@conference {70247, title = {LDDMEm: Large Deformation Diffeomorphic Metric Embedding}, booktitle = {Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2025}, year = {2026}, month = {2026 JAN 01}, abstract = {

We present a method, open-source software, and experiments which embed arbitrary deformation vector fields produced by any method (e.g., ANTs or VoxelMorph) in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. This decouples formal diffeomorphic shape analysis from image registration, which has many practical benefits. Shape analysis can be added to study designs without modification to already chosen image registration methods and existing databases of deformation fields can be reanalyzed within the LDDMM framework without repeating image registrations. Pairwise time series studies can be extended to full time series regression with minimal added computing. The diffeomorphic rigor of image registration methods can be compared by embedding deformation fields and comparing projection distances. Finally, the added value of formal diffeomorphic shape analysis can be more fairly evaluated when it is derived from and compared to a baseline set of deformation fields. In brief, the method is a straightforward use of geodesic shooting in diffeomorphisms with a deformation field as the target, rather than an image. This is simpler than the image registration case which leads to a faster implementation that requires fewer user derived parameters.

}, doi = {10.1007/978-3-032-04947-6_31}, url = {https://www.doi.org/10.1007/978-3-032-04947-6_31}, author = {Fleishman, Greg M. and Fletcher, P. Thomas} } @article {70285, title = {Whole-brain co-mapping of gene expression and neuronal activity at cellular resolution in behaving zebrafish}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 10}, abstract = {

The brain{\textquoteright}s capabilities rely on both the molecular properties of individual cells and their interactions across brain-wide networks. However, relating gene expression to activity in individual neurons across the entire brain remains elusive. Here we developed an experimental-computational platform, WARP, for whole-brain imaging of neuronal activity during behavior, expansion-assisted spatial transcriptomics, and cellular-level registration of these two modalities. Through joint analysis of whole-brain neuronal activity during multiple behaviors, cellular gene expression, and anatomy, we identified functions of molecularly defined populations {\textemdash} including luminance coding in a cckb-pou4f2 midbrain population and task-structured activity in pvalb7-eomesa hippocampal-like neurons {\textemdash} and defined over 2,000 other function-gene-anatomy subpopulations. Analysis of this unprecedented multimodal dataset also revealed that most gene-matched neurons showed stronger activity correlations, highlighting a brain-wide role for gene expression in functional organization. WARP establishes a foundational platform and open-access dataset for cross-experiment discovery, high-throughput function-to-gene mapping, unification of cell biology and systems neuroscience, and scalable circuit modeling at the whole-brain scale.

}, doi = {10.64898/2026.02.07.704095}, url = {https://www.biorxiv.org/content/early/2026/02/10/2026.02.07.704095}, author = {Marquez Legorreta, Emmanuel and Fleishman, Greg M. and Hesselink, Luuk W. and Eddison, Mark and Smeets, Kasper and Stringer, Carsen and Keller, Philipp J. and Narayan, Sujatha and Chen, Alex B. and Mensh, Brett D. and Sternson, Scott M. and Englitz, Bernhard and Tillberg, Paul W. and Ahrens, Misha B.} }