Quantitative analysis of axon elongation : a novel application of stochastic modeling techniques to long-term, high-resolution time-lapse microscopy of cortical axons

Axons exhibit a rich variety of behaviors, such as elongation, turning, branching, and fasciculation, all in service of the complex goal of wiring up the brain. In order to quantify these behaviors, I have developed a system for in vitro imaging of axon growth cones with time-lapse fluorescence microscopy. Image tiles are automatically captured and assembled into a mosaic image of a square millimeter region. GFP-expressing mouse cortical neurons can be imaged once every few minutes for up to weeks if phototoxicity is minimized. Looking at the data, the trajectories of axon growth cones seem to alternate between long, straight segments and sudden turns. I first rigorously test the idea that the straight segments are generated from a biased random walk by analyzing the correlation between growth cone steps in the time and frequency domain. To formalize and test the intuition that sharp turns join straight segments, I fit a hidden Markov model to time series of growth cone velocity vectors.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Sanjana, Neville Espi

Advisor dc:contributor.advisor

• H. Sebastian Seung.

• Brain and Cognitive Sciences.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/58281

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/58281