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Data-driven analysis for multimodal neuroimaging
Citation key Biessmann2011b
Author Bießmann, F.
Year 2012
School Technische Universität Berlin
Abstract Neuroimaging, the measurement, analysis and visualization of neural activity, contributed considerably to our understanding of information processing in the brain. The availability of non-invasive neuroimaging devices such as functional magnetic resonance imaging (fMRI) has been increasing rapidly throughout the last two decades. Nowadays every interested student can obtain non-invasively high resolution image time series of the blood-oxygen level dependent (BOLD) signal using fMRI. How exactly neural activity is reflected in the BOLD contrast is still subject of active research. For a more accurate interpretation of the fMRI signal and the underlying neurovascular coupling mechanisms, combined measurements of intracranial neural activity and fMRI signals are indispensable. Such simultaneous measurements have become technically possible – however appropriate analysis methods are still lacking. Classical analysis approaches rely on simplifying assumptions about the neurovascular coupling dynamics. These assumptions are convenient but numerous studies have provided empirical evidence against them. In this dissertation a novel analysis method, termed temporal kernel Canonical Correlation Analysis (tkCCA), will be developed, tested on artificial data and applied to experimental data in order to investigate neurovascular coupling mechanisms. TkCCA estimates dependency structures between high dimensional data with complex temporal coupling dynamics. The important advantages of tkCCA compared to standard methods are a) tkCCA can be directly applied to multimodal data, b) tkCCA is very efficient for high dimensional data with few data points (as is the case for fMRI) and c) tkCCA does not make use of restrictive assumptions about the data generating process. In particular tkCCA can be used to analyze high dimensional simultaneous measurements of neural activity and fMRI signals. Predictions of neural activity using tkCCA are better than when using classical methods. Basic research as well as clinical applications can profit from this more accurate prediction. Besides tkCCA is readily applicable to other domains in which data streams have high dimensional features that are non-instantaneously coupled, such as data from social networks in the World Wide Web.
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