Classification of dendritic spiking neurons’ shape through clustering and machine learning techniques

Abstract

A synapse is a specialized region between two adjacent neurons that allows information to be transmitted from one cell to another. The formation of these connections and transfer of signals via electrical impulses is a critical characteristic of neuronal cells. The synapse is formed by the axonal bouton on the axon side and the dendritic spine, a specialized outgrowth of the dendritic membrane, on the dendrite side. Dendritic spines exhibit diverse shapes and sizes, differing significantly across brain regions, cell categories, and animal species. Changes in dendritic spine morphology occur in neurodevelopmental and neurodegenerative ailments while also responding to external stimuli. Although deemed to facilitate synaptic plasticity, further investigation is essential for establishing the correlation between spine construction and its function. To address the issue in modern neurobiology of characterizing synapse morphology on 3D neuron images, the development of effective analytical methods is necessary. Our team has produced an open-source software solution for precise dendritic spine segmentation using 3D dendrite images. This software calculates the 10 most widely used 3D-adapted morphological features [1, 2] and enables classification and clustering of dendritic spine data sets to determine their shape. In addition to numerical features for describing the shape of a dendritic spine, researchers proposed using a histogram of chord lengths, known as the chord length distribution histogram (CLDH). This involves generating a set of random chords within the dendritic spine’s volume, connecting its outer boundaries and forming a histogram. By setting n=30 000, the probabilistic fluctuations of the histogram become insignificant. The derived metrics were then used for clustering and classifying the dataset. Classification based on predetermined morphological groups is a frequently used approach for analyzing the morphology of dendritic spines. This method involves categorizing spines into established groups such as thin, mushroom, and stubby. Experimenters generally perform classification in a semi-automated manner, leading to considerable error. We have created a spine categorization tool using a machine learning algorithm. The tool classifies spines based on a consensus reached by eight experts who manually labeled the training dataset. The accuracy of this method surpasses 77% when using classical morphological features and is comparable to expert labeling. The implementation of this approach reduces classification bias and complexity. Recent studies, including those using live microscopy in vitro and in vivo, indicate that dendritic spine shapes exhibit a continuum rather than distinct categories [3]. Therefore, it is essential to establish a dependable methodology for evaluating and examining the morphology of dendritic spines. We have created a clustering tool that establishes both the number of groups and their content based on data, rather than the experimenter’s discretion. This tool leverages the k-means and DBSCAN algorithms for its representation. Three clustering methods are presented to determine the number of clusters: the silhouette method, the elbow method, and a novel method, developed by the authors, based on the max class divergence criteria. The authors assume that cluster quality improves as clusters differ significantly in the number of mushroom/thin/stubby spine classes, as marked by experts. The benefit of this approach is that it considers the particular data type for clustering. Without this knowledge, assessing the quality of clustering is challenging. The use of the CLDH metric for clustering yielded a consistent and stable number of clusters (n=5) across all three methods described. These clusters contain dendritic spines that share similar shapes and have been validated by experts. In contrast, using classical metrics resulted in a variable cluster count ranging from n=4 to n=14. These findings suggest that the CLDH metric, with its complexity, provides enough information about synapse shape to enable precise clustering.