High-Efficiency Graph Measures in Discriminating Schizophrenia Patients from Healthy Controls Using Structural and Functional Connectivity

Network science has become an invaluable tool for neuroimaging analysis and has advanced our understanding of the brain's complex structural and functional topology. Network science, an interdisciplinary offshoot of graph theory, conceives of the brain as a system of nodes, representing brain regions or voxels, and edges, representing the structural or functional connection between these regions or voxels. Such a model is considered appealing in neuroscience as it describes a system with various interacting regions that produces complex behaviors. This conception of the brain has revealed that the brain exhibits “small-world” properties, namely that brain networks are systems with regional specialization and efficient global information transfer. More importantly, studies in brain networks have furthered our understanding of diseases and disorders affecting the brain. Assessing group differences in representative samples of brain networks has become increasingly important, yet appropriate methodological developments have remained relatively sparse. Additionally, there is increased interest in understanding dynamic changes, or how a network changes over time. Currently, most network analysis tools are designed to analyze a static representation, thus changes that occur over time are often overlooked or ignored. As the field continues to grow, it is becoming increasingly important to develop methods for understanding the complexity of these data. This research topic features selected contributions that explores group analysis and dynamics in brain network studies including methodological and practical applications employing various imaging modalities. This snapshot of the field represents the growing interest to expand and validate new methods for use in the field of brain network neuroscience. “Defining nodes in complex brain networks” is a review article discussing the methodological choices for defining nodes, particularly how the choice of atlas-based or voxel-based methods affects the computed network (Stanley et al., 2013). “Routing in the brain” is a commentary on network routing focusing on how current methods and models may not be adequate for brain networks. Further exploration of the packet switching network model may be more realistic and appropriate for the brain (Graham, 2014). “A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity” is a methods article introducing a Bayesian model for estimating functional networks, from functional magnetic resonance imaging (fMRI) data, leveraging complementary structural diffusion tensor imaging (DTI) data (Xue et al., 2015). “A permutation testing framework to compare groups of brain networks” is a methods article describing a permutation testing framework to statistically compare groups of functional brain networks while incorporating topological features inherent in each individual network (Simpson et al., 2013). “Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses” is a methods article describing a statistical approach for computing uncertainty in static and dynamic functional networks and aggregating network measures in task-related electrocorticography (ECoG) data (Stephen et al., 2014). “Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity” is a methods article introducing a generalization of the vector auto-regressive (VAR) model for comparing effective connectivity between experimental conditions while accounting for between-subject heterogeneity (Gorrostieta et al., 2013). “Statistical network analysis for functional MRI: summary networks and group comparisons” is a review article discussing the construction of summary networks and how to test for topological differences in groups of networks when these groups also exhibit significant differences in density (Ginestet et al., 2014). “Detecting functional connectivity change points for single-subject fMRI data” is a methods article that extends Dynamic Connectivity Regression (DCR), a data-driven technique which detects change points in functional connectivity between brain regions to determine state changes over the course of an experimental task, by introducing a novel algorithm aimed at increasing the estimation of networks for individual subject data (Cribben et al., 2013). “The Laplacian spectrum of neural networks” is an original research article examining the use of the Laplacian spectrum in anatomic networks of the macaque, cat and Caenorhabditis elegans. This method describes network structure at a systems level, assessing systemic infrastructural properties of the entire network as opposed to properties of specific nodes or connections (De Lange et al., 2014). “Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro” is an original research article on network dynamics in multi-electrode array data focusing on changes in the functional connectivity of evoked gamma oscillations in cortical circuits (Carmeli et al., 2013).

