step of establishing the foundational MRI resources for early developing brain studies. By enabling precise registration and integrating advanced resources such as brain atlases, segmentation maps, and cortical surfaces, BabyViSTA supports detailed analyses of early brain development. The identification of SAFs in infants marks a significant advancement in understanding early connectivity. BabyViSTA establishes a robust foundation for neurodevelopmental research, offering valuable insights into brain structure, function, and connectivity in both health and disease.
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Neuroinformatics and Data Sharing:
Brain Atlases 1
Keywords:
Atlasing
PEDIATRIC
Other - Infant Brain Resource
1|2Indicates the priority used for review
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2. Huang, H. et al. White and gray matter development in human fetal, newborn and pediatric brains. NeuroImage 33, 27–38 (2006).
3. Song, L. et al. Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels. Hum Brain Mapp 45, e26695 (2024).
4. Feng, L. et al. Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks. Neuroimage 185, 685–698 (2019).
5. Oishi, K., Chang, L. and Huang, H., 2019. Baby brain atlases. NeuroImage, 185, pp.865-880.
6. Zhao, C., Ouyang, M., Huang, H. (2021). Atlas of reproducible short-range association fibers in parietal lobe by STTAR tracing and clustering. Processings of ISMRM, 0868
7. Lin, R., Kim, J., Ouyang, M., Fan, X., Huang, H. (2024). A Docker-based Ensemble Pipeline for Tractogram with High-throughput, Reproducible and Comprehensive Mapping of White Matter Fibers (DEPTH). Processings of ISMRM, 2411.