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BioMed Research International
Volume 2014, Article ID 947252, 14 pages
Research Article

Intrinsic Functional Connectivity Networks in Healthy Elderly Subjects: A Multiparametric Approach with Structural Connectivity Analysis

1Department of Neurology, University of Ulm, Oberer Eselsberg 45, 89081 Ulm, Germany
2Experimental Cardiovascular Imaging, Core Facility Small Animal MRI, University of Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany

Received 7 March 2014; Revised 1 May 2014; Accepted 3 May 2014; Published 29 May 2014

Academic Editor: Yong He

Copyright © 2014 Martin Gorges et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Intrinsic functional connectivity magnetic resonance imaging (iFCMRI) provides an encouraging approach for mapping large-scale intrinsic connectivity networks (ICNs) in the “resting” brain. Structural connections as measured by diffusion tensor imaging (DTI) are a major constraint on the identified ICNs. This study aimed at the combined investigation of ten well-defined ICNs in healthy elderly subjects at single subject level as well as at the group level, together with the underlying structural connectivity. IFCMRI and DTI data were acquired in twelve subjects (68 ± 7 years) at a 3T scanner and were studied using the tensor imaging and fiber tracking software package. The seed-based iFCMRI analysis approach was comprehensively performed with DTI analysis, following standardized procedures including an 8-step processing of iFCMRI data. Our findings demonstrated robust ICNs at the single subject level and conclusive brain maps at the group level in the healthy elderly sample, supported by the complementary fiber tractography. The findings demonstrated here provide a methodological framework for future comparisons of pathological (e.g., neurodegenerative) conditions with healthy controls on the basis of multiparametric functional connectivity mapping.