Automated extraction and classification of dynamic metrical features of morphological development in dissociated Purkinje neurons

The dendrite arbor of Purkinje cells is altered following to tail regeneration in the leopard gecko

Purkinje cells of the cerebellum have a complex arborized arrangement of dendrites and are amongst the most distinctive cell types of the nervous system. Although the neuromorphology of Purkinje cells has been well described for some mammals and teleost fish, for most vertebrates less is known. Here we used a modified Golgi-Cox method to investigate the neuromorphology of Purkinje cells from the lizard Eublepharis macularius, the leopard gecko. Using Sholl and Branch Structure Analyses, we sought to investigate whether the neuromorphology of gecko Purkinje cells was altered is response to tail loss and regeneration. Tail loss is an evolved mechanism commonly used by geckos to escape predation. Loss of the tail represents a significant and sudden change in body length and mass, which is only partially recovered as the tail is regenerated. We predicted that tail loss and regeneration would induce a quantifiable change in Purkinje cell dendrite arborization. Post hoc comparisons of Sholl analyses data showed that geckos with regenerated tails have significant changes in dendrite diameter and the number of dendrite intersections in regions corresponding to the position of parallel fiber synapses. We propose that the neuromorphological alterations observed in gecko Purkinje cells represent a compensatory response to tail regrowth, and perhaps a role in motor learning.

New morphological features based on the Sholl analysis for automatic classification of traced neurons

BACKGROUND

This article addresses the automatic classification of reconstructed neurons through their morphological features. The purpose was to extend the capabilities of the L-Measure software.

METHODS

New morphological features were developed, based on modifications of the conventional Sholl analysis. The lengths of the compartments, as well as their volumes, were added to the features used in the classical analysis in order to improve the results during automatic neuron classification. FSM were used to obtain subsets of lower cardinality from the full feature sets and the usefulness of these subsets was tested through their use in supervised classification tasks. The study was based on two types of neurons belonging to mice: pyramidal and GABAergic interneurons. Furthermore, a set of pyramidal neurons belonging to Later 4 and Layer 5 was analyzed.

RESULTS

RF classifier shown the best performance combined with a Wrapper method.U-WNAD set allowed to obtain higher values than WN, A and D in all cases and better results than LM for the filters and wrappers FSM. U-LM-WNAD set, led to the highest AUC values for all the FSM studied. Similar results for different regions of cortex were obtained. Comparison with Existing Methods The new features exhibited high discriminatory power with which the values of AUC and Acc obtained in the experiments exceeded those obtained using only the features provided by L-Measure.

CONCLUSIONS

The highest values of AUC and Acc were obtained from the sets U-WNAD and U-LM-WNAD, evidencing the discriminatory power of the new proposed features.

A primal analysis system of brain neurons data

It is a very challenging work to classify the 86 billions of neurons in the human brain. The most important step is to get the features of these neurons. In this paper, we present a primal system to analyze and extract features from brain neurons. First, we make analysis on the original data of neurons in which one neuron contains six parameters: room type, X, Y, Z coordinate range, total number of leaf nodes, and fuzzy volume of neurons. Then, we extract three important geometry features including rooms type, number of leaf nodes, and fuzzy volume. As application, we employ the feature database to fit the basic procedure of neuron growth. The result shows that the proposed system is effective.