Manually tracing human amygdala across childhood and adolescence
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Description
The developmental pattern of the amygdala throughout childhood and adolescence has been inconsistently reported in previous neuroimaging studies. Given the relatively small size of the amygdala on full brain MRI scans, discrepancies may be partly due to methodological differences in amygdalar segmentation. To investigate the impact of volume extraction methods on amygdala volume, we compared FreeSurfer, FSL and volBrain segmentation measurements with those obtained by manual tracing. The manual tracing method, which we used as the’gold standard’, exhibited almost perfect intra- and inter-rater reliability. We observed systematic differences in amygdala volumes between automatic (FreeSurfer and volBrain) and manual methods. Specifically, compared with the manual tracing, FreeSurfer estimated larger amygdalae, and volBrain produced smaller amygdalae while FSL demonstrated a mixed pattern. The tracing bias was not uniform, but higher for smaller amygdalae. We further modeled amygdalar growth curves using accelerated longitudinal cohort data from the Chinese Color Nest Project. Trajectory modeling and statistical assessments of the manually traced amygdalae revealed linearly increasing and parallel developmental patterns for both girls and boys, although the amygdalae of boys were larger than those of girls. Compared to these trajectories, the shapes of developmental curves were similar when using the volBrain derived volumes. FreeSurfer derived trajectories had more nonlinearities and appeared flatter. FSL derived trajectories demonstrated an inverted U shape and were significantly different from those derived from manual tracing method. The use of amygdala volumes adjusted for total gray-matter volumes, but not intracranial volumes, resolved the shape discrepancies and led to reproducible growth curves between manual tracing and the automatic methods (except FSL). Our findings revealed steady growth of the human amygdala, mirroring its functional development across the school age. Methodological improvements are warranted for current automatic tools to achieve more accurate tracing of the amygdala at school age, calling for next generation tools.
Keywords:
- Amygdala
- Brain development
- Growth chart
- MRI
Subject:
- Psychology
Data File Download
To access the devCCNP Lite data, investigators must complete the application file Data Use Agreement on CCNP (DUA-CCNP) at https://cstr.cn/31253.11.sciencedb.o00133.00020 and have it reviewed and approved by the Chinese Color Nest Consortium (CCNC). More information about CCNP can be found at: http://deepneuro.bnu.edu.cn/?p=163 or https://github.com/zuoxinian/CCNP. Requests for further information and collaboration are encouraged and considered by the CCNC, and please read the Data Use Agreement and contact us via deepneuro@bnu.edu.cn.
Information
- xinian.zuo@bnu.edu.cn
- 7.02 MB
- 424
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Key-Area Research and Development Program of Guangdong Province (Grant No.2019B030335001)
The Start-up Funds for Leading Talents at Beijing Normal University, the National Basic Science Data Center "Chinese Data-sharing Warehouse for In-vivo Imaging Brain" (Grant No.NBSDC-DB-15)
The Beijing Municipal Science and Technology Commission (Grant No.Z161100002616023)
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- Version 1 published online2021-11-01 00:00:00 GMT+8
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This dataset supports or supplements the publication of the following papers
Q. Zhou, S. Liu, C. Jiang et al., Charting the human amygdala development across childhood and adolescence: Manual and automatic segmentation. Developmental Cognitive Neuroscience (2021), doi: https://doi.org/10.1016/j.dcn.2021.101028.
doi: 10.1016/j.dcn.2021.101028
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