- Title
- An analysis of signal processing algorithm performance for cortical intrinsic optical signal imaging and strategies for algorithm selection
- Creator
- Turley, J. A.; Zalewska, K.; Nilsson, M.; Walker, F. R.; Johnson, S. J.
- Relation
- Scientific Reports Vol. 7, no. 7198
- Publisher Link
- http://dx.doi.org/10.1038/s41598-017-06864-y
- Publisher
- Nature Publishing Group
- Resource Type
- journal article
- Date
- 2017
- Description
- Intrinsic Optical Signal (IOS) imaging has been used extensively to examine activity-related changes within the cerebral cortex. A significant technical challenge with IOS imaging is the presence of large noise, artefact components and periodic interference. Signal processing is therefore important in obtaining quality IOS imaging results. Several signal processing techniques have been deployed, however, the performance of these approaches for IOS imaging has never been directly compared. The current study aims to compare signal processing techniques that can be used when quantifying stimuli-response IOS imaging data. Data were gathered from the somatosensory cortex of mice following piezoelectric stimulation of the hindlimb. The effectiveness of each technique to remove noise and extract the IOS signal was compared for both spatial and temporal responses. Careful analysis of the advantages and disadvantages of each method were carried out to inform the choice of signal processing for IOS imaging. We conclude that spatial Gaussian filtering is the most effective choices for improving the spatial IOS response, whilst temporal low pass and bandpass filtering produce the best results for producing temporal responses when periodic stimuli are an option. Global signal regression and truncated difference also work well and do not require periodic stimuli.
- Subject
- biomedical engineering; electrical and electronic engineering; imaging and sensing; neuroscience
- Identifier
- http://hdl.handle.net/1959.13/1348918
- Identifier
- uon:30277
- Identifier
- ISSN:2045-2322
- Rights
- © The Author(s) 2017. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Language
- eng
- Full Text
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