- Poster presentation
- Open Access
Removing bleaching artifacts from voltage sensitive dye recordings with ICA
© Fathiazar et al; licensee BioMed Central Ltd. 2013
Published: 8 July 2013
Voltage sensitive dye (VSD) imaging of multiple neurons becomes one of the most promising up-to-date methods to investigate neuronal network activity. However, optical imaging signals are often superimposed by noise and artifacts. Hence, post-processing methods are needed to overcome this corruption and separate neuronal activity from other signals. One of the significant artifacts in VSD imaging is bleaching, a decrease of the optical signal while the recorded signal of the local field potentials remains unchanged . In this study we used independent component analysis (ICA) in comparison to principal component analysis (PCA) and detrend method to separate the neuronal VSD signals from bleaching artifacts. ICA is a blind source separation method that has been used in many different approaches such as to recover action potentials of neurons in multiple-detector optical recordings . We used the ICA-DTU Toolbox [http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=4043] with maximum likelihood formulation to identify neuronal signals and the effect of bleaching.
- Canepari M, Zecevic D: Membrane Potential Imaging in the Nervous System: Methods and Applications. 2010, New York: SpringerGoogle Scholar
- Miller EW, Lin JY, Frady EP, Steinbach PA, Kristan WB, Tsien RY: Optically monitoring voltage in neurons by photo-induced electron transfer through molecular wires. PNAS. 2012, 109 (6): 2114-2119. 10.1073/pnas.1120694109.PubMed CentralView ArticlePubMedGoogle Scholar
- Brown GD, Yamada S, Sejnowski TJ: Independent component analysis at neural cocktail party. Trends Neurosci. 2001, 24: 54-63. 10.1016/S0166-2236(00)01683-0.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.