Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. Here, we applied this model to predict functional connectivity from structural connectivity in a sample of 998 participants from the Human Connectome Project. Our results showed that the graph neural network accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. These results represent an important finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the model's performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for depicting connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

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In cases of brain disease such as temporal lobe epilepsy (TLE), damage may lead to functional reorganization and a shift in language dominance to homolog regions in the other hemisphere. If the effects of TLE on language dominance are hemisphere-focused, then brain regions and connections involved in word reading should be less leftlateralized in left temporal lobe epilepsy (lTLE) than right temporal lobe epilepsy (rTLE) or healthy controls, and the opposite effect should be observed in patients with rTLE. In our study, functional magnetic resonance imaging (fMRI) showed that patients with rTLE had more strongly lateralized left hemisphere (LH) activation than patients with lTLE and healthy controls in language-related brain regions (pars opercularis and fusiform gyrus (FuG)). Corresponding with this difference, diffusion tensor imaging (DTI) found differences in connectivity indicative of patients with lTLE having greater tract integrity than patients with rTLE in the right hemisphere (RH) uncinate fasciculus (UF), inferior longitudinal fasciculus (ILF), and inferior fronto-occipital fasciculus (IFOF) using the network-based statistic analysis method. The UF, ILF, and IFOF tract integrity have previously been associated with lexical (whole-word) processing abilities. Multivariate distance matrix regression provided converging evidence for regions of the IFOF having different connectivity patterns between groups with lTLE and rTLE. This research demonstrates language lateralization differences between patient groups with lTLE and rTLE, and corresponding differences in the connectivity strength of the ILF, IFOF, and UF. This research provides a novel approach to measuring lateralization of language in general, and the fMRI and DTI findings were integral for guiding the neurosurgeons performing the TLE resections. This approach should inform future studies of language lateralization and language reorganization in patients such as those with TLE.

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The complexity of brain activity has recently been investigated using the Hurst (H) exponent, which describes the extent to which functional magnetic resonance imaging (fMRI) blood oxygen-level dependent (BOLD) activity is self-similar 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 degree 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.