Determining Functional Connectivity using fMRI Data with Diffusion-Based Anatomical Weighting

• Type of Material: Article
• Authors: F Dubois Bowman, Emory University; Lijun Zhang, Emory University; Gordana Derado, Emory University; Shuo Chen, Emory University
• Language: English
• Date: 2012-09
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1016/j.neuroimage.2012.05.032
• Title of Journal or Parent Work: NeuroImage
• ISSN: 1053-8119
• Volume: 62
• Issue: 3
• Start Page: 1769
• End Page: 1779
• Grant/Funding Information: This work was supported by the National Institutes of Health grants R01-MH079251 and T32 GM074909-01.
• Keywords: fMRI; auditory processing; clustering; functional connectivity; resting-state networks; DTI; structural connectivity
• Research Categories: Biology, Biostatistics; Biology, Bioinformatics

Abstract

There is strong interest in investigating both functional connectivity (FC) using functional magnetic resonance imaging (fMRI) and structural connectivity (SC) using diffusion tensor imaging (DTI). There is also emerging evidence of correspondence between functional and structural pathways within many networks (Skudlarski et al., 2008; van den Heuvel et al., 2009; Greicius, et al., 2009), although some regions without SC exhibit strong FC (Honey et al., 2009). These findings suggest that FC may be mediated by (direct or indirect) anatomical connections, offering an opportunity to supplement fMRI data with DTI data when determining FC. We develop a novel statistical method for determining FC, called anatomically-weighted FC (awFC), which combines fMRI and DTI data. Our awFC approach implements a hierarchical clustering algorithm that establishes neural processing networks using a new distance measure consisting of two components, a primary functional component that captures correlations between fMRI signals from different regions and a secondary anatomical weight reflecting probabilities of SC. The awFC approach defaults to conventional unweighted clustering for specific parameter settings. We optimize awFC parameters using a strictly functional criterion, therefore our approach will generally perform at least as well as an unweighted analysis, with respect to intracluster coherence or autocorrelation. AwFC also yields more informative results since it provides structural properties associated with identified functional networks. We apply awFC to two fMRI data sets: resting-state data from 6 healthy subjects and data from 17 subjects performing an auditory task. In these examples, awFC leads to more highly autocorrelated networks than a conventional analysis. We also conduct a simulation study, which demonstrates accurate performance of awFC and confirms that awFC generally yields comparable, if not superior, accuracy relative to a standard approach.