Structural Connectivity Networks Research Articles

Topological Alterations of the Structural Brain Connectivity Network in Children with Juvenile Neuronal Ceroid Lipofuscinosis.

We used diffusion MR imaging to investigate the structural brain connectivity networks in juvenile neuronal ceroid lipofuscinosis, a neurodegenerative lysosomal storage disease of childhood. Although changes in conventional MR imaging are typically not visually apparent in children aged <10 years, we previously found significant microstructural abnormalities by using diffusion MR imaging. Therefore, we hypothesized that the structural connectivity networks would also be affected in the disease. We acquired diffusion MR imaging data from 14 children with juvenile neuronal ceroid lipofuscinosis (mean ± SD age, 9.6 ± 3.4 years; 10 boys) and 14 control subjects (mean ± SD age, 11.2 ± 2.3 years; 7 boys). A follow-up MR imaging was performed for 12 of the patients (mean ± SD age, 11.4 ± 3.2 years; 8 boys). We used graph theoretical analysis to investigate the global and local properties of the structural brain connectivity networks reconstructed with constrained spherical deconvolution-based whole-brain probabilistic tractography. We found significantly increased characteristic path length (P = .003) and decreased degree (P = .003), which indicated decreased network integration and centrality in children with juvenile neuronal ceroid lipofuscinosis. The findings were similar for the follow-up MR imaging, and there were no significant differences between the two acquisitions of the patients. In addition, we found that the disease severity correlated negatively (P < .007) with integration, segregation, centrality, and small-worldness of the networks. Moreover, we found significantly (P < .0003) decreased local efficiency in the left supramarginal gyrus and temporal plane, and decreased strength in the right lingual gyrus. We found significant global and local network alterations in juvenile neuronal ceroid lipofuscinosis that correlated with the disease severity and in areas related to the symptomatology.

Single image rain removal using image decomposition and a dense network

Removing rain from a single image is a challenging task due to the absence of temporal information. Considering that a rainy image can be decomposed into the low-frequency &#x0028 LF &#x0029 and high-frequency &#x0028 HF &#x0029 components, where the coarse scale information is retained in the LF component and the rain streaks and texture correspond to the HF component, we propose a single image rain removal algorithm using image decomposition and a dense network. We design two task-driven sub-networks to estimate the LF and non-rain HF components of a rainy image. The high-frequency estimation sub-network employs a densely connected network structure, while the low-frequency sub-network uses a simple convolutional neural network &#x0028 CNN &#x0029 . We add total variation &#x0028 TV &#x0029 regularization and LF-channel fidelity terms to the loss function to optimize the two subnetworks jointly. The method then obtains de-rained output by combining the estimated LF and non-rain HF components. Extensive experiments on synthetic and real-world rainy images demonstrate that our method removes rain streaks while preserving non-rain details, and achieves superior de-raining performance both perceptually and quantitatively.

Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics.

Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed in vivo may therefore be a result of evolutionary selection for optimal information encoding and expect the methodology developed to be generalizable for alternative model networks as well as experimental paradigms.

Functional-structural relationship in large-scale brain networks of patients with end stage renal disease after kidney transplantation: A longitudinal study.

It is unclear how the brain network changed after kidney transplantation (KT). We explored the patterns of large‐scale complex network after KT in end‐stage renal disease (ESRD) patients with resting‐state functional MRI (rs‐fMRI) and diffusion tensor imaging (DTI). Twenty‐one ESRD patients (14 men; mean age, 31.5 ± 9.9 years) scheduled for KT and 17 age‐ and gender‐matched healthy controls (HC) (8 men; mean age, 28.9 ± 7.2 years) were enrolled in this study. Each participant underwent rs‐fMRI and DTI scans in three time points (pre‐KT, 1 and 6 months after KT [for ESRD]). Graph theory analysis was used to characterize the topological properties by using functional and structural network connectivities intergroup correlation analysis was performed between functional/structural MR indexes and clinical markers. Compared with HC, pre‐KT ESRD patients showed an altered topological organization in both functional and structural networks. Compared with pre‐KT, increased node degree and node efficiency were observed for both functional and structural networks at 1 month after KT (all p < .05), which were further increased at 6 months after KT (p < .05). Both functional and structural networks did not recover completely at 6 months after KT (all p < .05). The patients showed an increased functional–structural connectivity coupling at 1 month after KT compared with HC (p = .041). A trend of progressive recovery of functional and structural connectivity networks was observed in ERSD patients after KT, which did not recover to the normal levels even in 6 months after KT. The study results underlie cognitive function recovery in ESRD patients following KT in the neuropathophysiological perspective.

