Combining structural and functional neuroimaging data for studying brain connectivity: A review

Authors


  • This work was supported in part by NIMH grant #MH080182 to Gabriele Gratton and by NIA grant #AG21887 to Monica Fabiani. Elena Rykhlevskaia was supported by a Beckman Graduate Fellowship. This work is in partial fulfillment of the requirements for the degree of Ph.D. in Psychology of the first author.

Address reprint requests to: E. Rykhlevskaia, who is now at Stanford University, Stanford Cognitive and Systems Neuroscience Laboratory, 780 Welch Road, Room 201, Palo Alto, CA 94304, USA, E-mail: elenary@stanford.edu, or to G. Gratton, University of Illinois, Beckman Institute, 405 N. Mathews Ave., Urbana, IL 61801, USA; E-mail: grattong@uiuc.edu

Abstract

Different brain areas are thought to be integrated into large-scale networks to support cognitive function. Recent approaches for investigating structural organization and functional coordination within these networks involve measures of connectivity among brain areas. We review studies combining in vivo structural and functional brain connectivity data, where (a) structural connectivity analysis, mostly based on diffusion tensor imaging is paired with voxel-wise analysis of functional neuroimaging data or (b) the measurement of functional connectivity based on covariance analysis is guided/aided by structural connectivity data. These studies provide insights into the relationships between brain structure and function. Promising trends involve (a) studies where both functional and anatomical connectivity data are collected using high-resolution neuroimaging methods and (b) the development of advanced quantitative models of integration.

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