Property:Abstract
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Hubs are network components that hold positions of high importance for network function. Previous research has identified hubs in human brain networks derived from neuroimaging data; however, there is little consensus on the localization of such hubs. Moreover, direct evidence regarding the role of various proposed hubs in network function (e.g., cognition) is scarce. Regions of the default mode network (DMN) have been frequently identified as "cortical hubs" of brain networks. On theoretical grounds, we have argued against some of the methods used to identify these hubs and have advocated alternative approaches that identify different regions of cortex as hubs. Our framework predicts that our proposed hub locations may play influential roles in multiple aspects of cognition, and, in contrast, that hubs identified via other methods (including salient regions in the DMN) might not exert such broad influence. Here we used a neuropsychological approach to directly test these predictions by studying long-term cognitive and behavioral outcomes in 30 patients, 19 with focal lesions to six "target" hubs identified by our approaches (high system density and participation coefficient) and 11 with focal lesions to two "control" hubs (high degree centrality). In support of our predictions, we found that damage to target locations produced severe and widespread cognitive deficits, whereas damage to control locations produced more circumscribed deficits. These findings support our interpretation of how neuroimaging-derived network measures relate to cognition and augment classic neuroanatomically based predictions about cognitive and behavioral outcomes after focal brain injury. +
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In 1943, a Greek war veteran named Stamatis Moraitis came to the United States for treatment of a combat-mangled arm. He’d survived a gunshot wound, escaped to Turkey and eventually talked his way onto the Queen Elizabeth, then serving as a troopship, to cross the Atlantic. Moraitis settled in Port Jefferson, N.Y., an enclave of countrymen from his native island, Ikaria. He quickly landed a job doing manual labor. Later, he moved to Boynton Beach, Fla. Along the way, Moraitis married a Greek-American woman, had three children and bought a three-bedroom house and a 1951 Chevrolet.\r\n\r\nOne day in 1976, Moraitis felt short of breath. Climbing stairs was a chore; he had to quit working midday. After X-rays, his doctor concluded that Moraitis had lung cancer. As he recalls, nine other doctors confirmed the diagnosis. They gave him nine months to live. He was in his mid-60s.\r\n\r\nMoraitis considered staying in America and seeking aggressive cancer treatment at a local hospital. That way, he could also be close to his adult children. But he decided instead to return to Ikaria, where he could be buried with his ancestors in a cemetery shaded by oak trees that overlooked the Aegean Sea. He figured a funeral in the United States would cost thousands, a traditional Ikarian one only $200, leaving more of his retirement savings for his wife, Elpiniki. Moraitis and Elpiniki moved in with his elderly parents, into a tiny, whitewashed house on two acres of stepped vineyards near Evdilos, on the north side of Ikaria. At first, he spent his days in bed, as his mother and wife tended to him. He reconnected with his faith. On Sunday mornings, he hobbled up the hill to a tiny Greek Orthodox chapel where his grandfather once served as a priest. When his childhood friends discovered that he had moved back, they started showing up every afternoon. They’d talk for hours, an activity that invariably involved a bottle or two of locally produced wine. I might as well die happy, he thought.\r\n\r\nIn the ensuing months, something strange happened. He says he started to feel stronger. One day, feeling ambitious, he planted some vegetables in the garden. He didn’t expect to live to harvest them, but he enjoyed being in the sunshine, breathing the ocean air. Elpiniki could enjoy the fresh vegetables after he was gone.\r\n\r\nSix months came and went. Moraitis didn’t die. Instead, he reaped his garden and, feeling emboldened, cleaned up the family vineyard as well. Easing himself into the island routine, he woke up when he felt like it, worked in the vineyards until midafternoon, made himself lunch and then took a long nap. In the evenings, he often walked to the local tavern, where he played dominoes past midnight. The years passed. His health continued to improve. He added a couple of rooms to his parents’ home so his children could visit. He built up the vineyard until it produced 400 gallons of wine a year. Today, three and a half decades later, he’s 97 years old — according to an official document he disputes; he says he’s 102 — and cancer-free. He never went through chemotherapy, took drugs or sought therapy of any sort. All he did was move home to Ikaria.
