ArticleScientific reports2017
An analysis of signal processing algorithm performance for cortical intrinsic optical signal imaging and strategies for algorithm selection.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Excitatory and inhibitory networks diverge following early blindness.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Spatiotemporal characteristics of visual cortical responses to transpalpebral electrical stimulation.iScience · 2026Article
- Cortical functional connectivity in mouse models of early blindness: enucleation vs. anophthalmia.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Bridging the gap: umIT makes complex imaging data accessible to scientists of all backgrounds.Neurophotonics · 2025Article
- Dual-modal in vivo assessment for electrophysical and hemodynamic characteristics of cerebral edema induced by lipopolysaccharide.Biomedical engineering online · 2022Article
- Optimizing intact skull intrinsic signal imaging for subsequent targeted electrophysiology across mouse visual cortex.Scientific reports · 2022Article
- Modular head-mounted cortical imaging device for chronic monitoring of intrinsic signals in mice.Journal of biomedical optics · 2022Article
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Authors and funding
5 authors.
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Abstract
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.
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