A topological understanding of neuronal morphologies

Lida Kanari (Ecole Polytechnique Federale de Lausanne, Switzerland)

The morphological diversity of neurons supports the complex information-processing capabilities of biological neuronal networks. A major challenge in neuroscience has been to reliably describe neuronal shapes with universal morphometrics that generalize across cell types and species. Inspired by algebraic topology, we have developed a topological descriptor of trees that couples the topology of their complex arborization with their geometric structure, retaining more information than traditional morphometrics.
The topological morphology descriptor (TMD) has proved to be very powerful in separating neurons into well-defined groups on morphological grounds. The TMD algorithm was used for the identification of two distinct morphological classes of pyramidal cells in the human cortex that also have distinct functional roles, suggesting the existence of a direct link between the anatomy and the function of neurons. The TMD algorithm also led to the objective morphological classification of rodent cortical neurons and has proved to be essential for the computational generation of neuronal morphologies that reproduce the characteristic shapes of neurons in the brain.