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Functional Imaging Methods: Source Separation Techniques


Optical recording techniques have become a widespread technique for measuring the activity of neural populations. Typically, the optical signal is generated by a mixture of sources, where only some of them are related to the optical signal which is correlated with the neural activity of interest. In addition, the signal-to-noise ratio is often very low, in particular, for single condition maps or and / short recording sequences. In this project we investigated whether recently blind source separation techniques (ICA, extended spatial decorrelation, generalized linear models, etc.) can be properly extended to cope with abovementioned challenges. For optical imaging of intrinsic signals we found, that second order methods based on spatial decorrelation algorithms provided the best results. Methods were applied to optical recording data using intrinsic signals as well as to calcium imaging data.

Acknowledgement: Research was funded by DFG, Wellcome Trust, and Technische Universität Berlin.

Selected Publications:

Analysis of Calcium Imaging Signals from the Honeybee Brain by Nonlinear Models
Citation key Stetter2001
Author Stetter, M. and Greve, H. and Galizia, C. and Obermayer, K.
Pages 119 – 128
Year 2001
DOI 10.1006/nimg.2000.0679
Journal Neuroimage
Volume 13
Publisher Elsevier
Abstract Recent Ca$^2+$-imaging studies on the antennal lobe of the honeybee (Apis mellifera) have shown that olfactory stimuli evoke complex spatiotemporal changes of the intracellular Ca$^2+$ concentration, in which stimulus-dependent subsets of glomeruli are highlighted. In this work we use nonlinear models for the quantitative identification of the spatial and temporal properties of the Ca$^2+$-dependent fluorescence signal. This technique describes time-series of the Ca$^2+$ signal as a superposition of biophysically motivated model functions for photobleaching and Ca$^2+$-dynamics, provides optimal estimates of their amplitudes (signal strengths) and time-constants together with error measures. Using this method, we can reliably identify two different stimulus-dependent signal components. Their delays and rise times, $delta_c1 = (0.4 \\pm 0.3)$ s, $\\tau_c1 = (3.8 \\pm 1.2)$ s for the fast component and $\\delta_c2 = (2.4 \\pm 0.6)$ s, $\\tau_c2 = (10.3 \\pm 3.2)$ s for the slow component, are constant over space and across different odors and animals. In chronical experiments, the amplitude of the fast (slow) component often decreases (increases) with time. The pattern of the Ca$^2+$-dynamics in space and time can be reliably described as a superposition of only two spatiotemporally separable patterns based on the fast and slow components. However, the distributions of both components over space turn out to differ from each other, and more work has to be done in order to specify their relationship with neuronal activity.
Bibtex Type of Publication Selected:sources
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