Algorithmic reconstruction of complete axonal arborizations in rat hippocampal neurons

Scorcioni, Ruggero; Ascoli, Giorgio A.

doi:10.1016/j.neucom.2004.10.105

Cited by 20 publications

(23 citation statements)

References 14 publications

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“…This cell was reconstructed from serial sections originally traced in (Tamamaki and Nojyo, 1991), using an automated procedure developed in (Scorcioni and Ascoli, 2005). The neuron morphology was imported by NEURON Import 3D tool and saved as template.…”

Section: Methodsmentioning

confidence: 99%

Shortest Loops are Pacemakers in Random Networks of Electrically Coupled Axons

Vladimirov¹,

Tu²,

Traub³

2012

Front. Comput. Neurosci.

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High-frequency oscillations (HFOs) are an important part of brain activity in health and disease. However, their origins remain obscure and controversial. One possible mechanism depends on the presence of sparsely distributed gap junctions that electrically couple the axons of principal cells. A plexus of electrically coupled axons is modeled as a random network with bi-directional connections between its nodes. Under certain conditions the network can demonstrate one of two types of oscillatory activity. Type I oscillations (100–200 Hz) are predicted to be caused by spontaneously spiking axons in a network with strong (high conductance) gap junctions. Type II oscillations (200–300 Hz) require no spontaneous spiking and relatively weak (low-conductance) gap junctions, across which spike propagation failures occur. The type II oscillations are reentrant and self-sustained. Here we examine what determines the frequency of type II oscillations. Using simulations we show that the distribution of loop lengths is the key factor for determining frequency in type II network oscillations. We first analyze spike failure between two electrically coupled cells using a model of anatomically reconstructed CA1 pyramidal neuron. Then network oscillations are studied by a cellular automaton model with random network connectivity, in which we control loop statistics. We show that oscillation periods can be predicted from the network’s loop statistics. The shortest loop, around which a spike can travel, is the most likely pacemaker candidate. The principle of one loop as a pacemaker is remarkable, because random networks contain a large number of loops juxtaposed and superimposed, and their number rapidly grows with network size. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We finally propose that type I oscillations may correspond to ripples, while type II oscillations correspond to so-called fast ripples.

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Section: Methodsmentioning

confidence: 99%

Shortest Loops are Pacemakers in Random Networks of Electrically Coupled Axons

Vladimirov¹,

Tu²,

Traub³

2012

Front. Comput. Neurosci.

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“…By the same token, LM has served validation purposes in processing the results of automated reconstruction algorithms applied to projection axons in the rat hippocampus neurons (i.e. comparing number of bifurcations, total length, path distance, branch order, and asymmetry with the same metrics extracted from other available morphologies of the same classes 22 ).…”

Section: Box 1 Quantitative Morphometry By L-measurementioning

confidence: 99%

L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies

2008

Self Cite

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L-Measure (LM) is a freely available software tool for the quantitative characterization of neuronal morphology. LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions. This report illustrates several LM protocols: (a) extraction of basic morphological parameters, (b) computation of frequency distributions, (c) measurements from user-specified sub regions of the neuronal arbors, (d) statistical comparison between two groups of cells, and (e) filtered selections and searches from collections of neurons based on any boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface, and typically execute within few minutes on a set of ~20 neurons. The tool is available at http:// krasnow.gmu.edu/cn3 for either online use on any Java-enabled browser and platform, or download for local execution under Windows and Linux.

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“…Boxed Inset: Zoomed-in (2×) individual basal tree with main path (red) ( C L = 0.24; C D = 0.43). B. Hippocampal CA1 pyramidal cell dendritic (basal: dark blue; apical: gray) and axonal (light blue) arbors [76,77,78]. Axonal main path (thickened red) would not be obvious without color, even though this axonal tree is highly caulescent ( C L = 0.89; C D = 0.89).…”

Section: Figuresmentioning

confidence: 99%

Quantifying neuronal size: Summing up trees and splitting the branch difference

Brown

Gillette

Ascoli

2008

Seminars in Cell & Developmental Biology

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Neurons vary greatly in size, shape, and complexity depending on their underlying function. Overall size of neuronal trees affects connectivity, area of influence, and other biophysical properties. Relative distributions of neuronal extent, such as the difference between subtrees at branch points, are also critically related to function and activity. This review covers neuromorphological research that analyzes shape and size to elucidate their functional role for different neuron types. We also introduce a novel morphometric, "caulescence", capturing the extent to which trees exhibit a main path. Neuronal tree types differ vastly in caulescence, suggesting potential neurocomputational correlates of this property.

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Algorithmic reconstruction of complete axonal arborizations in rat hippocampal neurons

Cited by 20 publications

References 14 publications

Shortest Loops are Pacemakers in Random Networks of Electrically Coupled Axons

Shortest Loops are Pacemakers in Random Networks of Electrically Coupled Axons

L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies

Quantifying neuronal size: Summing up trees and splitting the branch difference

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