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  Multivariate machine learning methods for fusing multimodal functional neuroimaging data

Dähne, S., Bießmann, F., Samek, W., Haufe, S., Goltz, D., Gundlach, C., et al. (2015). Multivariate machine learning methods for fusing multimodal functional neuroimaging data. In Proceedings of the IEEE (pp. 1507-1530). New York, N.Y.: IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-3120-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-0008-5
Genre: Conference Paper

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 Creators:
Dähne, Sven1, Author
Bießmann, Felix2, Author
Samek, Wojciech3, Author
Haufe, Stefan4, Author
Goltz, Dominique5, Author              
Gundlach, Christopher5, Author              
Villringer, Arno6, Author              
Fazli, Siamac7, Author
Müller, Klaus-Robert1, Author
Affiliations:
1Department of Software Engineering and Theoretical Computer Science, TU Berlin, Germany, ou_persistent22              
2Amazon, Berlin, Germany, ou_persistent22              
3Department of Video Coding and Analytics, Fraunhofer Heinrich Hertz Institute, Berlin, Germany, ou_persistent22              
4Laboratory for Intelligent Imaging and Neural Computing (LIINC), Columbia University in the City of New York, NY, USA, ou_persistent22              
5Institute of Psychology, University of Leipzig, Germany, ou_persistent22              
6Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
7Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea, ou_persistent22              

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Free keywords: fNIRS; Machine learning; Multimodal neuroimaging; Data fusion; Review; EEG; MEG; fMRI
 Abstract: Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.

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Language(s): eng - English
 Dates: 2015-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1109/JPROC.2015.2425807
 Degree: -

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Title: Proceedings of the IEEE
  Other : Proc. IEEE
Source Genre: Proceedings
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Publ. Info: New York, N.Y. : IEEE
Pages: - Volume / Issue: 103 (9) Sequence Number: - Start / End Page: 1507 - 1530 Identifier: ISSN: 0018-9219
CoNE: /journals/resource/991042729498374