BackgroundPatients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) show functional and structural connectivity alterations in the default mode network (DMN) while cerebrovascular disease (CeVD) shows functional and structural connectivity changes in the executive control network (ECN). Such disruptions are associated with memory and executive function impairment, respectively. Concurrent AD and CeVD pathology is associated with a higher rate of cognitive decline and differential neurodegenerative patterns. Together, such findings are likely reflective of different underlying pathology in AD with and without CeVD. However, few studies have examined the effect of CeVD on network functional connectivity (task-free functional magnetic resonance imaging (fMRI)) and structural connectivity (diffusion MRI) of the DMN and ECN in aMCI and AD using a hypothesis-driven multiple seed-based approach.MethodsWe examined functional and structural connectivity network changes in 39 aMCI, 50 aMCI+CeVD, 47 AD, 47 AD+CeVD, and 65 healthy controls (HCs) and their associations with cognitive impairment in the executive/attention and memory domains.ResultsWe demonstrate divergent DMN and ECN functional connectivity changes in CeVD and non-CeVD subjects. Compared with controls, intra-DMN hippocampal functional connectivity reductions were observed in both AD and AD+CeVD, while intra-DMN parietal and medial prefrontal-parietal functional connectivity was higher in AD+CeVD and aMCI+CeVD, but lower in AD. Intra-ECN frontal functional connectivity increases and fronto-parietal functional connectivity decreases occurred in CeVD but not non-CeVD subjects. Such functional connectivity alterations were related with cognitive impairment in a dissociative manner: intra-DMN functional connectivity changes were associated with worse cognition primarily in non-CeVD groups, while intra-ECN functional connectivity changes were associated with worse cognition primarily in CeVD groups. Additionally, CeVD and non-CeVD groups showed overlapping and distinct alterations in inter-network DMN-ECN functional connectivity depending on disease severity. In contrast to functional connectivity, CeVD groups had greater network structural connectivity damage compared with non-CeVD groups in both aMCI and AD patients. Network structural connectivity damage was associated with worse cognition.ConclusionsWe demonstrate differential functional and structural network changes between aMCI and AD patients with and without CeVD through diverging and deleterious network-based degeneration underlying domain-specific cognitive impairment.

It has been widely reported that structural and functional connectivities are disturbed in cortical networks in schizophrenia (SZ). However, much less is known about the structural and functional connectivities between cortical and subcortical regions in SZ. Here, diffusion tensor imaging (DTI) data was used to identify consistent cortico-subcortical structural connection patterns across SZ patients and controls, and thus 13 common cortical Regions of Interest (ROIs) were determined. DTI and resting state fMRI (R-fMRI) datasets were used to assess the structural and functional connectivities between the 13 cortical ROIs and 12 subcortical regions in 8 SZ patients and 10 normal controls. It was found that there are significantly increased functional connectivities for 7 cortico-subcortical connections between the 13 cortical ROIs and 12 subcortical regions. Among most of these connections, the functional connectivity strength was doubled in SZ in comparison to controls. The cortical ROIs with functional hyper-connectivities to subcortical regions are localized in frontal and parietal lobes. However, no significant difference in the structural connectivity between these cortical and subcortical regions was found between SZ and controls. Additional analysis results showed 4 significantly increased and 2 significantly decreased cortico-cortical connections. Our study results suggest the functional hyper-connectivity between cortical and subcortical regions, adding further evidence to literature findings that SZ is a disorder of connectivity between components of large-scale brain networks. The result of either increased or decreased functional connectivities among cortical ROIs exhibits the complex pattern of disturbance of brain networks in SZ.

Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multifaceted data to study the human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different brain-related measures interact. In this study, we analyze the multi-block HCP data using data integration via analysis of subspaces (DIVAS). We integrate structural and functional brain connectivity, substance use, cognition, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity, outside of brain connectivity itself. Nearly 14% of the variation in functional connectivity (FC) and roughly 12% of the variation in structural connectivity (SC) is attributed to shared spaces with genetics. Moreover, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. For example, in the (FC, SC, and substance use) subspace, these novel hypothesis tests highlight largely negative functional and structural connections suggesting the brain's role in physiological responses to increased substance use. Our findings are validated on genetically relevant subjects not studied in the main analysis.

The relationship between age-related changes in brain structural connectivity (SC) and functional connectivity (FC) with cognition is not well understood. Furthermore, it is not clear whether cognition is represented via a similar spatial pattern of FC and SC or instead is mapped by distinct sets of distributed connectivity patterns. To this end, we used a longitudinal, within-subject, multimodal approach aiming to combine brain data from diffusion-weighted MRI (DW-MRI), and functional MRI (fMRI) with behavioral evaluation, to better understand how changes in FC and SC correlate with changes in cognition in a sample of older adults. FC and SC measures were derived from the multimodal scans acquired at two time points. Change in FC and SC was correlated with 13 behavioral measures of cognitive function using Partial Least Squares Correlation (PLSC). Two of the measures indicate an age-related change in cognition and the rest indicate baseline cognitive performance. FC and SC—cognition correlations were expressed across several cognitive measures, and numerous structural and functional cortical connections, mainly cingulo-opercular, dorsolateral prefrontal, somatosensory and motor, and temporo-parieto-occipital, contributed both positively and negatively to the brain-behavior relationship. Whole-brain FC and SC captured distinct and independent connections related to the cognitive measures. Overall, we examined age-related function-structure associations of the brain in a comprehensive and integrated manner, using a multimodal approach. We pointed out the behavioral relevance of age-related changes in FC and SC. Taken together, our results highlight that the heterogeneity in distributed FC and SC connectivity patterns provide unique information about the variable nature of healthy cognitive aging.

