Diadem X: Automated 4 Dimensional Analysis of Morphological Data

He, Hai-yan; Cline, Hollis T.

doi:10.1007/s12021-011-9098-x

Cited by 16 publications

(16 citation statements)

References 24 publications

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“…The increased branch tip number we observed following STVE or dark could result from different cellular mechanisms, such as increased rate of branch additions or decreased rate of branch retractions, each associated with different mechanistic and functional implications (He and Cline, 2011). Likewise, decreased branch tip number could be due to decreased branch additions or increased branch retractions.…”

Section: Resultsmentioning

confidence: 99%

Experience-Dependent Bimodal Plasticity of Inhibitory Neurons in Early Development

Shen

Hiramoto

et al. 2016

Neuron

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Inhibitory neurons are heterogeneous in the mature brain. It is unclear when and how inhibitory neurons express distinct structural and functional profiles. Using in vivo time-lapse imaging of tectal neuron structure and visually-evoked Ca++ responses in tadpoles we found that inhibitory neurons cluster into two groups with opposite valence of plasticity after 4h of dark and visual stimulation. Half decreased dendritic arbor size and Ca++ responses after dark and increased them after visual stimulation, matching plasticity in excitatory neurons. Half increased dendrite arbor size and Ca++ responses following dark and decreased them after stimulation. At the circuit level, visually-evoked excitatory and inhibitory synaptic inputs were potentiated by visual experience and E/I remained constant. Our results indicate that developing inhibitory neurons fall into distinct functional groups with opposite experience-dependent plasticity and as such, are well positioned to foster experience-dependent synaptic plasticity and maintain circuit stability during labile periods of circuit development.

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Section: Resultsmentioning

confidence: 99%

Experience-Dependent Bimodal Plasticity of Inhibitory Neurons in Early Development

Shen

Hiramoto

et al. 2016

Neuron

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“…Figure 2f shows the final result of the RPCT algorithm and depicts the reconstructed tree. Figure 3a displays a single slice from a 3D image stack in which a cerebellar climbing fibre was stained (Sugihara 2011 Here, red dots represent residual principal subset samples. Traced branches are labeled with different colors and shapes (red is reserved for the principal subset samples).…”

Section: Branching Principal Subset Clustersmentioning

confidence: 99%

Principal Curves as Skeletons of Tubular Objects

Bas

Erdoğmuş

2011

Neuroinform

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Developments in image acquisition technology make high volumes of neuron images available to neuroscientists for analysis. However, manual processing of these images is not practical and is infeasible for larger and larger scale studies. Reliable interpretation and analysis of high volume data requires accurate quantitative measures. This requires analysis algorithms to use mathematical models that inherit the underlying geometry of biological structures in order to extract topological information. In this paper, we first introduce principal curves as a model for the underlying skeleton of axons and branches, then describe a recursive principal curve tracing (RPCT) method to extract this topology information from 3D microscopy imagery. RPCT first finds samples on the one dimensional principal set of the intensity function in space. Then, given an initial direction and location, the algorithm iteratively traces the principal curve in space using our principal curve tracing (PCT) method. Recursive implementation of PCT provides a compact solution for extracting complex tubular structures that exhibit bifurcations.

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“…Although technical obstacles remain (e.g., image alignment over long imaging intervals, identification of new structures), new advances in the field will make the 4D semi-auto reconstruction possible. 22 One of the DIA-DEM "products"-the DIADEM Metric, was designed to compare reconstructions of the same data sets, which with modification, can serve as a measurement for 4D reconstruction (see footnote 9).…”

Section: Future Directionsmentioning

confidence: 99%

The DIADEM and Beyond

Liu

2011

Neuroinform

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Diadem X: Automated 4 Dimensional Analysis of Morphological Data

Cited by 16 publications

References 24 publications

Experience-Dependent Bimodal Plasticity of Inhibitory Neurons in Early Development

Experience-Dependent Bimodal Plasticity of Inhibitory Neurons in Early Development

Principal Curves as Skeletons of Tubular Objects

The DIADEM and Beyond

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