Literature DB >> 16988438

Automated imaging of C. elegans behavior.

Christopher J Cronin1, Zhaoyang Feng, William R Schafer.   

Abstract

Automated systems for recording and analyzing behavior have many applications for the study of neurobiology in Caenorhabditis elegans. In particular, machine-based approaches allow for precise quantitative definitions of behavioral phenotypes that have traditionally been subjectively described by individual observers. Automated systems also facilitate the analysis of behaviors that occur over long time scales or are difficult to detect by eye. Here we describe the detailed methodology for the use of one recently described automated tracking system for C. elegans. These protocols make it possible to measure a wide range of parameters related to the morphology, body posture, and locomotion patterns of individual wild-type and mutant nematodes.

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Year:  2006        PMID: 16988438     DOI: 10.1385/1-59745-151-7:241

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  21 in total

1.  Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans.

Authors:  Jeffrey P Nguyen; Frederick B Shipley; Ashley N Linder; George S Plummer; Mochi Liu; Sagar U Setru; Joshua W Shaevitz; Andrew M Leifer
Journal:  Proc Natl Acad Sci U S A       Date:  2015-12-28       Impact factor: 11.205

2.  Biomechanical analysis of gait adaptation in the nematode Caenorhabditis elegans.

Authors:  Christopher Fang-Yen; Matthieu Wyart; Julie Xie; Risa Kawai; Tom Kodger; Sway Chen; Quan Wen; Aravinthan D T Samuel
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-03       Impact factor: 11.205

3.  Controlling airborne cues to study small animal navigation.

Authors:  Marc Gershow; Matthew Berck; Dennis Mathew; Linjiao Luo; Elizabeth A Kane; John R Carlson; Aravinthan D T Samuel
Journal:  Nat Methods       Date:  2012-01-15       Impact factor: 28.547

4.  Rapid and accurate developmental stage recognition of C. elegans from high-throughput image data.

Authors:  Amelia G White; Patricia G Cipriani; Huey-Ling Kao; Brandon Lees; Davi Geiger; Eduardo Sontag; Kristin C Gunsalus; Fabio Piano
Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit       Date:  2010-08-05

5.  High-throughput optical quantification of mechanosensory habituation reveals neurons encoding memory in Caenorhabditis elegans.

Authors:  Takuma Sugi; Yasuko Ohtani; Yuta Kumiya; Ryuji Igarashi; Masahiro Shirakawa
Journal:  Proc Natl Acad Sci U S A       Date:  2014-11-17       Impact factor: 11.205

6.  Mechanistic analysis of the search behaviour of Caenorhabditis elegans.

Authors:  Liliana C M Salvador; Frederic Bartumeus; Simon A Levin; William S Ryu
Journal:  J R Soc Interface       Date:  2014-01-15       Impact factor: 4.118

7.  Micro-electro-fluidic grids for nematodes: a lens-less, image-sensor-less approach for on-chip tracking of nematode locomotion.

Authors:  Peng Liu; Richard J Martin; Liang Dong
Journal:  Lab Chip       Date:  2013-02-21       Impact factor: 6.799

8.  A comparison of experience-dependent locomotory behaviors and biogenic amine neurons in nematode relatives of Caenorhabditis elegans.

Authors:  Laura Rivard; Jagan Srinivasan; Allison Stone; Stacy Ochoa; Paul W Sternberg; Curtis M Loer
Journal:  BMC Neurosci       Date:  2010-02-19       Impact factor: 3.288

9.  Fast, automated measurement of nematode swimming (thrashing) without morphometry.

Authors:  Steven D Buckingham; David B Sattelle
Journal:  BMC Neurosci       Date:  2009-07-20       Impact factor: 3.288

10.  AutoEPG: software for the analysis of electrical activity in the microcircuit underpinning feeding behaviour of Caenorhabditis elegans.

Authors:  James Dillon; Ioannis Andrianakis; Kate Bull; Steve Glautier; Vincent O'Connor; Lindy Holden-Dye; Christopher James
Journal:  PLoS One       Date:  2009-12-29       Impact factor: 3.240

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