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Items: 1 to 20 of 69

1.

Statistical analysis and data mining of digital reconstructions of dendritic morphologies.

Polavaram S, Gillette TA, Parekh R, Ascoli GA.

Front Neuroanat. 2014 Dec 4;8:138. doi: 10.3389/fnana.2014.00138. eCollection 2014.

2.

NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF.

Halavi M, Polavaram S, Donohue DE, Hamilton G, Hoyt J, Smith KP, Ascoli GA.

Neuroinformatics. 2008 Sep;6(3):241-52. doi: 10.1007/s12021-008-9030-1. Epub 2008 Oct 24.

3.

An ontology-based search engine for digital reconstructions of neuronal morphology.

Polavaram S, Ascoli GA.

Brain Inform. 2017 Jun;4(2):123-134. doi: 10.1007/s40708-017-0062-x. Epub 2017 Mar 23.

4.

The importance of metadata to assess information content in digital reconstructions of neuronal morphology.

Parekh R, Armañanzas R, Ascoli GA.

Cell Tissue Res. 2015 Apr;360(1):121-7. doi: 10.1007/s00441-014-2103-6. Epub 2015 Feb 5.

5.

Quantitative investigations of axonal and dendritic arbors: development, structure, function, and pathology.

Parekh R, Ascoli GA.

Neuroscientist. 2015 Jun;21(3):241-54. doi: 10.1177/1073858414540216. Epub 2014 Jun 27. Review.

6.

Digital reconstructions of neuronal morphology: three decades of research trends.

Halavi M, Hamilton KA, Parekh R, Ascoli GA.

Front Neurosci. 2012 Apr 23;6:49. doi: 10.3389/fnins.2012.00049. eCollection 2012.

7.

Generation, description and storage of dendritic morphology data.

Ascoli GA, Krichmar JL, Nasuto SJ, Senft SL.

Philos Trans R Soc Lond B Biol Sci. 2001 Aug 29;356(1412):1131-45. Review.

8.

Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure.

Lu Y, Carin L, Coifman R, Shain W, Roysam B.

Neuroinformatics. 2015 Jan;13(1):47-63. doi: 10.1007/s12021-014-9237-2.

PMID:
25086878
9.

A software tool for the analysis of neuronal morphology data.

Ledderose J, Sención L, Salgado H, Arias-Carrión O, Treviño M.

Int Arch Med. 2014 Feb 17;7(1):6. doi: 10.1186/1755-7682-7-6.

10.

A comparative computer simulation of dendritic morphology.

Donohue DE, Ascoli GA.

PLoS Comput Biol. 2008 Jun 6;4(5):e1000089. doi: 10.1371/journal.pcbi.1000089.

11.

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

Scorcioni R, Polavaram S, Ascoli GA.

Nat Protoc. 2008;3(5):866-76. doi: 10.1038/nprot.2008.51.

12.

Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography.

Wright SN, Kochunov P, Mut F, Bergamino M, Brown KM, Mazziotta JC, Toga AW, Cebral JR, Ascoli GA.

Neuroimage. 2013 Nov 15;82:170-81. doi: 10.1016/j.neuroimage.2013.05.089. Epub 2013 May 28.

13.

The DIADEM metric: comparing multiple reconstructions of the same neuron.

Gillette TA, Brown KM, Ascoli GA.

Neuroinformatics. 2011 Sep;9(2-3):233-45. doi: 10.1007/s12021-011-9117-y.

14.

Functionally relevant measures of spatial complexity in neuronal dendritic arbors.

Rothnie P, Kabaso D, Hof PR, Henry BI, Wearne SL.

J Theor Biol. 2006 Feb 7;238(3):505-26. Epub 2005 Aug 3.

PMID:
16083911
15.

An open repository for single-cell reconstructions of the brain forest.

Akram MA, Nanda S, Maraver P, Armañanzas R, Ascoli GA.

Sci Data. 2018 Feb 27;5:180006. doi: 10.1038/sdata.2018.6.

16.

The Flatness of Bifurcations in 3D Dendritic Trees: An Optimal Design.

van Pelt J, Uylings HB.

Front Comput Neurosci. 2012 Jan 25;5:54. doi: 10.3389/fncom.2011.00054. eCollection 2011.

17.

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.

Wan Y, Long F, Qu L, Xiao H, Hawrylycz M, Myers EW, Peng H.

Neuroinformatics. 2015 Oct;13(4):487-99. doi: 10.1007/s12021-015-9272-7.

PMID:
26036213
18.

Modelling brain-wide neuronal morphology via rooted Cayley trees.

Lin C, Huang Y, Quan T, Zhang Y.

Sci Rep. 2018 Oct 23;8(1):15666. doi: 10.1038/s41598-018-34050-1.

19.

Metrics for comparing neuronal tree shapes based on persistent homology.

Li Y, Wang D, Ascoli GA, Mitra P, Wang Y.

PLoS One. 2017 Aug 15;12(8):e0182184. doi: 10.1371/journal.pone.0182184. eCollection 2017.

20.

Quantitative 3-D analysis of GFAP labeled astrocytes from fluorescence confocal images.

Kulkarni PM, Barton E, Savelonas M, Padmanabhan R, Lu Y, Trett K, Shain W, Leasure JL, Roysam B.

J Neurosci Methods. 2015 May 15;246:38-51. doi: 10.1016/j.jneumeth.2015.02.014. Epub 2015 Mar 5.

PMID:
25745860

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