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Mining local connectivity patterns in fMRI data - Zurich Open Repository and Archive


Loewe, Kristian; Grueschow, Marcus; Borgelt, Christian (2013). Mining local connectivity patterns in fMRI data. In: Christian, Borgelt; María Ángeles, Gil; João M C, Sousa; Michel, Verleysen. Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Berlin: Springer (Bücher), 305-317.

Abstract

A core task in the analysis of functional magnetic resonance imaging (fMRI) data is to detect groups of voxels that exhibit synchronous activity while the subject is performing a certain task. Synchronous activity is typically interpreted as functional connectivity between brain regions. We compare classical approaches like statistical parametric mapping (SPM) and some new approaches that are loosely based on frequent pattern mining principles, but restricted to the local neighborhood of a voxel. In particular, we examine how a soft notion of activity (rather than a binary one) can be modeled and exploited in the analysis process. In addition, we explore a fault-tolerant notion of synchronous activity of groups of voxels in both the binary and the soft/fuzzy activity setting. We apply the methods to fMRI data from a visual stimulus experiment to demonstrate their usefulness.

Abstract

A core task in the analysis of functional magnetic resonance imaging (fMRI) data is to detect groups of voxels that exhibit synchronous activity while the subject is performing a certain task. Synchronous activity is typically interpreted as functional connectivity between brain regions. We compare classical approaches like statistical parametric mapping (SPM) and some new approaches that are loosely based on frequent pattern mining principles, but restricted to the local neighborhood of a voxel. In particular, we examine how a soft notion of activity (rather than a binary one) can be modeled and exploited in the analysis process. In addition, we explore a fault-tolerant notion of synchronous activity of groups of voxels in both the binary and the soft/fuzzy activity setting. We apply the methods to fMRI data from a visual stimulus experiment to demonstrate their usefulness.

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Additional indexing

Item Type:Book Section, refereed, further contribution
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Language:English
Date:2013
Deposited On:11 Nov 2013 10:18
Last Modified:05 Apr 2016 17:07
Publisher:Springer (Bücher)
Series Name:Studies in Fuzziness and Soft Computing
Number:285
ISSN:1434-9922
ISBN:978-3-642-30277-0
Publisher DOI:https://doi.org/10.1007/978-3-642-30278-7_24

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