Extensive efforts are devoted to understand the functional (FC) and structural connections (SC) of the brain. FC is usually measured by functional magnetic resonance imaging (fMRI), and conceptualized as degree of synchronicity in brain activity between different regions. SC is typically indexed by measures of white matter (WM) properties, e.g. by diffusion weighted imaging (DWI). FC and SC are intrinsically related, in that coordination of activity across regions ultimately depends on fast and efficient transfer of information made possible by structural connections. Convergence between FC and SC has been shown for specific networks, especially the default mode network (DMN). However, it is not known to what degree FC is constrained by major WM tracts and whether FC and SC change together over time. Here, 120 participants (20-85 years) were tested at two time points, separated by 3.3 years. Resting-state fMRI was used to measure FC, and DWI to measure WM microstructure as an index of SC. TRACULA, part of FreeSurfer, was used for automated tractography of 18 major WM tracts. Cortical regions with tight structural couplings defined by tractography were only weakly related at the functional level. Certain regions of the DMN showed a modest relationship between change in FC and SC, but for the most part, the two measures changed independently. The main conclusions are that anatomical alignment of SC and FC seems restricted to specific networks and tracts, and that changes in SC and FC are not necessarily strongly correlated.

Functional and structural connectivity substrates of cognitive performance in relapsing remitting multiple sclerosis with mild disability.

Developmental malformations (dysgenesis) of the corpus callosum lead to neurological conditions with a broad range of clinical presentations. Investigating the altered brain connectivity patterns is crucial to understanding both adaptive and maladaptive neuroplasticity in corpus callosum dysgenesis patients. Here, we acquired structural diffusion-weighted and resting-state functional MRI data from a cohort of 11 corpus callosum dysgenesis patients (five with agenesis and six with hypoplasia) and compared their structural and functional connectivity patterns to healthy subjects selected from the Human Connectome Project. We found that these patients have fewer structural inter- and intra-hemispheric brain connections relative to healthy controls. Interestingly, the patients with callosal agenesis have a scant number of inter-hemispheric connections but manage to maintain the full integrity of functional connectivity between the same cortical regions as the healthy subjects. On the other hand, the hypoplasic group presented abnormal structural and functional connectivity patterns relative to healthy controls while maintaining the same total amount of functional connections. These results demonstrate that acallosal patients can compensate for having fewer structural brain connections and present functional adaptation. However, hypoplasics present atypical structural connections to different brain regions, leading to entirely new and abnormal functional brain connectivity patterns.

The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure–function issue, treating a system of Jansen–Rit neural mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project. Via direct simulations we determine the similarity of functional (inferred from correlated activity between nodes) and structural connectivity matrices under variation of the parameters controlling single-node dynamics, highlighting a nontrivial structure–function relationship in regimes that support limit cycle oscillations. To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the so-called ‘false bifurcations’ (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset. We highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, and structure only weakly influences FC. Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted via linear stability analysis. This study thereby emphasises the substantial role that local dynamics can have in shaping large-scale functional brain states.

The aim of this study was to investigate the differences in structural connectivity based on diffusion tensor imaging (DTI) and functional connectivity based on arterial spin labeling (ASL) MRI between temporal lobe epilepsy (TLE) patients with and without hippocampal sclerosis (HS). We enrolled 50 patients with TLE, including 25 patients with HS and 25 patients without HS, who underwent brain MRI, including DTI and ASL. We calculated the network parameters of structural connectivity based on DTI and functional connectivity based on ASL using a graph theoretical analysis. The parameters included global network measures (radius, diameter, characteristic path length, global efficiency, local efficiency, mean clustering coefficient, transitivity, assortative coefficient, and small-worldness index) and a local network measure (betweenness centrality). The global and local network measures of structural connectivity were not different between TLE patients with and without HS. However, significant differences in functional connectivity existed between the two groups. The radius and diameter of the global network measures in the TLE patients with HS were significantly increased compared with those without HS (4.140 vs. 3.140, p = 0.045; 6.812 vs. 5.132, p = 0.049; respectively). No differences were detected between other global network measures of functional connectivity and local network measure. Significant differences in global network measures of functional connectivity based on ASL existed between TLE patients with and without HS. These findings suggest that TLE patients with HS exhibit a more disconnected functional brain network than those without HS.

