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Peer-reviewedCC BY 4.0Imaging Neuroscience

A curvilinear coordinate flatmap for visualizing hippocampal structure and development

  • Ashwin Anirudha Bhandiwad1
  • Fae Kronman2
  • Josephine Liwang2
  • Pan Gao3
  • Cyrille Rossant4,5
  • Daniel Birman5,6
  • Luis Puelles7
  • Song-Lin Ding1
  • Xiangmin Xu3
  • Lydia Ng1
  • Yongsoo Kim2,8
  • Tyler Mollenkopf1
8 affiliations
  1. 1Allen Institute for Brain Science, Seattle, WA, United States
  2. 2Department of Neuroscience and Experimental Therapeutics, College of Medicine, The Pennsylvania State University, Hershey, PA, United States
  3. 3Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, United States
  4. 4University College London, London, United Kingdom
  5. 5International Brain Laboratory, University College London, London, United Kingdom
  6. 6Allen Institute for Neural Dynamics, Seattle, WA, United States
  7. 7University of Murcia, Murcia, Spain
  8. 8Center for Neural Engineering, The Pennsylvania State University, University Park, United States
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Abstract

The hippocampal formation is a highly curved and topographically complex forebrain structure. This complex geometry presents persistent challenges for analyzing subregional, laminar, and connectivity patterns. Here, we present a computational workflow that generates curvilinear-coordinate flatmaps from mouse Common Coordinate Framework (CCF) registered hippocampal and retrohippocampal regions by solving the Laplacian equation to derive geodesic streamlines. This transformation unfolds the hippocampus into a planar slab, bounded by the meningeal and ventricular surfaces, with the depth defined along the radial axis. We apply this transform to mouse CCF-registered image volumes, single neuron reconstructions, and point cloud data, including spatial transcriptomic and rabies tracing datasets, revealing topographic variations in the dorsoventral and radial axes that are obscured in the CCF coordinate space. As proof of principle, we use flatmaps to show connectivity loss in a mouse model of Alzheimer’s disease and track postnatal development of microglial distribution in the hippocampus. This work provides an efficient and accessible resource for visualizing hippocampal organization across development and disease, offering new opportunities to interrogate the structure and function of this important brain region.

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Cite

BibTeXRIS
BibTeX
@article{bhandiwad2026curvilinear,
  title = {{A curvilinear coordinate flatmap for visualizing hippocampal structure and development}},
  author = {Ashwin Anirudha Bhandiwad and Fae Kronman and Josephine Liwang and Pan Gao and Cyrille Rossant and Daniel Birman and Luis Puelles and Song-Lin Ding and Xiangmin Xu and Lydia Ng and Yongsoo Kim and Tyler Mollenkopf},
  journal = {Imaging Neuroscience},
  year = {2026},
  month = nov,
  doi = {10.1162/imag.a.1429},
  url = {https://doi.org/10.1162/imag.a.1429},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A curvilinear coordinate flatmap for visualizing hippocampal structure and development
AU  - Ashwin Anirudha Bhandiwad
AU  - Fae Kronman
AU  - Josephine Liwang
AU  - Pan Gao
AU  - Cyrille Rossant
AU  - Daniel Birman
AU  - Luis Puelles
AU  - Song-Lin Ding
AU  - Xiangmin Xu
AU  - Lydia Ng
AU  - Yongsoo Kim
AU  - Tyler Mollenkopf
PY  - 2026
DA  - 2026/11/04
JO  - Imaging Neuroscience
DO  - 10.1162/imag.a.1429
UR  - https://doi.org/10.1162/imag.a.1429
AB  - The hippocampal formation is a highly curved and topographically complex forebrain structure. This complex geometry presents persistent challenges for analyzing subregional, laminar, and connectivity patterns. Here, we present a computational workflow that generates curvilinear-coordinate flatmaps from mouse Common Coordinate Framework (CCF) registered hippocampal and retrohippocampal regions by solving the Laplacian equation to derive geodesic streamlines. This transformation unfolds the hippocampus into a planar slab, bounded by the meningeal and ventricular surfaces, with the depth defined along the radial axis. We apply this transform to mouse CCF-registered image volumes, single neuron reconstructions, and point cloud data, including spatial transcriptomic and rabies tracing datasets, revealing topographic variations in the dorsoventral and radial axes that are obscured in the CCF coordinate space. As proof of principle, we use flatmaps to show connectivity loss in a mouse model of Alzheimer’s disease and track postnatal development of microglial distribution in the hippocampus. This work provides an efficient and accessible resource for visualizing hippocampal organization across development and disease, offering new opportunities to interrogate the structure and function of this important brain region.
SN  - 2837-6056
ER  -