There is growing interest in the complex topology of human brain functional networks, often measured using resting-state functional MRI (fMRI). Here, we used a meta-analysis of the large primary literature that used fMRI or PET to measure task-related activation (>1,600 studies; 1985–2010). We estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions. This continuous coactivation matrix was used to build a weighted graph to characterize network topology. The coactivation network was modular, with occipital, central, and default-mode modules predominantly coactivated by specific cognitive domains (perception, action, and emotion, respectively). It also included a rich club of hub nodes, located in parietal and prefrontal cortex and often connected over long distances, which were coactivated by a diverse range of experimental tasks. Investigating the topological role of edges between a deactivated and an activated node, we found that such competitive interactions were most frequent between nodes in different modules or between an activated rich-club node and a deactivated peripheral node. Many aspects of the coactivation network were convergent with a connectivity network derived from resting state fMRI data ( n = 27, healthy volunteers); although the connectivity network was more parsimoniously connected and differed in the anatomical locations of some hubs. We conclude that the community structure of human brain networks is relevant to cognitive function. Deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.
Amphibian chytridiomycosis is a disease caused by the fungus Batrachochytrium dendrobatidis (Bd). Whether Bd is a new emerging pathogen (the novel pathogen hypothesis; NPH) or whether environmental changes are exacerbating the host-pathogen dynamic (the endemic pathogen hypothesis; EPH) is debated. To disentangle these hypotheses we map the distribution of Bd and chytridiomycosis across the Iberian Peninsula centred on the first European outbreak site. We find that the infection-free state is the norm across both sample sites and individuals. To analyse this dataset, we use Bayesian zero-inflated binomial models to test whether environmental variables can account for heterogeneity in both the presence and prevalence of Bd, and heterogeneity in the occurrence of the disease, chytridiomycosis. We also search for signatures of Bd-spread within Iberia using genotyping. We show (1) no evidence for any relationship between the presence of Bd and environmental variables, (2) a weak relationship between environmental variables and the conditional prevalence of infection, (3) stage-dependent heterogeneity in the infection risk, (4) a strong association between altitude and chytridiomycosis, (5) multiple Iberian genotypes and (6) recent introduction and spread of a single genotype of Bd in the Pyrenees. We conclude that the NPH is consistent with the emergence of Bd in Iberia. However, epizootic forcing of infection is tied to location and shaped by both biotic and abiotic variables. Therefore, the population-level consequences of disease introduction are explained by EPH-like processes. This study demonstrates the power of combining surveillance and molecular data to ascertain the drivers of new emerging infections diseases.
A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.
set of signals (usually time series) at each of a collection of pixels (in two dimensions) or voxels (in three dimensions). Building from such data, various forms of higher-level data representations are employed in neuroimaging. Traditionally, two-and three-dimensional images have, naturally, been the norm, but increasingly in recent years there has emerged a substantial interest in network-based representations.1.1. Motivation. Let G = (V, E) denote a graph, based on d = |V | vertices. In this setting, the vertices v ∈ V correspond to regions of interest (ROIs) in the brain, often pre-defined through considerations of the underlying neurobiology (e.g., the putamen or the cuneus). Edges {u, v} ∈ E between vertices u and v are used to denote a measure of association between the corresponding ROIs. Depending on the imaging modality used, the notion of 'association' may vary. For example, in diffusion tensor imaging (DTI), associations are taken to be representative of structural connectivity between brain regions. On the other hand, in functional magnetic resonance imaging (fMRI), associations are instead thought to represent functional connectivity, in the sense that the two regions of the brain participate together in the achievement of some higher-order function, often in the context of performing some task (e.g., counting from 1 to 10).With neuroimaging now a standard tool in clinical neuroscience, and with the advent of several major neuroscience research initiatives -perhaps most prominent being the recently announced Brain Research Accelerated by Innovative Neurotechnologies (BRAIN) initiative -we are quickly moving towards a time in which we will have available databases composed of large collections of secondary data in the form of network-based data objects. Faced with databases in which networks are a fundamental unit of data, it will be necessary to have in place the statistical tools to answer such questions as, "What is the 'average' of a collection of networks?" and "Do these networks differ, on average, from a given nominal network?," as well as "Do two collections of networks differ on average?" and "What factors (e.g., age, gender, etc.) appear to contribute to differences in networks?", or finally, say, "Has there been a change in the networks for a given subpopulation from yesterday to today?" In order to answer these and similar questions, we require network-based analogues of classical tools for statistical estimation and hypothesis testing.While these classical tools are among the most fundamental and ubiquitous in use in practice, their extension to network-based datasets, however, is not immediate and, in fact, can be expected to be highly non-trivial. The main challenge in such an extension is due to the simple fact that networks 2 are not Euclidean objects (for which classical methods were developed)rather, they are combinatorial objects, defined simply through their sets of vertices and edges. Nevertheless, our work here in this paper demonstrates that networks can be associated with ce...
The normal myelination of neuronal axons is essential to neurodevelopment, allowing fast inter-neuronal communication. The most dynamic period of myelination occurs in the first few years of life, in concert with a dramatic increase in cognitive abilities. How these processes relate, however, is still unclear. Here we aimed to use a data-driven technique to parcellate developing white matter into regions with consistent white matter growth trajectories and investigate how these regions related to cognitive development. In a large sample of 183 children aged 3 months to 4 years, we calculated whole brain myelin volume fraction (VFM) maps using quantitative multicomponent relaxometry. We used spatial independent component analysis (ICA) to blindly segment these quantitative VFM images into anatomically meaningful parcels with distinct developmental trajectories. We further investigated the relationship of these trajectories with standardized cognitive scores in the same children. The resulting components represented a mix of unilateral and bilateral white matter regions (e.g., cortico-spinal tract, genu and splenium of the corpus callosum, white matter underlying the inferior frontal gyrus) as well as structured noise (misregistration, image artifact). The trajectories of these regions were associated with individual differences in cognitive abilities. Specifically, components in white matter underlying frontal and temporal cortices showed significant relationships to expressive and receptive language abilities. Many of these relationships had a significant interaction with age, with VFM becoming more strongly associated with language skills with age. These data provide evidence for a changing coupling between developing myelin and cognitive development. Hum Brain Mapp 35:4475–4487, 2014.
Neuroimaging studies of brain anatomy in autism spectrum disorder (ASD) have mostly been based on measures of cortical volume (CV). However, CV is a product of 2 distinct parameters, cortical thickness (CT) and surface area (SA), that in turn have distinct genetic and developmental origins.Objective: To investigate regional differences in CV, SA, and CT as well as their relationship in a large and well-characterized sample of men with ASD and matched controls.Design: Multicenter case-control design using quantitative magnetic resonance imaging.
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