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• W1997788017 abstract "Voltage sensitive dye imaging (VSDI) is the only technique that allows to directly measure neuronal activity over a large cortical population. It thus gives access to the dynamics of lateral interactions within or between cortical areas. However, VSDI signal suffers from a weak signal-to-noise ratio and processing methods are either rudimentary or dedicated to spatial or temporal denoising alone. Here we present an innovative method inspired by fMRI data processing, where the goal is to allow, for the first time, denoising of spatio-temporally inseparable VSDI signals and in the most challenging experimental condition, i.e. single trials in awake behaving monkeys. The method is based on a linear model (LM) decomposition of individual VSDI trials. The LM was designed meticulously by identifying all noise and signal components that are known to affect VSDI. We then compared its output against the classical methods based on blank division and detrending. LM proved to be significantly much more efficient to denoise spatial maps and temporal dynamics compared to these usual techniques. It also largely reduced trial-to-trial variability. These performances resulted in a four-fold improvement of signal-to-noise ratio and a two-fold increase of response detectability. Hence, with this method, fewer trials were needed to reach a high signal-to-noise ratio. Lastly, we showed that the LM method can accommodate for a large range of response dynamics, a crucial property for estimating spatial spread of activity or contrast dynamics. We believe that this method will make a strong contribution to imaging dynamics of population responses with high spatial and temporal resolution in trial-based experiments of awake animals." @default.
• W1997788017 created "2016-06-24" @default.
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• W1997788017 date "2011-01-01" @default.
• W1997788017 modified "2023-10-06" @default.
• W1997788017 title "Linear model decomposition for voltage-sensitive dye imaging signals: Application in awake behaving monkey" @default.
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• W1997788017 doi "https://doi.org/10.1016/j.neuroimage.2010.08.041" @default.
• W1997788017 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20800686" @default.
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