Large‐scale‐free network organisation is likely key for biofilm phase transition

Non-linear Kuramoto model has been used to study synchronised or sync behaviour in numerous fields; however, its application in biology is scarce. Here, the basic model has been introduced and examples where large-scale small-world or scale-free networks are crucial for spontaneous sync have been provide even for low coupling strength. This information was next checked for relevance in living systems where it is now well known that biological networks are scale-free. A recent transcriptome-wide data analysis of a Saccharomyces cerevisiae biofilm showed that low- to middle-expressed genes are key for scale invariance in biology. Together, the current data indicate that a biological network connectivity structure with low coupling strength, or expression levels, is sufficient for sync behaviour. For biofilm regulation, it may, therefore, be necessary to investigate large-scale low-expression genes rather than small-scale high-expression genes.

CNN-based health model using knowledge mining of influencing factors

In modern society, the number of chronic patients is increasing due to various causes, such as drinking, smoking, unhealthy lifestyles, and stress. Chronic diseases must be managed with constant care, but may get worse from various factors. With the development of information technology, healthcare technologies using health big data, machine learning, and reinforcement learning are attracting attention. Using these technologies, it is possible to predict potential diseases that may occur in the future by using data learning and clustering of similar data. To predict the potential for disease, we should research various models based on the convolutional neural network (CNN), which can identify knowledge objects from unstructured data such as medical data. However, the fully connected network structure of the CNN generally uses a large amount of memory. Another problem is that complexity increases with the number of layers. This causes the overfitting problem, which increases error. To solve this problem, this paper proposes a CNN-based health model using knowledge mining of influencing factors. The proposed method uses hidden layers of a double-layer structure within the CNN structure. The double-layer structure has the optimal conditions for classification, compared with a single layer that allows the AND/OR operations. First, the amount of data used is reduced by extracting influencing factors through multivariate analysis, and these influencing factors are used as input data. Significant influencing factors are extracted from the first hidden layer using the significance level. This improves accuracy, because it extracts data required for analysis. Common influencing factors appropriate for significance levels are extracted. Common influencing factors refer to correlated factors that can affect each other. In the second hidden layer, the correlations between influencing factors are discovered through a correlation coefficient, and they are classified into positive and negative factors. Furthermore, associated rules are discovered through knowledge mining from among the classified influencing factors. They are subdivided into influencing factors like obesity, high blood pressure, and diabetes through the rules of the discovered influencing factors. For performance evaluation, the root mean square error (RMSE) of the CNN model is evaluated according to the application of knowledge mining to the influencing factors. The evaluation of accuracy, computational load, complexity, and learning rate showed better results, compared with the existing method. Through the proposed health model, knowledge about the associations of various factors is derived.

The Neural Architecture of General Knowledge

Cognitive performance varies widely between individuals and is highly influenced by structural and functional properties of the brain. In the past, neuroscientific research was principally concerned with fluid intelligence, while neglecting its equally important counterpart crystallized intelligence. Crystallized intelligence is defined as the depth and breadth of knowledge and skills that are valued by one's culture. The accumulation of crystallized intelligence is guided by information storage capacities and is likely to be reflected in an individual's level of general knowledge. In spite of the significant role general knowledge plays for everyday life, its neural foundation largely remains unknown. In a large sample of 324 healthy individuals, we used standard magnetic resonance imaging along with functional magnetic resonance imaging and diffusion tensor imaging to examine different estimates of brain volume and brain network connectivity and assessed their predictive power with regard to both general knowledge and fluid intelligence. Our results demonstrate that an individual's level of general knowledge is associated with structural brain network connectivity beyond any confounding effects exerted by age or sex. Moreover, we found fluid intelligence to be best predicted by cortex volume in male subjects and functional network connectivity in female subjects. Combined, these findings potentially indicate different neural architectures for information storage and information processing. © 2019 European Association of Personality Psychology

Analysis of structural connectivity network of basal ganglia in mouse brain: MR diffusion-tractography at 9.4 T

