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Diadem X: automated 4 dimensional analysis of morphological data.

He HY, Cline HT - Neuroinformatics (2011)

View Article: PubMed Central - PubMed

Affiliation: The Scripps Research Institute, La Jolla, CA 92037, USA.

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The development of multi-photon imaging technique has greatly facilitated in vivo time-lapse imaging and enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... Although the mechanisms underlying this widely observed effect of TTX treatment are still unclear, this was the first demonstration that neuronal activity affected the growth and structure of individual neurons... Subsequently Antonini and Stryker conducted heroic experiments (Fig.  1) which demonstrated that the morphology of individual geniculocortical axons changes over periods of days in response to decreased visual experience. 7 Specifically, by comparing populations of neurons from animals treated with monocular deprivation, they found that geniculocortical axons carrying information in the open eye pathway elaborated more complex axon arbors than axons in the deprived-eye pathway. 8 These experiments were important because they demonstrated that sensory input activity governed the elaboration of neuronal axons and that the gross re-organization of ocular dominance columns in monocularly-deprived animals seen using radioactive tracers reported a population-level change in neuronal structure, rather than, for instance, a change in the distribution of axons within layer 4 of visual cortex... It is important to point out that these conclusions were generated by comparing populations of neurons from animals at different stages and treated with different visual stimulation or deprivation paradigms, so that specific information about cellular mechanisms governing elaboration or regression of axon arbor development could not be determined... Many studies have documented the invasion and development of axon arbors by comparing samples across different developmental timepoints. 9 In parallel, other studies demonstrated an increase followed by a gradual decrease in synapse density. 10 Together these studies suggested a model in which axon arbors go through a period of exuberant elaboration and excess synaptogenesis followed by an elimination phase, in which both synapses and axon branches were pruned... As described by Hua and Smith,11 this classical model of sequential axon arbor elaboration and pruning is not borne out by time-lapse in vivo imaging of developing retinotectal axons in Xenopus frog tadpoles and Zebrafish. 1213 Rather, branch addition and synaptogenesis are concurrent with branch retraction and synapse elimination for both axons and dendrites, as suggested by light microscopy time-lapse data14 and demonstrated more conclusively by combining in vivo time-lapse imaging with subsequent serial section electron microscopy. 15 Importantly, the final structure of the axon is indistinguishable (Fig.  2), and these fundamental differences in the cellular mechanisms, and therefore the molecular/genetic/signaling events underlying arbor development, would only be recognized by time-lapse in vivo imaging data... This serves as but one example of the essential need for in vivo time-lapse imaging data for accurate identification of mechanisms governing brain development, circuit plasticity and neurological diseases... The development of multi-photon imaging techniques has greatly facilitated in vivo time-lapse imaging, which enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... A second technical issue is the identification of persistent and new structures (branch tips, boutons, spines) in sequential images... Despite the fact that comparison of 3 dimensional data sets remains a significant challenge, recent progress in a number of labs suggests that semi-automated analysis of time-lapse images of neuronal structures is a tractable problem within the near future... Such automation will have a significant impact on the ability to assess developmental, plasticity-induced, regressive or therapeutic changes in nervous system structure and will follow smoothly from the advances seen as a result of the Diadem Challenge.

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Examples of large-scale rearrangements of retinotectal axon arbor structure observed by in vivo time-lapse images collected at daily intervals. From O’Rourke and Fraser, 1990
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Fig3: Examples of large-scale rearrangements of retinotectal axon arbor structure observed by in vivo time-lapse images collected at daily intervals. From O’Rourke and Fraser, 1990

Mentions: The magnitude of structural changes in time-lapse imaging data can determine the choice of analysis and type of data presentation. The first time-lapse in vivo imaging experiments of retinotectal axon arbor development collected images at daily intervals.13 Because the changes in the arbor structure were often large (Fig. 3), data were analyzed by quantifying cumulative increases in features of neuronal structure, such as total branch length or branch tip numbers, based on 2 dimensional drawings of reconstructed neurons.13 Studies in which images were collected at more frequent intervals allowed identification of specific dynamic events, including branch addition, extension, shortening or retraction or branch stabilization within axonal or dendritic arbors either from manual drawings of neurons,17 or maximal image projections and manual analysis of spine dynamics or using custom macros, for instance in MatLab.18 Although these studies do reveal structural dynamics associated with a variety of normal and pathological conditions, analysis of 2 dimensional data with manual scoring introduces a variety of errors.Fig. 3


Diadem X: automated 4 dimensional analysis of morphological data.