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

Functional connectivity is scaffolded by the structural connections of the brain. Disruptions of either structural or functional connectivity can lead to deficits in cognitive functions and increase the risk for neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). To date, very little research has examined the association between structural and functional connectivity in typical development, while no studies have attempted to understand the development of structure-function coupling in children with ADHD. 175 individuals (84 typically developing children and 91 children with ADHD) participated in a longitudinal neuroimaging study with up to three waves. In total, we collected 278 observations between the ages 9 and 14 (139 each in typically developing controls and ADHD). Regional measures of structure-function coupling were calculated at each timepoint using Spearman’s rank correlation and mixed effect models were used to determine group differences and longitudinal changes in coupling over time. In typically developing children, we observed increases in structure-function coupling strength across multiple higher-order cognitive and sensory regions. Overall, weaker coupling was observed in children with ADHD, mainly in the prefrontal cortex, superior temporal gyrus, and inferior parietal cortex. Further, children with ADHD showed an increased rate of coupling strength predominantly in the inferior frontal gyrus, superior parietal cortex, precuneus, mid-cingulate, and visual cortex, compared to no corresponding change over time in typically developing controls. This study provides evidence of the joint maturation of structural and functional brain connections in typical development across late childhood to mid-adolescence, particularly in regions that support cognitive maturation. Findings also suggest that children with ADHD exhibit different patterns of structure-function coupling, suggesting atypical patterns of coordinated white matter and functional connectivity development predominantly in the regions overlapping with the default mode network, salience network, and dorsal attention network during late childhood to mid-adolescence.

The human entorhinal-hippocampal circuitry is characterized by an information-specific functional organization where two routes, that are preferentially connected to the parahippocampal cortex or the perirhinal and retrosplenial cortices, divide the entorhinal cortex as well as hippocampal subiculum and CA1 subregions.

Synathesia is a condition in which stimulation of a sensory modality triggers another sensation in the alike or an unalike sensory modality. Currently, synaesthesia is deemed a neurological condition that engages unwanted transfer of signals between brain regions from one sense to another “crosstalk activation”. The probability that undiagnosed synaesthesia may impact the results of structural magnetic resonance imaging (MRI), Diffusion Tensor imaging (DTI), functional magnetic resonance imaging (fMRI) and resting state connectivity studies is high, given the multiple anatomical and functional connections within the brain. In this paper, the currently available literature to mark which sensations adjured by synaesthesia and how could this impact MRI different modalities. Our study found that synaesthesia can have an opaque impact on fMRI studies of sensory, memory and cognitive functions, and there is testimony to suggest structural connections in the brain are also mutated DTI measurements especially, it shows enhanced structural connectivity for synesthetes between brain regions, higher Fractional anisotropy (FA), as well as increased in the white matter integrity between some regions.. Given the low dispersal of synaesthesia, the likelihood of synaesthesia being a perplexing factor in DTI, fMRI studies of patient groups is small; however, determining the existence of synaesthesia is paramount for investigating individual patients especially Shizoherenia, and autistic patients.

IntroductionThe deterioration of white matter pathways is one of the hallmarks of the ageing brain. In theory, this decrease in structural integrity leads to disconnection between regions of brain networks and thus to altered functional connectivity and a decrease in cognitive abilities. However, in many studies, associations between structural and functional connectivity are rather weak or not observed at all. System segregation, defined as the extent of partitioning between different resting state networks has increasingly gained attention in recent years as a new metric for functional changes in the aging brain. Yet there is a shortage of previous reports describing the association of structural integrity and functional segregation.MethodsTherefore, we used a large a large sample of 2,657 participants from the Hamburg City Health Study, a prospective population-based study including participants aged 46–78 years from the metropolitan region Hamburg, Germany. We reconstructed structural and functional connectomes to analyze whether there is an association between age-related differences in structural connectivity and functional segregation, and whether this association is stronger than between structural connectivity and functional connectivity. In a second step, we investigated the relationship between functional segregation and executive cognitive function and tested whether this association is stronger than that between functional connectivity and executive cognitive function.ResultsWe found a significant age-independent association between decreasing structural connectivity and decreasing functional segregation across the brain. In addition, decreasing functional segregation showed an association with decreasing executive cognitive function. On the contrary, no such association was observed between functional connectivity and structural connectivity or executive function.DiscussionThese results indicate that the segregation metric is a more sensitive biomarker of cognitive ageing than functional connectivity at the global level and offers a unique and more complementary network-based explanation.