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  Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information

Biessmann, F., Murayama, Y., Logothetis, N., Müller, K.-R., & Meinecke, F. (2012). Non-separable Spatiotemporal Brain Hemodynamics Contain Neural Information. In G. Langs, I. Rish, & B. Murphy (Eds.), Machine Learning and Interpretation in Neuroimaging (pp. 140-147). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-8F70-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-8F72-E
Genre: Conference Paper

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 Creators:
Biessmann, F1, 2, Author              
Murayama, Y1, 2, Author              
Logothetis, NK1, 2, Author              
Müller, K-R, Author              
Meinecke, FC, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: The goal of many functional Magnetic Resonance Imaging (fMRI) studies is to infer neural activity from hemodynamic signals. Classical fMRI analysis approaches assume a canonical hemodynamic response function (HRF), which is identical in every voxel. Canonical HRFs imply space-time separability. Many studies explored the relevance of non-separable HRFs. These studies were focusing on the relationship between stimuli or electroencephalographic data and fMRI data. It is not clear from these studies whether non-separable spatiotemporal dynamics of fMRI signals contain neural information. This study provides direct empirical evidence that non-separable spatiotemporal deconvolutions of multivariate fMRI time series predict intracortical neural signals better than standard canonical HRF models. Our results demonstrate that there is more neural information in fMRI signals than detected by most analysis methods.

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 Dates: 2012
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-34713-9_18
 Degree: -

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Title: NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI 2011)
Place of Event: Sierra Nevada, Spain
Start-/End Date: 2011-12-16 - 2011-12-17

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Source 1

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Title: Machine Learning and Interpretation in Neuroimaging
Source Genre: Proceedings
 Creator(s):
Langs, G, Editor
Rish, I, Editor
Grosse-Wentrup, M1, Author            
Murphy, B, Editor
Affiliations:
1 Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 140 - 147 Identifier: ISBN: 978-3-642-34712-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 7263 Sequence Number: - Start / End Page: - Identifier: -