Blind signal separation from optical imaging recordings with extended spatial decorrelation

Abstract

Optical imaging is the video recording of two-dimensional patterns of changes in light reflectance from cortical tissue evoked by stimulation. We derived a method, extended spatial decorrelation (ESD), that uses second-order statistics in space for separating the intrinsic signals into the stimulus related components and the nonspecific variations. The performance of ESD on model data is compared to independent component analysis algorithms using statistics of fourth and higher order. Robustness against sensor noise is scored. When applied to optical images, ESD separates the stimulus specific signal well from biological noise and artifacts.

Imaging brain dynamics using independent component analysis

“…ICA has also been applied to MEG recordings [37] which carry signals from brain sources and are in part complementary to EEG signals, and to data from positron emission tomography (PET), a method for following changes in blood flow in the brain on slower time scales following the injection of radioactive isotopes into the bloodstream [62]. Other interesting applications of ICA are to the electrocorticogram (EcoG)-direct measurements of electrical activity from the surface of the cortex [63], and to optical recordings of electrical activity from the surface of the cortex using voltage-sensitive dyes [64]. Finally, ICA has proven effective at analyzing single-unit activity from the cerebral cortex [65], [66] and in separating neurons in optical recordings from invertebrate ganglia [67].…”

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density

“…Independent component analysis (ICA) (Comon 1994;Bell and Sejnowski 1995) is a technique that proved to be of use for this kind of blind source separation problem in many fields. In neuroscience it was used, among others, in the analysis of fMRI data (McKeown et al 1998;Stone et al 2002), optical imaging recordings (Schiessl et al 2000;Reidl et al 2007), in characterization of receptive fields (Saleem et al 2008), and in analysis of local field potentials (Tanskanen et al 2005).…”

Spatio-Temporal Autoregressive Models Defined Over Brain Manifolds

“…The task at hand is to determine the changing patterns of causal influences that different brain structures exert on each other by means of the Neuroinformatics _______________________________________________________________ Volume 2, 2004 analysis of dynamical brain imaging data. This type of data includes EEG/MEG source distributions (Valdés et al, 2000), optical recordings (Schiessl et al, 2000) and fMRI and are, from a statistical point of view, spatiotemporal data sets (Mardia et al, 1998;Wikle and Cressie, 1999)-that is, vector valued time series where the dimensionality of the vectors is very large, having originated from sampling over an underlying continuous manifold.…”