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

Scorcioni, Ruggero; Polavaram, Sridevi; Ascoli, Giorgio A.

doi:10.1038/nprot.2008.51

Cited by 330 publications

(311 citation statements)

References 52 publications

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“…Using the obex as a landmark, a micropipette (tip diameter Ͻ40 m) was inserted at 50°from the vertical to a depth determined from a weight-depth curve (Sherrard et al, 1986). The anterograde tracer, lysine fixable dextran-fluorescein solution (4% in distilled water; Fluoroemerald, D-1820 10,000 molecular weight; Invitrogen), was injected into the right inferior olive.…”

Section: Methodsmentioning

confidence: 99%

“…Order 1 corresponds to terminal branches, and the order number increases toward the soma. Quantitative morphological parameters of the whole neuron were extracted using Lmeasure software (Scorcioni et al, 2008) (http://cng.gmu.edu:8080/ Lm/). Sholl analysis was performed with the plugin Simple Neurite Tracer in Fiji program (http://pacific.mpi-cbg.de).…”

Section: Methodsmentioning

confidence: 99%

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Mature Purkinje Cells Require the Retinoic Acid-Related Orphan Receptor-α (RORα) to Maintain Climbing Fiber Mono-Innervation and Other Adult Characteristics

et al. 2013

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Neuronal maturation during development is a multistep process regulated by transcription factors. The transcription factor ROR␣ (retinoic acid-related orphan receptor ␣) is necessary for early Purkinje cell (PC) maturation but is also expressed throughout adulthood. To identify the role of ROR␣ in mature PCs, we used Cre-lox mouse genetic tools in vivo that delete it specifically from PCs between postnatal days 10 -21. Up to 14 d of age, differences between mutant and control PCs were not detectable: both were mono-innervated by climbing fibers (CFs) extending along their well-developed dendrites with spiny branchlets. By week 4, mutant mice were ataxic, some PCs had died, and remaining PC soma and dendrites were atrophic, with almost complete disappearance of spiny branchlets. The innervation pattern of surviving ROR␣-deleted PCs was abnormal with several immature characteristics. Notably, multiple functional CF innervation was reestablished on these mature PCs, simultaneously with the relocation of CF contacts to the PC soma and their stem dendrite. This morphological modification of CF contacts could be induced even later, using lentivirus-mediated depletion of rora from adult PCs. These data show that the late postnatal expression of ROR␣ cell-autonomously regulates the maintenance of PC dendritic complexity, and the CF innervation status of the PC (dendritic vs somatic contacts, and mono-innervation vs multi-innervation). Thus, the differentiation state of adult neurons is under the control of transcription factors; and in their absence, adult neurons lose their mature characteristics and acquire some characteristics of an earlier developmental stage.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mature Purkinje Cells Require the Retinoic Acid-Related Orphan Receptor-α (RORα) to Maintain Climbing Fiber Mono-Innervation and Other Adult Characteristics

et al. 2013

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“…The most extensive tool in this category is L-Measure (103), which can compute over 100 independent morphological parameters (regarding the soma and the tree), from populations of cells, to individual neurons, to portions thereof. This enables detailed comparative analyses on large numbers of neurons, the discovery of characteristic morphological features across cell classes, the detection of differences induced by specific growth factors, the analysis of developmental changes, the extraction of parameter distributions required for computational simulations to generate virtual neurons, and the assessment of the quality and limitations of these models by comparing their emergent properties with the original experimental data (103). Another tool is XL_Calculations (111), which was designed to distinguish between neurons at different stages of differentiation, and to this end facilitates batch processing of large numbers of NeuronJ tracing files and the automated computation of over 45 different quantitative tree measures.…”

Section: Quantification Toolsmentioning

confidence: 99%

Neuron tracing in perspective

2010

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The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. ' 2010 International Society for Advancement of Cytometry

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“…These parameters were calculated from digital morphological files using L-Measure (http://krasnow. gmu.edu/L-Neuron), a free software program for quantitative morphometric analysis of neuronal reconstructions (Scorcioni and Ascoli 2001;Scorcioni et al 2008). In particular, the dendritic surface was divided into bins based on the path distance from the soma.…”

Section: Computation Of Synaptic Efficacy (Se)mentioning

confidence: 99%

Dendritic Excitability and Neuronal Morphology as Determinants of Synaptic Efficacy

Komendantov

Ascoli²

2009

Journal of Neurophysiology

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Komendantov AO, Ascoli GA. Dendritic excitability and neuronal morphology as determinants of synaptic efficacy. J Neurophysiol 101: 1847-1866, 2009. First published January 28, 2009 doi:10.1152/jn.01235.2007. The ability to trigger neuronal spiking activity is one of the most important functional characteristics of synaptic inputs and can be quantified as a measure of synaptic efficacy (SE). Using model neurons with both highly simplified and real morphological structures (from a single cylindrical dendrite to a hippocampal granule cell, CA1 pyramidal cell, spinal motoneuron, and retinal ganglion neurons) we found that SE of excitatory inputs decreases with the distance from the soma and active nonlinear properties of the dendrites can counterbalance this global effect of attenuation. This phenomenon is frequency dependent, with a more prominent gain in SE observed at lower levels of background inputoutput neuronal activity. In contrast, there are no significant differences in SE between passive and active dendrites under higher frequencies of background activity. The influence of the nonuniform distribution of active properties on SE is also more prominent at lower background frequencies. In models with real morphologies, the effect of active dendritic conductances becomes more dramatic and inverts the SE relationship between distal and proximal locations. In active dendrites, distal synapses have higher efficacy than that of proximal ones because of arising dendritic spiking in thin branches with high-input resistance. Lower levels of dendritic excitability can make SE independent of the distance from the soma. Although increasing dendritic excitability may boost SE of distal synapses in real neurons, it may actually reduce overall SE. The results are robust with respect to morphological variation and biophysical properties of the model neurons. The model of CA1 pyramidal cell with realistic distributions of dendritic conductances demonstrated important roles of hyperpolarization-activated (h-) current and A-type K ϩ current in controlling the efficacy of single synaptic inputs and overall SE differently in basal and apical dendrites. I N T R O D U C T I O NDendrites play a central role in neuronal information processing. Electrical signals from other neurons are transmitted onto dendrites via synaptic inputs, which are located throughout the dendritic tree. The most important functional characteristic of synaptic inputs is their ability to influence neuronal spiking activity. At a strong excitatory synapse, a single action potential (AP) in the presynaptic cell might trigger an AP in the postsynaptic neuron, whereas at a weak excitatory synapse only a subthreshold excitatory postsynaptic potential (EPSP) would be initiated under the same stimulating conditions. In real neuronal networks an individual neuron receives many synaptic inputs from numerous other neurons. Under the appropriate circumstance of background activity, even an individual weak synaptic input can be the determining factor for AP initiation...

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

Cited by 330 publications

References 52 publications

Mature Purkinje Cells Require the Retinoic Acid-Related Orphan Receptor-α (RORα) to Maintain Climbing Fiber Mono-Innervation and Other Adult Characteristics

Mature Purkinje Cells Require the Retinoic Acid-Related Orphan Receptor-α (RORα) to Maintain Climbing Fiber Mono-Innervation and Other Adult Characteristics

Neuron tracing in perspective

Dendritic Excitability and Neuronal Morphology as Determinants of Synaptic Efficacy

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