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Analysis of Neural Data

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Principal Component Analysis and Blind Separation of Sources for Optical Imaging of Intrinsic Signals
Citation key Stetter2000a
Author Stetter, M. and Schießl, I. and Otto, T. and Sengpiel, F. and Hübener, M. and Bonhoeffer, T. and Obermayer, K.
Pages 482 – 490
Year 2000
DOI 10.1006/nimg.2000.0551
Journal Neuroimage
Volume 11
Publisher Elsevier
Abstract The analysis of data sets from optical imaging of intrinsic signals requires the separation of signals, which accurately reflect stimulated neuronal activity (mapping signal), from signals related to background activity. Here we show that blind separation of sources by Extended Spatial Decorrelation (ESD) is a powerful method for the extraction of the mapping signal from the total recorded signal. ESD is based on the assumptions, $(i)$ that each signal component varies smoothly across space and $(ii)$ that every component has zero cross-correlation functions with the other components. In contrast to the standard analysis of optical imaging data, the proposed method $(i)$ is applicable to non-orthogonal stimulus-conditions, $(ii)$ can remove the global signal, blood-vessel patterns and movement artifacts, $(iii)$ works without ad hoc assumptions about the data structure in the frequency domain, and $(iv)$ provides a confidence measure for the signals (Z-score). We first demonstrate on orientation maps from cat and ferret visual cortex, that Principal Component Analysis (PCA), which acts as a preprocessing step to ESD, can already remove global signals from image stacks, as long as data stacks for at least two – not necessarily orthogonal – stimulus conditions are available. We then show that the full ESD analysis can further reduce global signal components and – finally – concentrate the mapping signal within a single component both for differential image stacks and for image stacks recorded during presentation of a single stimulus.
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