He HY, Cline HT - Neuroinformatics (2011)

Examples of large-scale rearrangements of retinotectal axon arbor structure observed by in vivo time-lapse images collected at daily intervals. From O’Rourke and Fraser, 1990
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3104138&req=5

Fig3: Examples of large-scale rearrangements of retinotectal axon arbor structure observed by in vivo time-lapse images collected at daily intervals. From O’Rourke and Fraser, 1990
Mentions: The magnitude of structural changes in time-lapse imaging data can determine the choice of analysis and type of data presentation. The first time-lapse in vivo imaging experiments of retinotectal axon arbor development collected images at daily intervals.13 Because the changes in the arbor structure were often large (Fig. 3), data were analyzed by quantifying cumulative increases in features of neuronal structure, such as total branch length or branch tip numbers, based on 2 dimensional drawings of reconstructed neurons.13 Studies in which images were collected at more frequent intervals allowed identification of specific dynamic events, including branch addition, extension, shortening or retraction or branch stabilization within axonal or dendritic arbors either from manual drawings of neurons,17 or maximal image projections and manual analysis of spine dynamics or using custom macros, for instance in MatLab.18 Although these studies do reveal structural dynamics associated with a variety of normal and pathological conditions, analysis of 2 dimensional data with manual scoring introduces a variety of errors.Fig. 3

View Article: PubMed Central - PubMed

Affiliation: The Scripps Research Institute, La Jolla, CA 92037, USA.

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

The development of multi-photon imaging technique has greatly facilitated in vivo time-lapse imaging and enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... Although the mechanisms underlying this widely observed effect of TTX treatment are still unclear, this was the first demonstration that neuronal activity affected the growth and structure of individual neurons... Subsequently Antonini and Stryker conducted heroic experiments (Fig.  1) which demonstrated that the morphology of individual geniculocortical axons changes over periods of days in response to decreased visual experience. 7 Specifically, by comparing populations of neurons from animals treated with monocular deprivation, they found that geniculocortical axons carrying information in the open eye pathway elaborated more complex axon arbors than axons in the deprived-eye pathway. 8 These experiments were important because they demonstrated that sensory input activity governed the elaboration of neuronal axons and that the gross re-organization of ocular dominance columns in monocularly-deprived animals seen using radioactive tracers reported a population-level change in neuronal structure, rather than, for instance, a change in the distribution of axons within layer 4 of visual cortex... It is important to point out that these conclusions were generated by comparing populations of neurons from animals at different stages and treated with different visual stimulation or deprivation paradigms, so that specific information about cellular mechanisms governing elaboration or regression of axon arbor development could not be determined... Many studies have documented the invasion and development of axon arbors by comparing samples across different developmental timepoints. 9 In parallel, other studies demonstrated an increase followed by a gradual decrease in synapse density. 10 Together these studies suggested a model in which axon arbors go through a period of exuberant elaboration and excess synaptogenesis followed by an elimination phase, in which both synapses and axon branches were pruned... As described by Hua and Smith,11 this classical model of sequential axon arbor elaboration and pruning is not borne out by time-lapse in vivo imaging of developing retinotectal axons in Xenopus frog tadpoles and Zebrafish. 1213 Rather, branch addition and synaptogenesis are concurrent with branch retraction and synapse elimination for both axons and dendrites, as suggested by light microscopy time-lapse data14 and demonstrated more conclusively by combining in vivo time-lapse imaging with subsequent serial section electron microscopy. 15 Importantly, the final structure of the axon is indistinguishable (Fig.  2), and these fundamental differences in the cellular mechanisms, and therefore the molecular/genetic/signaling events underlying arbor development, would only be recognized by time-lapse in vivo imaging data... This serves as but one example of the essential need for in vivo time-lapse imaging data for accurate identification of mechanisms governing brain development, circuit plasticity and neurological diseases... The development of multi-photon imaging techniques has greatly facilitated in vivo time-lapse imaging, which enables comparison of the fine morphological structures of individual neurons over time... Despite the fact that 4D data acquisition has become easier and can be applied to a variety of brain tissues, both in vivo and in tissue slices, the analysis of these 4D data remains extremely laborious and painstaking... A second technical issue is the identification of persistent and new structures (branch tips, boutons, spines) in sequential images... Despite the fact that comparison of 3 dimensional data sets remains a significant challenge, recent progress in a number of labs suggests that semi-automated analysis of time-lapse images of neuronal structures is a tractable problem within the near future... Such automation will have a significant impact on the ability to assess developmental, plasticity-induced, regressive or therapeutic changes in nervous system structure and will follow smoothly from the advances seen as a result of the Diadem Challenge.

Show MeSH