Abstract
The common approach in morphological analysis of dendritic spines is to categorize spines into subpopulations based on whether they are stubby, mushroom, thin, or filopodia. Corresponding cellular models of synaptic plasticity, long-term potentiation, and long-term depression associate synaptic strength with either spine enlargement or spine shrinkage. Although a variety of automatic spine segmentation and feature extraction methods were developed recently, no approaches allowing for an automatic and unbiased distinction between dendritic spine subpopulations and detailed computational models of spine behavior exist.
We propose an automatic and statistically based method for the unsupervised construction of spine shape taxonomy based on arbitrary features. The taxonomy is then utilized in the newly introduced computational model of behavior, which relies on transitions between shapes. Models of different populations are compared using supplied bootstrap-based statistical tests.
We compared two populations of spines at two time points. The first population was stimulated with long-term potentiation, and the other in the resting state was used as a control. The comparison of shape transition characteristics allowed us to identify differences between population behaviors. Although some extreme changes were observed in the stimulated population, statistically significant differences were found only when whole models were compared. Therefore, we hypothesize that the learning process is related to the subtle changes in the whole ensemble of different dendritic spine structures, but not at the level of single shape classes.
The source code of our software is freely available for non-commercial use1.
Footnotes
E-mail: d.plewczynski{at}cent.uw.edu.pl
Contact: d.plewczynski{at}cent.uw.edu.pl.
the date of receipt and acceptance should be inserted later
↵7 We expect that there is a higher percentage of growing spines from ACTIVE than from CONTROL. Therefore, we decide to define the threshold point between growing and not-growing groups to be the threshold maximizing the difference between number of growing spines from ACTIVE and the number of growing spines from CONTROL. What we observed for shrinking spines is similar, therefore we similarly seek the threshold point between shrinking and not-shrinking groups.