Explore the vast range of titles available.

The brain is increasingly studied with graph theoretical approaches, which can be used to characterize network topology. However, studies on brain networks have reported contradictory findings, and do not easily converge to a clear concept of the structural and functional network organization of the brain. It has recently been suggested that the minimum spanning tree (MST) may help to increase comparability between studies. The MST is an acyclic sub-network that connects all nodes and may solve several methodological limitations of previous work, such as sensitivity to alterations in connection strength (for weighted networks) or link density (for unweighted networks), which may occur concomitantly with alterations in network topology under empirical conditions. If analysis of MSTs avoids these methodological limitations, understanding the relationship between MST characteristics and conventional network measures is crucial for interpreting MST brain network studies. Here, we firstly demonstrated that the MST is insensitive to alterations in connection strength or link density. We then explored the behavior of MST and conventional network-characteristics for simulated regular and scale-free networks that were gradually rewired to random networks. Surprisingly, although most connections are discarded during construction of the MST, MST characteristics were equally sensitive to alterations in network topology as the conventional graph theoretical measures. The MST characteristics diameter and leaf fraction were very strongly related to changes in the characteristic path length when the network changed from a regular to a random configuration. Similarly, MST degree, diameter, and leaf fraction were very strongly related to the degree of scale-free networks that were rewired to random networks. Analysis of the MST is especially suitable for the comparison of brain networks, as it avoids methodological biases. Even though the MST does not utilize all the connections in the network, it still provides a, mathematically defined and unbiased, sub-network with characteristics that can provide similar information about network topology as conventional graph measures. •Conventional network analyses are accompanied with methodological limitations.•The minimum spanning tree is an acyclic sub-network that connects all nodes in the original network.•The minimum spanning tree avoids several methodological biases.•Minimum spanning tree metrics can be interpreted along the lines of conventional network analyses.

The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.

The relation between pathology and cognitive dysfunction in dementia is still poorly understood, although disturbed communication between different brain regions is almost certainly involved. In this study we combine magneto-encephalography (MEG) and network analysis to investigate the role of functional sub-networks (modules) in the brain with regard to cognitive failure in Alzheimer's disease. Whole-head resting-state (MEG) was performed in 18 Alzheimer patients (age 67±9, 6 females, MMSE 23±5) and 18 healthy controls (age 66±9, 11 females, MMSE 29±1). We constructed functional brain networks based on interregional synchronization measurements, and performed graph theoretical analysis with a focus on modular organization. The overall modular strength and the number of modules changed significantly in Alzheimer patients. The parietal cortex was the most highly connected network area, but showed the strongest intramodular losses. Nonetheless, weakening of intermodular connectivity was even more outspoken, and more strongly related to cognitive impairment. The results of this study demonstrate that particularly the loss of communication between different functional brain regions reflects cognitive decline in Alzheimer's disease. These findings imply the relevance of regarding dementia as a functional network disorder. ► Network analysis applied to MEG data to study functional sub-networks (modules). ► In Alzheimer's disease, altered modular organization relates to cognitive symptoms. ► Intermodular connectivity is damaged most, parietal region has highest local damage.

We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity of reasonably large groups of interacting excitatory and inhibitory neurons, were reciprocally and excitatory coupled using random rewiring as described by Watts and Strogatz. Numerical analysis of the network revealed an abrupt transition towards a synchronized state as a function of increasing coupling strength α. Synchronization increased with increasing degree and decreasing regularity of the network. Parameters of the functional network showed a diverse dependency on structural connectivity: normalized clustering coefficient γ and path length λ increased with increasing α. For sufficiently large α, however, γ decreased with increasing rewiring probability p, while λ increased. Hence, a structured functional network exists despite the randomness of the underlying structural network. That is, patterns of functional connectivity are influenced by patterns of the corresponding structural level but do not necessarily agree with those.

We set out to determine whether changes in resting-state cortico-cortical functional connectivity are a feature of early-stage Parkinson's disease (PD), explore how functional coupling might evolve over the course of the disease and establish its relationship with clinical deficits. Whole-head magnetoencephalography was performed in an eyes-closed resting-state condition in 70 PD patients with varying disease duration (including 18 recently diagnosed, drug-naive patients) in an “OFF” medication state and 21 controls. Neuropsychological testing was performed in all subjects. Data analysis involved calculation of three synchronization likelihood (SL, a general measure of linear and non-linear temporal correlations between time series) measures which reflect functional connectivity within (local) and between (intrahemispheric and interhemispheric) ten major cortical regions in five frequency bands. Recently diagnosed, drug-naive patients showed an overall increase in alpha1 SL relative to controls. Cross-sectional analysis in all patients revealed that disease duration was positively associated with alpha2 and beta SL measures, while severity of parkinsonism was positively associated with theta and beta SL measures. Moderately advanced patients had increases in theta, alpha1, alpha2 and beta SL, particularly with regard to local SL. In recently diagnosed patients, cognitive perseveration was associated with increased interhemispheric alpha1 SL. Increased resting-state cortico-cortical functional connectivity in the 8–10 Hz alpha range is a feature of PD from the earliest clinical stages onward. With disease progression, neighboring frequency bands become increasingly involved. These findings suggest that changes in functional coupling over the course of PD may be linked to the topographical progression of pathology over the brain.

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process. •We apply an epidemic spreading model on a human connectome.•Empirically observed opposite information flow patterns were replicated.•Posterior structural hubs play a dominant role in the network dynamics.•Global patterns are a result of the unequal spatial distribution of degrees.

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimer's disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimer's disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.

We introduce a directed phase lag index to investigate the spatial and temporal pattern of phase relations of oscillatory activity in a model of macroscopic structural and functional brain networks. Direction of information flow was determined with the directed phase lag index (dPLI) defined as the probability that the instantaneous phase of X was smaller than the phase of Y (modulo π). X was said to phase-lead Y if 0.5

Journal Article

Share this book

Add to My Shelf