Analysis of structural connectivity network of basal ganglia in mouse brain: MR diffusion-tractography at 9.4 T

Changes of white matter integrity and structural network connectivity in nondemented cerebral small‐vessel disease

Previous studies have found widespread impairment of white matter (WM) integrity and disruption of structural network connectivity in cerebral small-vessel disease, but have not evaluated these changes jointly in nondemented patients. To jointly investigate the microstructural impairment of WM and the related alterations of structural network topology in nondemented cerebral small-vessel disease (CSVD-ND). Prospective. Thirty-seven CSVD-ND patients and 34 elderly controls, who were age-, sex-, and education-matched. 3.0T/diffusion tensor imaging (DTI). Clinical characteristics, lacunar infarct, and white matter hyperintensity (WMH) was assessed. A multiatlas likelihood fusion (MALF) algorithm was used for DTI-based brain segmentation and network node defining. Then the alterations of WM integrity and structural network topology were investigated jointly. Student's t-test, chi-square test, Mann-Whitney U-test, linear regression, Pearson correlation, and multiple comparison correction. Decreased fractional anisotropy and increased trace values were observed in predefined structures (P < 0.05, familywise error rate-corrected), including major commissural fibers, projection fibers, and some association fibers. Topologically, both groups showed small-worldness. CSVD-ND patients showed reduced global and local efficiency (P < 0.001). Despite widespread impairment of WM integrity, CSVD-ND patients only showed reduced nodal efficiency in the right superior occipital gyrus and the right lingual gyrus (P < 0.05, familywise error rate-corrected). The nodal local efficiency of the right precuneus was associated with the processing speed after adjusting the effect of lacunar infarct and WMH (r = -0.499, P = 0.038). WM integrity was widely impaired in nondemented CSVD patients but structural network connectivity was relatively preserved. DTI may potentially provide information for the pathophysiology of CSVD in the nondemented phase. 1 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:1162-1169.

A multilayer exponential random graph modelling approach for weighted networks

A new modelling approach for the analysis of weighted networks with ordinal/ polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random graph model (ERGM) generative process where each network layer represents a different ordinal dyadic category. The network layers are assumed to be generated by an ERGM process conditional on their closest lower network layers. A crucial advantage of the proposed method is the possibility of adopting the binary network statistics specification to describe both the between-layer and across-layer network processes and thus facilitating the interpretation of the parameter estimates associated to the network effects included in the model. The Bayesian approach provides a natural way to quantify the uncertainty associated to the model parameters. From a computational point of view, an extension of the approximate exchange algorithm is proposed to sample from the doubly-intractable parameter posterior distribution. A simulation study is carried out on artificial data and applications of the methodology are illustrated on well-known datasets. Finally, a goodness-of-fit diagnostic procedure for model assessment is proposed.

Construction of brain structural connectivity network using a novel integrated algorithm based on ensemble average propagator

An important task for neuroscience is to accurately construct structural connectivity network of human brain. Tractography constructed based on high angular resolution diffusion imaging (HARDI) provides valuable information of human brain structural connections. Existing algorithms, mainly categorized as deterministic or probabilistic, come with inherent limitations (e.g., fiber direction uncertainty induced by noise, or anatomically unreasonable connections and heavy computational cost). In this study, a novel integrated algorithm was proposed to construct brain structural connectivity network by incorporating the deterministic path planning and probabilistic connection strength estimation, based on ensemble average propagator (EAP). We first estimated EAPs from multi-shell samples using the spherical polar Fourier imaging (SPFI), and then extracted diffusion orientations coinciding with neural fiber tracts. Only under angular constraints, the deterministic path planning algorithm was subsequently used to find all reasonable pathways between pairwise white matter (WM) voxels in different regions of interest (ROIs). Consequently, a train of consecutive WM voxels along each of the identified pathways was determined, and the connection strength of these pathways was computed by integrating their EAP alignment over a solid angle. The connection strength of a pair of WM voxels was assigned as the connection strength with the largest connection possibility. Finally, the connection strength between two ROIs was calculated as the sum of all the connection probabilities of each pair of WM voxels in the ROIs. A comparison against voxel-graph based probabilistic tractography method was performed on Fibercup phantom dataset, and the results demonstrated that the proposed method can produce better structural connection and is more computationally economical. Lastly, three datasets from Human Connectome Project (HCP) S1200 group were tested and their structural connectivity networks were constructed for topological analysis. The results showed great consistency in network metrics with previous WM network studies in healthy adults.

