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Infinite Relational Modeling of Functional Connectivity in Resting State fMRI

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Keywords
  • Functional Magnetic Resonance Imaging (Fmri) Can Be Applied To Study The Functional Connectivity Of
  • Most Analyses Of Functional Resting State Networks (Rsn) Have Been Based On The Analysis Of Correlat
  • While These Models Can Identify Coherently Behaving Groups In Terms Of Correlation They Give Little
  • In This Paper We Take A Different View On The Analysis Of Functional Resting State Networks
  • Starting From The Definition Of Resting State As Functional Coherent Groups We Search For Functional
  • We Use The Infinite Relational Model (Irm) To Quantify Functional Coherent Groups Of Resting State N
Disciplines
  • Communication

Abstract

Infinite Relational Modeling of Functional Connectivity in Resting State fMRI - DTU Orbit (21/03/14) Infinite Relational Modeling of Functional Connectivity in Resting State fMRI - DTU Orbit (21/03/14) Infinite Relational Modeling of Functional Connectivity in Resting State fMRI Functional magnetic resonance imaging (fMRI) can be applied to study the functional connectivity of the neural elements which form complex network at a whole brain level. Most analyses of functional resting state networks (RSN) have been based on the analysis of correlation between the temporal dynamics of various regions of the brain. While these models can identify coherently behaving groups in terms of correlation they give little insight into how these groups interact. In this paper we take a different view on the analysis of functional resting state networks. Starting from the definition of resting state as functional coherent groups we search for functional units of the brain that communicate with other parts of the brain in a coherent manner as measured by mutual information. We use the infinite relational model (IRM) to quantify functional coherent groups of resting state networks and demonstrate how the extracted component interactions can be used to discriminate between functional resting state activity in multiple sclerosis and normal subjects. General information State: Published Organisations: Cognitive Systems, Department of Informatics and Mathematical Modeling Authors: Mørup, M. (Intern), Madsen, K. H. (Ekstern), Dogonowski, A. M. (Ekstern), Siebner, H. (Ekstern), Hansen, L. K. (Intern) Publication date: 2010 Host publication information Title: Neural Information Processing Systems : (NIPS) Volume: 23 Main Research Area: Technical/natural sciences Conference: 24th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 06/12/10 - 06/12/10 Documents: NIPS2010_1259.pdf Links: In external repository (Open Access) Source: orbit Source-ID: 274238 Publication: Research - pee

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