A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks.
ABSTRACT: The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is a central focus in brain network science. It is an open question, however, how strongly the SC-FC relationship depends on specific topological features of brain networks or the models used for describing excitable dynamics. Using a basic model of discrete excitable units that follow a susceptible - excited - refractory dynamic cycle (SER model), we here analyze how functional connectivity is shaped by the topological features of a neural network, in particular its modularity. We compared the results obtained by the SER model with corresponding simulations by another well established dynamic mechanism, the Fitzhugh-Nagumo model, in order to explore general features of the SC-FC relationship. We showed that apparent discrepancies between the results produced by the two models can be resolved by adjusting the time window of integration of co-activations from which the FC is derived, providing a clearer distinction between co-activations and sequential activations. Thus, network modularity appears as an important factor shaping the FC-SC relationship across different dynamic models.
Project description:The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is of central importance in brain network science. It is an open question, however, how the SC-FC relationship depends on specific topological features of brain networks or the models used for describing neural dynamics. Using a basic but general model of discrete excitable units that follow a susceptible-excited-refractory activity cycle (SER model), we here analyze how the network activity patterns underlying functional connectivity are shaped by the characteristic topological features of the network. We develop an analytical framework for describing the contribution of essential topological elements, such as common inputs and pacemakers, to the coactivation of nodes, and demonstrate the validity of the approach by comparison of the analytical predictions with numerical simulations of various exemplar networks. The present analytic framework may serve as an initial step for the mechanistic understanding of the contributions of brain network topology to brain dynamics.
Project description:Modularity is a ubiquitous topological feature of structural brain networks at various scales. Although a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a purely topological perspective. Our topological reinforcement model acts enhancing the topological overlap between nodes, that is, iteratively allowing connections between non-neighbor nodes with high neighborhood similarity. This rule reliably evolves synthetic random networks toward a modular architecture. Such final modular structure reflects initial "proto-modules," thus allowing to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle might be biologically implemented as a Hebbian rule. Concretely, we explore a simple model of excitable dynamics, where the plasticity rule acts based on the functional connectivity (co-activations) between nodes. Results produced by the activity-based model are consistent with the ones from the purely topological rule in terms of the final network configuration and modules composition. Our findings suggest that the selective reinforcement of topological overlap may be a fundamental mechanism contributing to modularity emergence in brain networks.
Project description:The objective was to assess dynamic functional connectivity (FC) and local/global connectivity in Parkinson's disease (PD) patients with mild cognitive impairment (PD-MCI) and with normal cognition (PD-NC). The sample included 35 PD patients and 26 healthy controls (HC). Cognitive assessment followed an extensive neuropsychological battery. For resting-state functional MRI (rs-fMRI) analysis, independent component analysis (ICA) was performed and components were located in 7 networks: Subcortical (SC), Auditory (AUD), Somatomotor (SM), visual (VI), cognitive-control (CC), default-mode (DMN), and cerebellar (CB). Dynamic FC analysis was performed using the GIFT toolbox. FC differences between groups in each FC state were analysed with the network-based statistic (NBS) approach. Finally, a graph-theoretical analysis for local/global parameters was performed. The whole sample showed 2 dynamic FC states during the rs-fMRI. PD-MCI patients showed decreased mean dwell time in the hypo-connectivity state (<i>p</i> = 0.030) and showed increased number of state transitions (<i>p</i> = 0.007) compared with the HC. In addition, in the hypo-connectivity state, PD-MCI patients showed reduced inter-network FC between the SM-CC, SM-VI, SM-AUD, CC-VI and SC-DMN compared with the HC (<i>p</i> < 0.05-FDR). These FC alterations in PD-MCI were accompanied by graph-topological alterations in nodes located in the SM network (<i>p</i> < 0.001). In contrast, no differences were found between the PD-NC and HC. Findings suggest the presence of dynamic functional brain deteriorations in PD-MCI that are not present in PD-NC, showing the PD-MCI group dynamic FC dysfunctions, reduced FC mostly between SM-CC networks and graph-topological deteriorations in the SM network. A dynamic FC approach could be helpful to understand cognitive deterioration in PD.