Weighted kshell degree neighborhood: A new method for identifying the influential spreaders from a variety of complex network connectivity structures

Due to the fast and worldwide growth of the social network, it has become a potent platform for broadcasting any information. Through the network, people can easily reach to a mass, can easily propagate a piece of information within a short time. Considering the advantages, especially to accelerate the information spreading or controlling the spreading, the organizations want to exploit the social network to its best. However, as we know, the network is formed by connecting one node (i.e., user) to another node, and it is not that all the nodes will be effective equally in spreading. Because it depends on many factors and one of them is their topological position in the network. Automatically finding the effective nodes (the influential spreaders) from a network is a real challenge. In the literature, kshell decomposition and degree centrality are the two popular measures for identifying the influential spreaders from a network. Moreover, it is more challenging in identifying the influential spreaders when network connectivity structure varies from network to network. It has been found that the kshell decomposition method works better in the complete global network connectivity structures and neighbors’ degree method in the incomplete global network connectivity structures. But the degree of completeness of the network connectivity structures also vary. Under this circumstance, only the kshell method or only the neighbors’ degree method will not be able to obtain the best influential spreaders. To overcome this problem, this article proposes an indexing method weighted kshell degree neighborhood which is a composition of kshell and degree through tunable parameters. We have evaluated the effectiveness of the proposed method using different real networks and the Susceptible-Infected-Recovered (SIR) spreading epidemic model. The results show that the proposed method can significantly obtain the best spreading dynamics from different varieties of network connectivity structures and outperforms the other existing indexing methods.

Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence.

The cortex is organised into broadly hierarchical functional systems with distinct neuroanatomical characteristics reflected by macroscopic measures of cortical morphology. Diffusion-weighted magnetic resonance imaging allows the delineation of areal connectivity, changes to which reflect the ongoing maturation of white matter tracts. These developmental processes are intrinsically linked with timing coincident with the development of cognitive function. In this study, we use a data-driven multivariate approach, nonnegative matrix factorisation, to define cortical regions that co-vary together across a large paediatric cohort (n = 456) and are associated with specific subnetworks of cortical connectivity. We find that age between 3 and 21 years is associated with accelerated cortical thinning in frontoparietal regions, whereas relative thinning of primary motor and sensory regions is slower. Together, the subject-specific weights of the derived set of cortical components can be combined to predict chronological age. Structural connectivity networks reveal a relative increase in strength in connection within, as opposed to between hemispheres that vary in line with cortical changes. We confirm our findings in an independent sample.

Alterations of white matter network in patients with left and right non-lesional temporal lobe epilepsy.

The goal of this study was to investigate alterations of white matter (WM) network in patients with left non-lesional temporal lobe epilepsy (nl-TLE) and right nl-TLE to assess the relationship between the white matter network properties and clinical parameters. T1 magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) were acquired for 45 participants, including 30 nl-TLE patients (13 left, 17 right) and 15 healthy controls. Diffusion tensor tractography was computed to model the WM structural network. The topologic properties of the WM network were obtained by graph theoretical analysis, and the between-group differences in global and nodal properties of the WM network were examined by network-based statistical analysis (NBS). The relationship between WM network properties and clinical parameters was assessed by Pearson's correlation analysis. NBS results indicated that patients with left and right nl-TLE experienced distinct changes of WM nodal and global network properties compared with HCs. Positive correlation coefficients were found in several regions. The structural disruptions of networks in the two nl-TLE groups were observed to be different in distribution and severity. This study provides evidence for changes of the WM network topological properties and structural connectivity in nl-TLE patients, which provide useful insights for the understanding of disease mechanisms of TLE and improving treatment outcomes for nl-TLE. • This study aims to investigate alterations of white matter (WM) network in patients with non-lesional temporal lobe epilepsy (nl-TLE). • Network-based statistical analysis results indicated that patients with left and right nl-TLE experienced distinct changes of WM nodal and global network properties compared with healthy controls. • This study provides useful insights for the understanding of disease mechanisms of TLE and improving treatment outcomes for nl-TLE.

