On the Potential of the Excluded Volume and Auto-Correlation as Neuromorphometric Descriptors

TL;DR

This paper explores how autocorrelation and excluded volume measurements can enhance the characterization and classification of neuronal cells by quantifying connectivity features.

Contribution

It introduces the use of autocorrelation and excluded volume as neuromorphometric descriptors for neuronal cell analysis and classification.

Findings

Autocorrelation effectively quantifies dendrite connectivity.

Excluded volume provides complementary connectivity information.

Approaches demonstrated on real neuronal cells.

Abstract

This work investigates at what degree two neuromorphometric measurements, namely the autocorrelation and the excluded volume of a neuronal cell can influence the characterization and classification of such a type of cells. While the autocorrelation function presents good potential for quantifying the dendrite-dendrite connectivity of cells in mosaic tilings, the excluded volume, i.e. the amount of the surround space which is geometrically not accessible to an axon or dendrite, provides a complementary characterization of the cell connectivity. The potential of such approaches is illustrated with respect to real neuronal cells.