language-icon Old Web
English
Sign In

Sholl analysis

Sholl analysis is a method of quantitative analysis commonly used in neuronal studies to characterize the morphological characteristics of an imaged neuron, first used to describe the differences in the visual and motor cortices of cats. Sholl was interested in comparing the morphology of different types of neurons, such as the star-shaped stellate cells and the cone-shaped pyramidal cells, and of different locations in the dendritic field of the same type of neurons, such as basal and apical processes of the pyramidal neuron. He looked at dendritic length and diameter (Sholl, p. 389, Fig. 1) and also the number of cells per volume (Sholl, p. 401, The packing density of perikarya). Sholl analysis is a method of quantitative analysis commonly used in neuronal studies to characterize the morphological characteristics of an imaged neuron, first used to describe the differences in the visual and motor cortices of cats. Sholl was interested in comparing the morphology of different types of neurons, such as the star-shaped stellate cells and the cone-shaped pyramidal cells, and of different locations in the dendritic field of the same type of neurons, such as basal and apical processes of the pyramidal neuron. He looked at dendritic length and diameter (Sholl, p. 389, Fig. 1) and also the number of cells per volume (Sholl, p. 401, The packing density of perikarya). While methods for estimating the number of cells have vastly improved since 1953 with the advent of unbiased stereology, the method Sholl uses to record the number of intersections at various distances from the cell body is still in use and is actually named after Sholl. 'In order to study the way in which the number of branches varies with the distance from the perikaryon, it is convenient to use a series of concentric spherical shells as co-ordinates of reference. ...... these shells have their common centre in the perikaryon' (Sholl, p. 392, The manner of dendritic branching). What Sholl called the 'Concentric Shell Method' is now known as 'Sholl Analysis'. As well as the number of intersections per concentric shell, Sholl also calculated the mean diameter of the dendrites or axons within each concentric shell (Sholl, p. 396, table 2 and 3). Sholl appreciated that his method is good for comparing neurons, for instance in figure 8 the differences in the number of dendritic intersections correlated with distance from the cell body is compared between neurons from the motor and visual cortex. Sholl also realized his method is useful to determine where and how big is the region where synapses are possible, something he called the neuron's 'connective zone' (Sholl, p. 402, The connective zone of a neuron). In 1953, Sholl was working with projections of 3-D neurons into two-dimensions, but now Sholl analysis can be done on 3-D images (e.g. image stacks or 3-D montages) of neurons, making the concentric circles truly three-dimensional shells. In addition to intersections and diameter: total dendritic length, surface area, and volume of processes per shell; number of nodes, endings, varicosities and spines per shell; and branching order of the dendrites in each shell, can be included in the analysis. For modern examples of the use of Sholl analysis to analyze neurons, please see (O'Neill, et al., 2015) or (Chowhudry, et al., 2015). Curves produced from the 'number of intersections vs. distance from the cell body data' are usually of somewhat irregular shape, and much work has been done to determine appropriate means of analyzing the results. Common methods include Linear Analysis, Semi-log Analysis and Log-Log Analysis The Linear Method is the analysis of the function N(r), where N is the number of crossings for a circle of radius r. This direct analysis of the neuron count allows the easy computation of the critical value, the dendrite maximum, and the Schoenen Ramification Index.

[ "Branching (version control)", "Hippocampal formation", "Hippocampus", "Dendritic spine" ]
Parent Topic
Child Topic
    No Parent Topic