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• W2022608872 abstract "Event Abstract Back to Event A novel model-free fMRI data analysis technique based on clustering in a mutual information space Simon Benjaminsson1*, Peter Fransson2 and Anders Lansner1 1 Royal Institute of Technology , Sweden 2 Karolinska Institute, Sweden Non-parametric data-driven analysis techniques can be used to study large datasets with few assumptions about the data and underlying experiment. Variations of Independent Component Analysis (ICA) have been the methods mostly used on fMRI data, e.g. in finding resting-state networks thought to reflect the connectivity of the brain.Here we present a novel data analysis technique and demonstrate it on resting-state fMRI data. From the mutual information between the activities of the voxels over time, a distance matrix is created for all voxels in the input space. Multidimensional scaling is used to create a lower-dimensional map reflecting the dependency relations for the voxels based on this distance matrix. By performing clustering in the map we can find the strong statistical regularities in the data, which for the resting-state data turns out to be the resting-state networks.The results are compared to what ICA finds on the same multi-subject dataset. Contrary to ICA, the decomposition is performed in the last step of the algorithm and is computationally simple. This opens up for rapid analysis and visualization of the data on different spatial levels, as well as automatically finding a suitable number of decomposition components. The implementation of the algorithm has been parallelized and is capable of handling very large datasets. Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Poster Presentation Topic: Neuroimaging Citation: Benjaminsson S, Fransson P and Lansner A (2019). A novel model-free fMRI data analysis technique based on clustering in a mutual information space. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.028 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 May 2009; Published Online: 09 May 2019. * Correspondence: Simon Benjaminsson, Royal Institute of Technology, Stockholm, Sweden, simonbe@kth.se Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Simon Benjaminsson Peter Fransson Anders Lansner Google Simon Benjaminsson Peter Fransson Anders Lansner Google Scholar Simon Benjaminsson Peter Fransson Anders Lansner PubMed Simon Benjaminsson Peter Fransson Anders Lansner Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page." @default.
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• W2022608872 title "A novel model-free fMRI data analysis technique based on clustering in a mutual information space" @default.
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