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A multi-variate blind source separation algorithm

  • Background and objective The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation. Methods The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations. Results The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar. Conclusion The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.

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Metadaten
Author:Markus GoldhackerORCiD, P. Keck, A. Igel, Elmar Wolfgang LangORCiD, Ana Maria ToméORCiD
DOI:https://doi.org/10.1016/j.cmpb.2017.08.019
ISSN:1872-7565
ISSN:0169-2607
Parent Title (German):Computer Methods and Programs in Biomedicine
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of first Publication:2017
Release Date:2022/08/11
Tag:Blind source separation; Independent component analysis; Resting state; Retinotopy; Spatio temporal; fMRI
Volume:151
First Page:91
Last Page:99
Institutes:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG