Preserving Derivative Information while Transforming Neuronal Curves

TL;DR

This paper introduces a new method to accurately map neuron traces in brain atlases by preserving derivative information during transformations.

Contribution

The novel framework uses jet theory to preserve derivatives of neuron traces during transformations, improving mapping accuracy.

Findings

The method improves mapping accuracy in simulated and real neuron traces under random diffeomorphisms.

A Python package called brainlit implements the framework and is freely available for use.

Abstract

The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit.

The online version contains supplementary material available at 10.1007/s12021-023-09648-0.

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