Four-Dimensional Mapping of Dynamic Subcortical Development of Infant Brains

Authors

Liangjun Chen¹ , Ya Wang¹ , Zhengwang Wu¹ , Yue Shan¹ , Tengfei Li¹ , Sheng-Che Hung¹ , Hongtu Zhu¹ , Weili Lin¹ , Li Wang¹ , Gang Li¹

1. University of North Carolina at Chapel Hill

Conference / event

Organization for Human Brain Mapping 2022, June 2022 (Virtual)

Poster summary

In this study, we investigate the dynamic early developmental patterns of the thalamus, caudate, putamen, pallidum, hippocampus, and amygdala, by leveraging 513 high-resolution longitudinal MRI scans densely covering the first two postnatal years. By exploring the gross volumetric development and spatiotemporally-detailed surface areal expansion of each subcortical structure, our study reveals that (1) each subcortical structure (except for the amygdala with an approximately linear increase) exhibits a distinct rapid volumetric growth after birth, then its growth rate slows down starting from a structure-specific age; (2) males gradually exhibit larger relative volume than females in the thalamus, putamen, pallidum, and hippocampus after birth; (3) the areal expansion of each subcortical structure is unique with high spatiotemporal heterogeneities. This study greatly advances our understanding of the dynamic and heterogeneous longitudinal developmental patterns of the subcortical structures during infancy.

Keywords

Infant, Subcortical structures, MRI, Developmental trajectory, Surface area

Research areas

Bioinformatics and Genomics, Neuroscience

References

1. Howell, et al., “The unc/umn baby connectome project (bcp): an overview of the study design and protocol development.” NeuroImage, 185, 2019: 891–905.
2. Wang, et al., Volume-based analysis of 6-month-old infant brain mri for autism biomarker identification and early diagnosis.” In: MICCAI, 2018.
3. Chen, et al., “A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation.” In: MICCAI, 2020.
4. Chen, et al., “A 4D Infant Brain Volumetric Atlas based on the UNC/UMN Baby Connectome Project (BCP) Cohort.” NeuroImage, 2022: 119097.
5. Avants et al., “The optimal template effect in hippocampus studies of diseased populations.” Neuroimage, 49 (3), 2010: 2457–2466.
6. Lin, et al., "Inference in generalized additive mixed modelsby using smoothing splines." Journal of the royal statistical society: Series b, 1999: 381-400.

Funding

1. NIH (No. MH116225)
2. NIH (No. MH109773)
3. NIH (No. MH117943)
4. NIH (No. MH123202)

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license

Copyright © 2022 Chen et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.