TY - JOUR
T1 - The ANTsX ecosystem for mapping the mouse brain
AU - Tustison, Nicholas J.
AU - Chen, Min
AU - Kronman, Fae N.
AU - Duda, Jeffrey T.
AU - Gamlin, Clare
AU - Tustison, Mia G.
AU - Kunst, Michael
AU - Dalley, Rachel
AU - Sorenson, Staci
AU - Wang, Quanxin
AU - Ng, Lydia
AU - Kim, Yongsoo
AU - Gee, James C.
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/12
Y1 - 2025/12
N2 - Large-scale efforts by the BRAIN Initiative Cell Census Network (BICCN) are generating a comprehensive reference atlas of cell types in the mouse brain. A key challenge in this effort is mapping diverse datasets, acquired with varied imaging, tissue processing, and profiling methods, into shared coordinate frameworks. Here, we present mouse brain mapping pipelines developed using the Advanced Normalization Tools Ecosystem (ANTsX) to align MERFISH spatial transcriptomics and high-resolution fMOST morphology data to the Allen Common Coordinate Framework (CCFv3), and developmental MRI and LSFM data to the Developmental CCF (DevCCF). Simultaneously, we introduce two novel methods: 1) a velocity field–based approach for continuous interpolation across developmental timepoints, and 2) a deep learning framework for automated brain parcellation using minimally annotated and publicly available data. All workflows are open-source and reproducible. We also provide general guidance for selecting appropriate strategies across modalities, enabling researchers to adapt these tools to new data.
AB - Large-scale efforts by the BRAIN Initiative Cell Census Network (BICCN) are generating a comprehensive reference atlas of cell types in the mouse brain. A key challenge in this effort is mapping diverse datasets, acquired with varied imaging, tissue processing, and profiling methods, into shared coordinate frameworks. Here, we present mouse brain mapping pipelines developed using the Advanced Normalization Tools Ecosystem (ANTsX) to align MERFISH spatial transcriptomics and high-resolution fMOST morphology data to the Allen Common Coordinate Framework (CCFv3), and developmental MRI and LSFM data to the Developmental CCF (DevCCF). Simultaneously, we introduce two novel methods: 1) a velocity field–based approach for continuous interpolation across developmental timepoints, and 2) a deep learning framework for automated brain parcellation using minimally annotated and publicly available data. All workflows are open-source and reproducible. We also provide general guidance for selecting appropriate strategies across modalities, enabling researchers to adapt these tools to new data.
UR - https://www.scopus.com/pages/publications/105026298316
UR - https://www.scopus.com/pages/publications/105026298316#tab=citedBy
U2 - 10.1038/s41467-025-66741-5
DO - 10.1038/s41467-025-66741-5
M3 - Article
C2 - 41274934
AN - SCOPUS:105026298316
SN - 2041-1723
VL - 16
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 11548
ER -