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Comparison of Mass-Univariate, Unimodal and Multivariate Multimodal Analysis Methods for Neurovascular Coupling Analysis

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Biessmann,  F
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Murayama,  Y
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Logothetis,  NK
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Biessmann, F., Meinecke, F., Murayama, Y., Logothetis, N., & Müller, K. (2010). Comparison of Mass-Univariate, Unimodal and Multivariate Multimodal Analysis Methods for Neurovascular Coupling Analysis. Frontiers in Computational Neuroscience, 2010(Conference Abstract: Bernstein Conference on Computational Neuroscience). doi:10.3389/conf.fncom.2010.51.00075.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BE1C-7
Abstract
In the past years multimodal brain imaging methods have yielded valuable insights into how functional magnetic resonance imaging (fMRI) signals are related to the underlying neural activity. However, the rapid advances in multimodal imaging technology were not accompanied by the development of appropriate analysis methods for multimodal data. We present a multimodal analysis framework, temporal kernel Canonical Correlation Analysis (tkCCA) [1,2], and show how it can be used to analyse the spatio-temporal and time-frequency correlation structure between simultaneously measured intracortical neurophysiological recordings and high dimensional fMRI signals. Given the spectrograms of neurophysiological activity and the simultaneously recorded fMRI data we estimate a convolution linking di_erent bands of neural bandpower to an activity pattern of fMRI signals. The convolution can be interpreted as the pattern in time-frequency and time-voxel space that maximises the canonical correlation [3] between neural and haemodynamic activity. We show results from data recorded in primary visual cortex during spontaneous activity and during visual stimulation. The analysis resulted in robust neurovascular coupling patterns across different experimental conditions. We compared the multivariate patterns with univariate coupling measures and spatial principal component analysis (SPCA) by measuring the accuracy when predicting neural activity from BOLD signals. Our _ndings suggest that the _lters estimated by tkCCA predict neural activity better than univariate methods and unimodal methods such as SPCA.