Abstract
The complexity of brain activity has recently been investigated using the Hurst exponent (H), which describes the extent to which functional magnetic resonance imaging (fMRI) blood oxygen-level dependent (BOLD) activity is simple vs. complex. For example, research has demonstrated that fMRI activity is more complex before than after consumption of alcohol and during task than resting state. The measurement of H in fMRI is a novel method that requires the investigation of additional factors contributing to complexity. Graph theory metrics of centrality can assess how centrally important to the brain network each region is, based on diffusion tensor imaging (DTI) counts of probabilistic white matter (WM) tracts. DTI derived centrality was hypothesized to account for the complexity of functional activity, based on the supposition that more sources of information to integrate should result in more complex activity. FMRI BOLD complexity as measured by H was associated with five brain region centrality measures: degree, eigenvector, PageRank, current flow betweenness, and current flow closeness centrality. Multiple regression analyses demonstrated that eigenvector centrality was the most robust predictor of complexity, whereby greater centrality was associated with increased complexity (lower H). Regions known to be highly connected, including the thalamus and hippocampus, notably were among the highest in centrality and complexity. This research has led to a greater understanding of how brain region characteristics such as DTI centrality relate to the novel Hurst exponent approach for assessing brain activity complexity, and implications for future research that employ these measures are discussed.
Highlights
Brain structural connectivity predicts brain functional complexity: diffusion tensor imaging (DTI) derived centrality accounts for variance in fractal properties of fMRI signal Introduction Neuroimaging analyses have evolved since the advent of techniques such as functional magnetic resonance imaging to better understand what the blood oxygen level dependent (BOLD) signal implies about the underlying processing in the brain
In the present research we apply network science graph theory metrics of centrality as a novel way to further explore this important application of fractal analysis for characterizing fMRI activation complexity
Fractal analysis of fMRI activity is a recent technique demonstrating that lower H is related to higher complexity of cognitive processing (e.g., Maxim et al, 2005; He, 2011; Weber et al, 2014)
Summary
High quality DTI and resting state fMRI data for 100 unrelated subjects were obtained from the Human Connection Project (HCP) database (Van Essen et al, 2013; please see this paper for ethics statements). The degrees of freedom used in these analyses correspond to the 268 regions of the brain, with each region averaged over the 100 subjects In these linear regression models current flow closeness centrality and current flow betweenness centrality are positively associated with H, likely owing to their particular sensitivity to their position along shortest paths and distance from other regions, which measures routing rather than integration. In each of the degree centrality, eigenvector centrality, and PageRank centrality models, examples of the highest centrality regions, which had some of the lowest values of H (highest complexity), included the left and right anterior and posterior regions of the thalamus (well-known to be important for connecting regions related to various different modalities), and 13 regions of the medial temporal gyrus including the right posterior hippocampus (commonly viewed as being integral in the encoding of multi-modal information from many disparate brain regions, e.g., Damasio, 1989; see Bird & Burgess, 2008 for a review; see Figure 2 for example sagittal and axial brain slices of degree centrality and H in various regions including the thalamus and hippocampus; see Table 2)
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