Navigation of brain networks

Understanding the mechanisms of neural communication in large-scale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45%-60% reductions in navigation performance. Specifically, we found that brain networks cannot be progressively rewired (randomized or clusterized) to result in topologies with significantly improved navigation performance. Navigation was also found to: i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks; and, ii) explain significant variation in functional connectivity. Unlike prevalently studied communication strategies in connectomics, navigation does not mandate biologically unrealistic assumptions about global knowledge of network topology. We conclude that the wiring and spatial embedding of brain networks is conducive to effective decentralized communication. Graph-theoretic studies of the connectome should consider measures of network efficiency and centrality that are consistent with decentralized models of neural communication

Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously -- elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala, and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow time scales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding "feeder" cortical regions show unstable, rapidly fluctuating dynamics likely crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.Comment: 35 pages, 6 figure

Estimating the impact of structural directionality: How reliable are undirected connectomes?

Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Due to limitations of non-invasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (i) remove uni-directional connections, (ii) add reciprocal connections, and (iii) combine equal numbers of removed and added uni-directional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.Comment: 29 pages, 6 figures, 9 supplementary figures, 4 supplementary table

Automatic laser shutdown implications for all optical data networks

Generalized multiprotocol label switching (GMPLS), optical packet, and burst-switched networks in which the synchronous digital hierarchy/synchronous optical network (SDH/SONET) layer is removed may be rendered nonfunctional because the current standard for triggering Automatic Power Reduction (APR) cannot distinguish between a fiber that has been de-energized and a fiber failure. If this standard is applied, without modification, the likelihood of unnecessary amplifier shutdown in optical networks is significant. These shutdown events may impact large regions of the network and render optical links inoperable. To avoid unnecessary amplifier shutdown, amendments to the current operation of APR are suggested

Network Scaling Effects in Graph Analytic Studies of Human Resting-State fMRI Data

Graph analysis has become an increasingly popular tool for characterizing topological properties of brain connectivity networks. Within this approach, the brain is modeled as a graph comprising N nodes connected by M edges. In functional magnetic resonance imaging (fMRI) studies, the nodes typically represent brain regions and the edges some measure of interaction between them. These nodes are commonly defined using a variety of regional parcellation templates, which can vary both in the volume sampled by each region, and the number of regions parcellated. Here, we sought to investigate how such variations in parcellation templates affect key graph analytic measures of functional brain organization using resting-state fMRI in 30 healthy volunteers. Seven different parcellation resolutions (84, 91, 230, 438, 890, 1314, and 4320 regions) were investigated. We found that gross inferences regarding network topology, such as whether the brain is small-world or scale-free, were robust to the template used, but that both absolute values of, and individual differences in, specific parameters such as path length, clustering, small-worldness, and degree distribution descriptors varied considerably across the resolutions studied. These findings underscore the need to consider the effect that a specific parcellation approach has on graph analytic findings in human fMRI studies, and indicate that results obtained using different templates may not be directly comparable

Altered functional brain connectivity in a non-clinical sample of young adults with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is characterized by symptoms of inattention and hyperactivity/impulsivity that often persist in adulthood. There is a growing consensus that ADHD is associated with abnormal function of diffuse brain networks, but such alterations remain poorly characterized. Using resting-state functional magnetic resonance imaging, we characterized multivariate (complex network measures), bivariate (network-based statistic), and univariate (regional homogeneity) properties of brain networks in a non-clinical, drug-naive sample of high-functioning young men and women with ADHD (nine males, seven females) and a group of matched healthy controls. Data from our sample allowed the isolation of intrinsic functional connectivity alterations specific to ADHD diagnosis and symptoms that are not related to developmental delays, general cognitive dysfunction, or history of medication use. Multivariate results suggested that frontal, temporal, and occipital cortices were abnormally connected locally as well as with the rest of the brain in individuals with ADHD. Results from the network-based statistic support and extend multivariate results by isolating two brain networks comprising regions between which inter-regional connectivity was significantly altered in the ADHD group; namely, a frontal amygdala-occipital network and a frontal temporal-occipital network. Brain behavior correlations further highlighted the key role of altered orbitofrontal-temporal and frontal-amygdala connectivity for symptoms of inattention and hyperactivity/impulsivity. All univariate properties were similar between groups. Taken together, results from this study show that the diagnosis and the two main symptom dimensions of ADHD are related to altered intrinsic connectivity in orbitofrontal-temporal-occipital and fronto-amygdala-occipital networks. Accordingly, our findings highlight the importance of extending the conceptualization of ADHD beyond segregated fronto-striatal alterations

Packet delay in optical circuit-switched networks

Abstract-A framework is provided for evaluation of packet delay distribution in an optical circuit-switched network. The framework is based on a fluid traffic model, packet queueing at edge routers, and circuit-switched transmission between edge routers. Packets are assigned to buffers according to their destination, delay constraint, physical route and wavelength. At every decision epoch, a subset of buffers is allocated to end-to-end circuits for transmission, where circuit holding times are based on limited and exhaustive circuit allocation policies. To ensure computational tractability, the framework approximates the evolution of each buffer independently. "Slack variables" are introduced to decouple amongst buffers in a way that the evolution of each buffer remains consistent with all other buffers in the network. The delay distribution is derived for a single buffer and an approximation is given for a network of buffers. The approximation entails finding a fixed point for the functional relation between the "slack variables" and a specific circuit allocation policy. An analysis of a specific policy, in which circuits are probabilistically allocated based on buffer size, is given as an illustrative example. The framework is shown to be in good agreement with a discrete event simulation model. Index Terms-Circuit switching, fixed point approximation, packet delay, WDM network

Genetic and epigenetic regulation in Lingo-1 : Effects on cognitive function and white matter microstructure in a case-control study for schizophrenia

Leucine-rich repeat and immunoglobulin domain-containing protein (Lingo-1) plays a vital role in a large number of neuronal processes underlying learning and memory, which are known to be disrupted in schizophrenia. However, Lingo-1 has never been examined in the context of schizophrenia. The genetic association of a single-nucleotide polymorphism (SNP, rs3144) and methylation (CpG sites) in the Lingo-1 3′-UTR region was examined, with the testing of cognitive dysfunction and white matter (WM) integrity in a schizophrenia case-control cohort (n = 268/group). A large subset of subjects (97 control and 161 schizophrenia subjects) underwent structural magnetic resonance imaging (MRI) brain scans to assess WM integrity. Frequency of the rs3144 minor allele was overrepresented in the schizophrenia population (p = 0.03), with an odds ratio of 1.39 (95% CI 1.016–1.901). CpG sites surrounding rs3144 were hypermethylated in the control population (p = 0.032) compared to the schizophrenia group. rs3144 genotype was predictive of membership to a subclass of schizophrenia subjects with generalized cognitive deficits (p < 0.05), in addition to having associations with WM integrity (p = 0.018). This is the first study reporting a potential implication of genetic and epigenetic risk factors in Lingo-1 in schizophrenia. Both of these genetic and epigenetic alterations may also have associations with cognitive dysfunction and WM integrity in the context of the schizophrenia pathophysiology