(found 3 matches in 0.001135s)
Barcodes Distinguish Morphology of Neuronal Tauopathy
David Beers, Despoina Goniotaki, Diane P. Hanger, Alain Goriely, Heather A. Harrington
The geometry of neurons is known to be important for their functions. Hence, neurons are often classified by their morphology. Two recent methods, persistent homology and the topological morphology descriptor, assign a morphology descriptor called a barcode to a neuron equipped with a given function, such as the Euclidean distance from the root of the neuron. These barcodes can be converted into matrices called persistence images, which can then be averaged across groups. We show that when the defining function is the path length from the root, both the topological morphology descriptor and persistent homology are equivalent. We further show that persistence images arising from the path length procedure provide an interpretable summary of neuronal morphology. We introduce \topological morphology functions\, a class of functions similar to Sholl functions, that can be recovered from the associated topological morphology descriptor. To demonstrate this topological approach, we compare healthy cortical and hippocampal mouse neurons to those affected by progressive tauopathy. We find a significant difference in the morphology of healthy neurons and those with a tauopathy at a postsymptomatic age. We use persistence images to conclude that the diseased group tends to have neurons with shorter branches as well as fewer branches far from the soma.
A Topological Representation of Branching Neuronal Morphologies
Lida Kanari, Pawe\\textbackslash\l D\\textbackslash\lotko, Martina Scolamiero, Ran Levi, Julian Shillcock, Kathryn Hess, Henry Markram
Using Persistent Homology to Reveal Hidden Information in Neural Data
Gard Spreemann, Benjamin Dunn, Magnus Bakke Botnan, Nils A. Baas
We propose a method, based on persistent homology, to uncover topological properties of a priori unknown covariates of neuron activity. Our input data consist of spike train measurements of a set of neurons of interest, a candidate list of the known stimuli that govern neuron activity, and the corresponding state of the animal throughout the experiment performed. Using a generalized linear model for neuron activity and simple assumptions on the effects of the external stimuli, we infer away any contribution to the observed spike trains by the candidate stimuli. Persistent homology then reveals useful information about any further, unknown, covariates.