Preserving Derivative Information while Transforming Neuronal Curves.
Thomas L AtheyDaniel TwardUlrich MuellerLaurent YounesJoshua T VogelsteinMichael I MillerPublished in: Research square (2023)
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, though zeroth order mapping is generally adequate in our real data setting. Our method is freely available in our open-source Python package brainlit.
Keyphrases
- high resolution
- white matter
- spinal cord
- high density
- cerebral ischemia
- resting state
- single cell
- healthcare
- health information
- cell therapy
- stem cells
- working memory
- mass spectrometry
- electronic health record
- subarachnoid hemorrhage
- transcranial magnetic stimulation
- blood brain barrier
- single molecule
- machine learning