Numerical upscaling of discrete network models

In this paper a numerical multiscale method for discrete networks is presented. The method gives an accurate coarse scale representation of the full network by solving sub-network problems. The method is used to solve problems with highly varying connectivity or random network structure, showing optimal order convergence rates with respect to the mesh size of the coarse representation. Moreover, a network model for paper-based materials is presented. The numerical multiscale method is applied to solve problems governed by the presented network model.

Social cognition in psychosis: Predictors and effects of META-cognitive training

Social cognition deficits are found in schizophrenia and bipolar disorder, but its neural underpinnings are poorly understood. Given the complexity of psychological functions underlying this kind of cognition, we hypothesized that alterations in global structural connectivity could contribute to those deficits. To test this hypothesis, we studied a group of schizophrenia and bipolar patients with connectomics based on diffusion magnetic resonance imaging and assessments of general and social cognition. The latter was assessed using the Mayer, Salovey and Caruso Emotional Intelligence Test (MSCEIT) for emotional intelligence and the Spanish Group for Schizophrenia Treatment Optimization (Grupo Español para la OPtimización del Tratamiento de la Esquizofrenia, GEOPTE) test for behavioral aspects of social cognition. Graph theory applied to fractional anisotropy for the connections among cortical regions was used to obtain the small-world (SW) index of the structural connectivity network. In addition, we assessed the possibility of predicting the response of social cognition deficits to Meta-cognitive Training based on their possible underpinnings in a subgroup of patients. Patients showed lower scores in emotional intelligence and behavioral social cognition. MSCEIT scores were associated with SW index and working memory, and GEOPTE scores were related to verbal memory. Improvement in social cognition after Meta-cognitive Training was associated with lower scores of the social cognition in the baseline, according to the GEOPTE scale. Our findings support structural connectivity as one of the factors underlying emotional intelligence in schizophrenia, and the use of Meta-cognitive Training to improve social cognition in patients with larger deficits.

Habitual tea drinking modulates brain efficiency: evidence from brain connectivity evaluation.

The majority of tea studies have relied on neuropsychological measures, and much fewer on neuroimaging measures, especially for interregional connections. To date, there has been no exploration of the effect of tea on system-level brain networks. We recruited healthy older participants to two groups according to their history of tea drinking frequency and investigated both functional and structural networks to reveal the role of tea drinking on brain organization. The results showed that tea drinking gave rise to the more efficient structural organization, but had no significant beneficial effect on the global functional organization. The suppression of hemispheric asymmetry in the structural connectivity network was observed as a result of tea drinking. We did not observe any significant effects of tea drinking on the hemispheric asymmetry of the functional connectivity network. In addition, functional connectivity strength within the default mode network (DMN) was greater for the tea-drinking group, and coexistence of increasing and decreasing connective strengths was observed in the structural connectivity of the DMN. Our study offers the first evidence of the positive contribution of tea drinking to brain structure and suggests a protective effect on age-related decline in brain organisation.

Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease.

Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at k-levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.

Functional and transcriptional connectivity of communities in breast cancer co-expression networks

Transcriptional co-expression networks represent the concerted gene regulation programs by means of statistical inference of co-expression patterns. The rich phenomenology of transcriptional processes behind complex phenotypes such as cancer, is often captured (at least partially) in the connectivity structure of transcriptional co-expression networks. By analyzing the community structure of these networks, we may develop a deeper understanding of that phenomenology. We identified the modular structure of a transcriptional co-expression network obtained from breast cancer gene expression as well as a non-cancer adjacent breast tissue network as a control. We then analyzed the biological functions associated to the resulting communities by means of enrichment analysis. We also generated two projected networks for both, tumor and control networks: The first one is a projection to a network in which nodes are communities and edges represent topologically adjacent communities, indicating co-expression patterns between them. For the second projection, a bipartite network was generated containing a layer of modules and a layer of biological processes, with links between modules and the functions in which they are enriched; from this bipartite network, a projection to the community layer was obtained. From the analysis of the communities and projections, we were able to discern distinctive patterns of regulation between tumors and controls. Even though the connectivity structure of transcriptional co-expression networks is quite different, the topology of the projected networks is somehow similar, indicating functional compartmentalization, in both tumor and control conditions. However, the biological functions represented in the corresponding modules resulted notably different, with the tumor network comprising functional modules enriched for well-known hallmarks of cancer.

Characterizing the role of the structural connectome in seizure dynamics.

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.