Brain Structural Connectivity Predicts Brain Functional Complexity: Diffusion Tensor Imaging Derived Centrality Accounts for Variance in Fractal Properties of Functional Magnetic Resonance Imaging Signal

Abstract

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.

Gray and white matter abnormalities in primary trigeminal neuralgia with and without neurovascular compression

“…We observed that compared to HCs, significant decreases in FA and increases in RD were widespread over areas of the whole-brain WM skeleton in both TN wNVC and TN woNVC. Regarding TN wNVC, decreases in FA were coupled with increases in RD in areas of the corpus callosum, corona radiata and internal capsule, which indicates probable damage to pain integration, cognitiveaffective, and motor functions, also could be relative to compensatory mechanisms for this type of TN [40,41]. In addition, increases in RD in the areas of the posterior thalamic radiation, which is suggestive of damage to the microstructural properties of the thalamicsomatosensory.…”

“…In TN wNVC, the brain connections of temporal gyrus, occipital lobe, insula, and thalamus are related to multisensory integration of auditory, visual, olfactory, and somatosensory information [43]. Altered connectivity in these regions may interfere the temporal filtering of nociceptive information in offset analgesia [39], and underlie the psychological disturbances including anxiety, fear, and panic in TN patients [41], and sensory, cognitive-affective, and modulatory aspects of pain [30]. However, patients with TN woNVC exhibited higher local network integration in the hippocampus, PFC and angular gyrus as compared to HC, which may undermine the spatial filtering of nociceptive information in CPM, especially the top-down pain processing [39].…”

Rich-club reorganization and related network disruptions are associated with the symptoms and severity in classic trigeminal neuralgia patients

“…These regions are anatomically and widely connected through fiber tracts, such as the superior longitudinal fasciculus, fronto-occipital fasciculus, and fasciculus uncinatus, all of which have been found to be impairment in CTN patients ( DeSouza et al, 2014 , Liu et al, 2018 ). The alterations of these areas may reflect cognition-related perception and analgesia modulation ( Andica et al, 2020 , Iwata et al, 2005 , Seminowicz et al, 2004 ), multisensory integration ( Chiapparini et al, 2010 ) or the psychological complaints of chronic pain ( Neudorf et al, 2020 ). Overall, our findings indicate that the white matter related SN was more vulnerable than the FN in CTN patients, wherein the disorders of peripheral regions involving multiple networks mainly affected global information integration.…”

Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality Based on Structural Connectivity

“…Functional connectivity centrality has been used to demonstrate age and sex related differences (Zuo et al, 2012), differences between patient and control groups (for patients with schizophrenia, Chen et al, 2015;bipolar disorder, Deng et al, 2019, Zhou et al, 2017retinitus pigmentosa, Lin et al, 2021; and diabetic optic neuropathy, Xu et al, 2020), and differences related to genotype (Wink et al, 2018). Structural connectivity centrality has also been used to demonstrate differences between patient and control groups (for patients with prenatal alcohol exposure, Long et al, 2020; traumatic brain injury, Raizman et al, 2020;gut inflamation, Turkiewicz et al, 2021;and brain tumours, Yu et al, 2016), and to demonstrate a relationship between structural centrality and functional complexity (e.g., Hurst exponent; inversely related to fractal dimension, where a fractal dimension exceeding the topological dimension of the signal indicates complex functional activity) suggesting that regions integrating information from many sources have more complex functional activity (Neudorf et al, 2020). An important question that has not yet been explored, to our knowledge, is to what extent variance in functional connectivity-based centrality measures can be accounted for by structural connectivity and structural connectivity-based centrality.…”