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Which network structures favor the rapid spread of new ideas, behaviors, or technologies? This question has been studied extensively using epidemic models. Here we consider a complementary point of view and consider scenarios where the individuals' behavior is the result of a strategic choice among competing alternatives. In particular, we study models that are based on the dynamics of coordination games. Classical results in game theory studying this model provide a simple condition for a new action or innovation to become widespread in the network. The present paper characterizes the rate of convergence as a function of the structure of the interaction network. The resulting predictions differ strongly from the ones provided by epidemic models. In particular, it appears that innovation spreads much more slowly on well-connected network structures dominated by long-range links than in low-dimensional ones dominated, for example, by geographic proximity.

We consider the online stochastic matching problem proposed by Feldman et al. [Feldman J, Mehta A, Mirrokni VS, Muthukrishnan S (2009) Online stochastic matching: Beating 1 − 1/ e . Annual IEEE Sympos. Foundations Comput. Sci. 117-126] as a model of display ad allocation. We are given a bipartite graph; one side of the graph corresponds to a fixed set of bins, and the other side represents the set of possible ball types. At each time step, a ball is sampled independently from the given distribution and it needs to be matched upon its arrival to an empty bin. The goal is to maximize the number of allocations. We present an online algorithm for this problem with a competitive ratio of 0.702. Before our result, algorithms with a competitive ratio better than 1 − 1/ e were known under the assumption that the expected number of arriving balls of each type is integral. A key idea of the algorithm is to collect statistics about the decisions of the optimum offline solution using Monte Carlo sampling and use those statistics to guide the decisions of the online algorithm. We also show that our algorithm achieves a competitive ratio of 0.705 when the rates are integral. On the hardness side, we prove that no online algorithm can have a competitive ratio better than 0.823 under the known distribution model (and henceforth under the permutation model). This improves upon the 5/6 hardness result proved by Goel and Mehta [Goel G, Mehta A (2008) Online budgeted matching in random input models with applications to adwords. ACM-SIAM Symposium Discrete Algorithms 982-991] for the permutation model.

In this paper, we give the first approximation algorithm for the problem of max-min fair allocation of indivisible goods. An instance of this problem consists of a set of k people and m indivisible goods. Each person has a known linear utility function over the set of goods which might be different from the utility functions of other people. The goal is to distribute the goods among the people and maximize the minimum utility received by them. The approximation ratio of our algorithm is ... As a crucial part of our algorithm, we design and analyze an iterative method for rounding a fractional matching on a tree which might be of independent interest. We also provide better bounds when we are allowed to exclude a small fraction of the people from the problem.

The human isocortex consists of tangentially organized layers with unique cytoarchitectural properties. These layers show spatial variations in thickness and cytoarchitecture across the neocortex, which is thought to support function through enabling targeted corticocortical connections. Here, leveraging maps of the 6 cortical layers based on 3D human brain histology, we aimed to quantitatively characterize the systematic covariation of laminar structure in the cortex and its functional consequences. After correcting for the effect of cortical curvature, we identified a spatial pattern of changes in laminar thickness covariance from lateral frontal to posterior occipital regions, which differentiated the dominance of infra- versus supragranular layer thickness. Corresponding to the laminar regularities of cortical connections along cortical hierarchy, the infragranular-dominant pattern of laminar thickness was associated with higher hierarchical positions of regions, mapped based on resting-state effective connectivity in humans and tract-tracing of structural connections in macaques. Moreover, we show that regions with similar laminar thickness patterns have a higher likelihood of structural connections and strength of functional connections. In sum, here we characterize the organization of laminar thickness in the human isocortex and its association with cortico-cortical connectivity, illustrating how laminar organization may provide a foundational principle of cortical function.

Determining sex-bias in brain structure is of great societal interest to improve diagnostics and treatment of brain-related disorders. So far, studies on sex-bias in brain structure predominantly focus on macro-scale measures, and often ignore factors determining this bias. Here we study sex-bias in cortical and hippocampal microstructure in relation to sex hormones. Investigating quantitative intracortical profiling in-vivo using the T1w/T2w ratio in 1093 healthy females and males of the cross-sectional Human Connectome Project young adult sample, we find that regional cortical and hippocampal microstructure differs between males and females and that the effect size of this sex-bias varies depending on self-reported hormonal status in females. Microstructural sex-bias and expression of sex hormone genes, based on an independent post-mortem sample, are spatially coupled. Lastly, sex-bias is most pronounced in paralimbic areas, with low laminar complexity, which are predicted to be most plastic based on their cytoarchitectural properties. Albeit correlative, our study underscores the importance of incorporating sex hormone variables into the investigation of brain structure and plasticity. Here, the authors demonstrate that cortical microstructure in young adults shows marked sex bias, which is most pronounced in paralimbic areas. The effects are put into context with variations in sex hormones and local cytoarchitecture.

