Dynamo: An open-source Python application for quantifying neuronal structural plasticity over time
Year: 2026
Type: preprint
Abstract: SUMMARY Neurons exhibit tremendous structural plasticity during the growth of dendritic and axonal arbors in early brain circuit formation, followed by experience-driven structural plasticity throughout life. Advances in labeling and in vivo time-lapse imaging allow capture of dynamic structural changes within intact and awake animals, and fluorescent biosensors of neural activity offer opportunities to link structural and functional plasticity. However, the resulting large multi-dimensional data sets are challenging to quantify due to time-consuming tracking of minute structural changes throughout complex neuronal morphologies across time. Here, we present Dynamo: an open-source Python application that enables dynamic morphometries , the quantitative analysis of morphological changes over time, by streamlining arbor reconstruction, registration of structures across time, and quantitative analyses of growth behavior. Dynamo yields rich characterization of neural structural changes necessary for determining how rapid growth events culminate into long-term patterning, linking structural and functional plasticity, and for identifying underlying molecular mechanisms. HIGHLIGHTS Dynamo tracks individual neuronal structures across 4D time-lapse imaging data A Tree-Branch-Point hierarchy preserves element identity as arbors remodel Machine learning and manual tools enable fast, accurate 3D/4D reconstruction Dynamo links structural dynamics to molecular and neural activity signals eTOC BLURB Hogg, Coleman, et al. present Dynamo, an open-source Python platform that represents neuronal arbors as identity-preserving Tree-Branch-Point objects across time-lapse imaging series. Combining machine learning-based tracing with manual reconstruction and registration tools, Dynamo enables direct, programmable quantification of dendritic and spine structural plasticity linked to molecular and activity signals.
Institution: University of British Columbia
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7213283407
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