Project description:Although anomalies in the topological architecture of whole-brain connectivity have been found to be associated with Alzheimer's disease (AD), our understanding about the progression of AD in a functional connectivity (FC) perspective is still rudimentary and few study has explored the function-structure relations in brain networks of AD patients. By using resting-state functional MRI (fMRI), this study firstly investigated organizational alternations in FC networks in 12 AD patients, 15 amnestic mild cognitive impairment (aMCI) patients, and 14 age-matched healthy aging subjects and found that all three groups exhibit economical small-world network properties. Nonetheless, we found a decline of the optimal architecture in the progression of AD, represented by a more localized modular organization with less efficient local information transfer. Our results also show that aMCI forms a boundary between normal aging and AD and represents a functional continuum between healthy aging and the earliest signs of dementia. Moreover, we revealed a dissociated relationship between the overall FC and structural connectivity (SC) in AD patients. In this study, diffusion tensor imaging tractography was used to map the structural network of the same individuals. The decreased FC-SC coupling may be indicative of more stringent and less dynamic brain function in AD patients. Our findings provided insightful implications for understanding the pathophysiological mechanisms of brain dysfunctions in aMCI and AD patients and demonstrated that functional disorders can be characterized by multimodal neuroimaging-based metrics.
Project description:In childhood frontal lobe epilepsy (FLE), cognitive impairment and educational underachievement are serious, well-known co-morbidities. The broad scale of affected cognitive domains suggests wide-spread network disturbances that not only involves, but also extends beyond the frontal lobe. In this study we have investigated whole brain connectional properties of children with FLE in relation to their cognitive impairment and compared them with healthy controls. Functional connectivity (FC) of the networks was derived from dynamic fluctuations of resting state fMRI and structural connectivity (SC) was obtained from fiber tractograms of diffusion weighted MRI. The whole brain network was characterized with graph theoretical metrics and decomposed into modules. Subsequently, the graph metrics and the connectivity within and between modules were related to cognitive performance. Functional network disturbances in FLE were related to increased clustering, increased path length, and stronger modularity compared to healthy controls, which was accompanied by stronger within- and weaker between-module functional connectivity. Although structural path length and clustering appeared normal in children with FLE, structural modularity increased with stronger cognitive impairment. It is concluded that decreased coupling between large-scale functional network modules is a hallmark for impaired cognition in childhood FLE.
Project description: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 <i>k</i>-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.
Project description:How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses.
Project description:Recent imaging connectomics studies have demonstrated that the spontaneous human brain functional networks derived from resting-state functional MRI (R-fMRI) include many non-trivial topological properties, such as highly efficient small-world architecture and densely connected hub regions. However, very little is known about dynamic functional connectivity (D-FC) patterns of spontaneous human brain networks during rest and about how these spontaneous brain dynamics are constrained by the underlying structural connectivity. Here, we combined sub-second multiband R-fMRI data with graph-theoretical approaches to comprehensively investigate the dynamic characteristics of the topological organization of human whole-brain functional networks, and then employed diffusion imaging data in the same participants to further explore the associated structural substrates. At the connection level, we found that human whole-brain D-FC patterns spontaneously fluctuated over time, while homotopic D-FC exhibited high connectivity strength and low temporal variability. At the network level, dynamic functional networks exhibited time-varying but evident small-world and assortativity architecture, with several regions (e.g., insula, sensorimotor cortex and medial prefrontal cortex) emerging as functionally persistent hubs (i.e., highly connected regions) while possessing large temporal variability in their degree centrality. Finally, the temporal characteristics (i.e., strength and variability) of the connectional and nodal properties of the dynamic brain networks were significantly associated with their structural counterparts. Collectively, we demonstrate the economical, efficient, and flexible characteristics of dynamic functional coordination in large-scale human brain networks during rest, and highlight their relationship with underlying structural connectivity, which deepens our understandings of spontaneous brain network dynamics in humans.