We give an explicit construction of the weak local limit of a class of preferential attachment graphs. This limit contains all local information and allows several computations that are otherwise hard, for example, joint degree distributions and, more generally, the limiting distribution of subgraphs in balls of any given radius k around a random vertex in the preferential attachment graph. We also establish the finite-volume corrections which give the approach to the limit.

Background and Aims. Visceral adiposity index (VAI) is a novel marker of fat distribution and function which incorporates both anthropometric and laboratory measures. Recently, several studies have suggested VAI as a screening tool for metabolic syndrome (MetS). Here, we aimed to consolidate the results of these studies by performing a systematic review and meta-analysis. Methods and Results. We searched PubMed and EMBASE online databases for eligible studies that investigated the association of VAI and MetS. After reviewing 294 records, we included 33 eligible papers with a sum of 20516 MetS and 53242 healthy participants. The risk of bias in the included studies was assessed, and the relevant data was extracted. All included studies reported a significant association between VAI and MetS screening, but were highly heterogeneous in their reported effects. We pooled the diagnostic test accuracy metrics of VAI for MetS screening and showed that it has a moderate-to-high accuracy with an area under the summary receiver operating characteristics curve of 0.847, a pooled sensitivity of 78%, and a pooled specificity of 79%. Besides, we pooled the difference in means of VAI between patients with MetS and healthy controls, revealing that VAI was 2.15 units higher in MetS patients. Conclusions. VAI is an accurate, low-cost, and widely available screening marker for MetS. However, further studies are needed to evaluate its applicability in clinical practice, determine an optimal cut-off, and identify populations that would benefit the most from it.

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans. The neurobiology of human brain development and aging is hard to study in vivo. The authors report on distinct spatial associations between brain morphology and cellular as well as molecular brain properties throughout neurodevelopment and aging.

The human cerebral cortex shows hemispheric asymmetry, yet the microstructural basis of this asymmetry remains incompletely understood. Here, we probe layer-specific microstructural asymmetry using one post-mortem male brain. Overall, anterior and posterior regions show leftward and rightward asymmetry respectively, but this pattern varies across cortical layers. A similar anterior-posterior pattern is observed using in vivo Human Connectome Project ( N  = 1101) T1w/T2w microstructural data, with average cortical asymmetry showing the strongest similarity with post-mortem -based asymmetry of layer III. Moreover, microstructural asymmetry is found to be heritable, varies as a function of age and sex, and corresponds to intrinsic functional asymmetry. We also observe a differential association of language and markers of mental health with microstructural asymmetry patterns at the individual level, illustrating a functional divergence between inferior-superior and anterior-posterior microstructural axes, possibly anchored in development. Last, we could show concordant evidence with alternative in vivo microstructural measures: magnetization transfer ( N  = 286) and quantitative T1 ( N  = 50). Together, our study highlights microstructural asymmetry in the human cortex and its functional and behavioral relevance. The human cortex displays an anterior-to-posterior asymmetry, identified via both post-mortem and in vivo microstructural measurements. Microstructural asymmetry is heritable, varies across cortical layers and between sexes, and relates to functional asymmetry and behavior.

Several neuroimaging studies have investigated localized aberrations in brain structure, function or connectivity in late-life depression, but the ensuing results are equivocal and often conflicting. Here, we provide a quantitative consolidation of neuroimaging in late-life depression using coordinate-based meta-analysis by searching multiple databases up to March 2020. Our search revealed 3252 unique records, among which we identified 32 eligible whole-brain neuroimaging publications comparing 674 patients with 568 controls. The peak coordinates of group comparisons between the patients and the controls were extracted and then analyzed using activation likelihood estimation method. Our sufficiently powered analysis on all the experiments, and more homogenous subsections of the data (patients > controls, controls > patients, and functional imaging experiments) revealed no significant convergent regional abnormality in late-life depression. This inconsistency might be due to clinical and biological heterogeneity of LLD, as well as experimental (e.g., choice of tasks, image modalities) and analytic flexibility (e.g., preprocessing and analytic parameters), and distributed patterns of neural abnormalities. Our findings highlight the importance of clinical/biological heterogeneity of late-life depression, in addition to the need for more reproducible research by using pre-registered and standardized protocols on more homogenous populations to identify potential consistent brain abnormalities in late-life depression.