Abstract
Graph analysis has become an increasingly popular tool for characterizing topological properties of brain connectivity networks. Within this approach, the brain is modeled as a graph comprising N nodes connected by M edges. In functional magnetic resonance imaging (fMRI) studies, the nodes typically represent brain regions and the edges some measure of interaction between them. These nodes are commonly defined using a variety of regional parcellation templates, which can vary both in the volume sampled by each region, and the number of regions parcellated. Here, we sought to investigate how such variations in parcellation templates affect key graph analytic measures of functional brain organization using resting-state fMRI in 30 healthy volunteers. Seven different parcellation resolutions (84, 91, 230, 438, 890, 1314, and 4320 regions) were investigated. We found that gross inferences regarding network topology, such as whether the brain is small-world or scale-free, were robust to the template used, but that both absolute values of, and individual differences in, specific parameters such as path length, clustering, small-worldness, and degree distribution descriptors varied considerably across the resolutions studied. These findings underscore the need to consider the effect that a specific parcellation approach has on graph analytic findings in human fMRI studies, and indicate that results obtained using different templates may not be directly comparable.
Highlights
Our perceptions, thoughts, emotions and experiences are the product of dynamic interactions occurring between functionally specialized regions of the brain
For rs-functional magnetic resonance imaging (fMRI) analyses, 512 echo-planar imaging (EPI) volumes depicting blood oxygen level www.frontiersin.org dependent (BOLD) contrast were acquired of the whole-brain using the following parameters: repetition time (TR) = 1600 ms; echo time (TE) = 35 ms; number of excitations (NEX) = 1; number of slices = 23; slice thickness = 7 mm plus 0 mm interslice gap; Flip Angle (FA) = 90°; field of view (FOV) = 240 mm × 240 mm; image matrix size = 64 × 64; voxel dimensions = 3.75 mm × 3.75 mm
In this study we examined the effects of different parcellation scales on measures of brain network organization derived from the application of graph analytic techniques to human rs-fMRI data
Summary
Thoughts, emotions and experiences are the product of dynamic interactions occurring between functionally specialized regions of the brain. The organization of these resting-state networks recapitulates functional networks observed across a range of cognitive, emotional, motor, and perceptual tasks (Fox et al, 2006; Vincent et al, 2007; Smith et al, 2009) They are robust across individuals and time (Damoiseaux et al, 2006; Buckner et al, 2009; Shehzad et al, 2009), can affect task-evoked activity (Fox and Raichle, 2007; Hesselmann et al, 2008), correlate with behavioral measures (Seeley et al, 2007; Kelly et al, 2008; van den Heuvel et al, 2009), and emerge from underlying neuronal dynamics (Nir et al, 2007; Shmuel and Leopold, 2008). They provide an attractive means by which to characterize functional connectome properties
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