Application of Blind Separation of Sources to Optical Recording of Brain Activity

Schöner, Holger; Stetter, Martin; Schießl, Ingo; Mayhew, J.E.W.; Lund, J.S.; McLoughlin, N.; Obermayer, Klaus

by Holger Schöner, Martin Stetter, Ingo Schießl, J.E.W. Mayhew, J.S. Lund, N. McLoughlin, Klaus Obermayer

Abstract:

In the analysis of data recorded by optical imaging from intrinsic signals (measurement of changes of light reflectance from cortical tissue) the removal of noise and artifacts such as blood vessel patterns is a serious problem. Often bandpass filtering is used, but the underlying assumption that a spatial frequency exists, which separates the mapping component from other components (especially the global signal), is questionable. Here we propose alternative ways of processing optical imaging data, using blind source separation techniques based on the spatial decorrelation of the data. We first perform benchmarks on artificial data in order to select the way of processing, which is most robust with respect to sensor noise. We then apply it to recordings of optical imaging experiments from macaque primary visual cortex. We show that our BSS technique is able to extract ocular dominance and orientation preference maps from single condition stacks, for data, where standard post-processing procedures fail. Artifacts, especially blood vessel patterns, can often be completely removed from the maps. In summary, our method for blind source separation using extended spatial decorrelation is a superior technique for the analysis of optical recording data.

Reference:

Application of Blind Separation of Sources to Optical Recording of Brain Activity (Holger Schöner, Martin Stetter, Ingo Schießl, J.E.W. Mayhew, J.S. Lund, N. McLoughlin, Klaus Obermayer), In Advances in Neural Information Processing Systems NIPS 12 (S.A. Solla, T.K. Leen, K.-R. Müller, eds.), MIT Press, 2000.

Bibtex Entry:

@inproceedings{schoner_application_2000,
	title = {Application of Blind Separation of Sources to Optical Recording of Brain Activity},
	abstract = {In the analysis of data recorded by optical imaging from intrinsic signals (measurement of changes of light reflectance from cortical tissue) the removal of noise and artifacts such as blood vessel patterns is a serious problem. Often bandpass filtering is used, but the underlying assumption that a spatial frequency exists, which separates the mapping component from other components (especially the global signal), is questionable. Here we propose alternative ways of processing optical imaging data, using blind source separation techniques based on the spatial decorrelation of the data. We first perform benchmarks on artificial data in order to select the way of processing, which is most robust with respect to sensor noise. We then apply it to recordings of optical imaging experiments from macaque primary visual cortex. We show that our {BSS} technique is able to extract ocular dominance and orientation preference maps from single condition stacks, for data, where standard post-processing procedures fail. Artifacts, especially blood vessel patterns, can often be completely removed from the maps. In summary, our method for blind source separation using extended spatial decorrelation is a superior technique for the analysis of optical recording data.},
	booktitle = {Advances in Neural Information Processing Systems {NIPS} 12},
	publisher = {{MIT} Press},
	author = {Schöner, Holger and Stetter, Martin and Schießl, Ingo and Mayhew, J.E.W. and Lund, J.S. and McLoughlin, N. and Obermayer, Klaus},
	editor = {Solla, S.A. and Leen, T.K. and Müller, K.-R.},
	year = {2000},
	keywords = {blind source separation, optical imaging},
	pages = {949--955},
	file = {download/publications/Schoener2000_NIPS_BSSforOI.pdf}
}