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In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network's dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data. +
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In the ocean, organic particles harbour diverse bacterial communities, which collectively digest and recycle essential nutrients. Traits like motility and exo-enzyme production allow individual taxa to colonize and exploit particle resources, but it remains unclear how community dynamics emerge from these individual traits. Here we track the taxon and trait dynamics of bacteria attached to model marine particles and demonstrate that particle-attached communities undergo rapid, reproducible successions driven by ecological interactions. Motile, particle-degrading taxa are selected for during early successional stages. However, this selective pressure is later relaxed when secondary consumers invade, which are unable to use the particle resource but, instead, rely on carbon from primary degraders. This creates a trophic chain that shifts community metabolism away from the particle substrate. These results suggest that primary successions may shape particle-attached bacterial communities in the ocean and that rapid community-wide metabolic shifts could limit rates of marine particle degradation. +
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Brain disorders? Not really: Why network structures block reductionism in psychopathology research +
In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion of real patterns , and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders. +
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A Major Role of the Macrophage in Quantitative Genetic Regulation of Immunoresponsiveness and Antiinfectious Immunity +
It is clear today that the immune system is constituted by a coordinated network of perfectly integrated and interacting cells and molecules subject to strict cooperation to ensure the highest possible efficiency in antiinfectious immunity. A simplified scheme of the immune system in higher vertebrates is represented in this chapter. The enzyme equipment of macrophage phagosomes endows these cells with bactericidal or bacteriostatic activity on ingested microorganisms, therefore, constituting the first important mechanism in antiinfectious defense. The metabolic activity of macrophages on engulfed antigens also regulates the specific response of T and B lymphocytes through a complex process of antigen handling and antigen presentation, establishing a sort of symbiotic relationship between lymphocytes and macrophages. There are two essential components in the immune response: one is specific and the other nonspecific. The specific response involves the stereospecific selective recognition. The nonspecific aspect of the immune response includes the handling of the phagocytized antigen and the rate at which the process of multiplication and differentiation of small lymphocytes takes place. The protective effect of specific vaccination is essentially based on immunological memory. The antibody molecules, according to their isotypes, play specialized defensive roles against various types of invading microorganisms, particularly in collaboration with the complement system, inducing bactericidal or opsonizing effects. © 1984, Academic Press, Inc. All rights reserved. +
Laboratory experiments show us that the deleterious character of accumulated novel age-specific mutations is reduced and made less variable with increased age. While theories of aging predict that the frequency of deleterious mutations at mutation-selection equilibrium will increase with the mutation's age of effect, they do not account for these age-related changes in the distribution of de novo mutational effects. Furthermore, no model predicts why this dependence of mutational effects upon age exists. Because the nature of mutational distributions plays a critical role in shaping patterns of senescence, we need to develop aging theory that explains and incorporates these effects. Here we propose a model that explains the age dependency of mutational effects by extending Fisher's geometrical model of adaptation to include a temporal dimension. Using a combination of simple analytical arguments and simulations, we show that our model predicts age-specific mutational distributions that are consistent with observations from mutation-accumulation experiments. Simulations show us that these age-specific mutational effects may generate patterns of senescence at mutation-selection equilibrium that are consistent with observed demographic patterns that are otherwise difficult to explain. +
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research
and application. The purpose of this review is to provide an introduction to the core
concepts and tools of machine learning in a manner easily understood and intuitive
to physicists. The review begins by covering fundamental concepts in ML and modern
statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization
before moving on to more advanced topics in both supervised and unsupervised learning.
Topics covered in the review include ensemble models, deep learning and neural networks,
clustering and data visualization, energy-based models (including MaxEnt models and
Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize
the many natural connections between ML and statistical physics. A notable aspect of
the review is the use of Jupyter notebooks to introduce modern ML/statistical packages
to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations
of supersymmetric decays of proton-proton collisions). We conclude with an extended
outlook discussing possible uses of machine learning for furthering our understanding
of the physical world as well as open problems in ML where physicists maybe able to
contribute. +
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Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often co-circulate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, as the changes in population structure associated with the recent introduction of partial-coverage vaccines have significantly reduced pneumococcal disease. Here we show pneumococcal lineages from multiple populations each have a distinct combination of intermediate frequency genes. Functional analysis suggested these loci were likely subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts, or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: +
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Microbes assemble into complex, dynamic, and species-rich communities that play critical roles in human health and in the environment. The complexity of natural environments and the large number of niches present in most habitats are often invoked to explain the maintenance of microbial diversity in the presence of competitive exclusion. Here we show that soil and plant-associated microbiota, cultivated ex situ in minimal synthetic environments with a single supplied source of carbon, universally re-assemble into large and dynamically stable communities with strikingly predictable coarse-grained taxonomic and functional compositions. We find that generic, non-specific metabolic cross-feeding leads to the assembly of dense facilitation networks that enable the coexistence of multiple competitors for the supplied carbon source. The inclusion of universal and non-specific cross-feeding in ecological consumer-resource models is sufficient to explain our observations, and predicts a simple determinism in community structure, a property reflected in our experiments. +
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Models relating phenotype space to fitness (phenotype-fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher's geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model "from first principles" is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher's model's assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts. +
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Niche partitioning due to adaptive foraging reverses effects of nestedness and connectance on pollination network stability +
Much research debates whether properties of ecological networks such as nestedness and con- nectance stabilise biological communities while ignoring key behavioural aspects of organisms within these networks. Here, we computationally assess how adaptive foraging (AF) behaviour interacts with network architecture to determine the stability of plant–pollinator networks. We find that AF reverses negative effects of nestedness and positive effects of connectance on the sta- bility of the networks by partitioning the niches among species within guilds. This behaviour enables generalist pollinators to preferentially forage on the most specialised of their plant part- ners which increases the pollination services to specialist plants and cedes the resources of general- ist plants to specialist pollinators. We corroborate these behavioural preferences with intensive field observations of bee foraging. Our results show that incorporating key organismal behaviours with well-known biological mechanisms such as consumer-resource interactions into the analysis of ecological networks may greatly improve our understanding of complex ecosystems. +
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Phenotypic variation is common in most pathogens, yet the mechanisms that maintain this diver- sity are still poorly understood. We asked whether continuous host variation in susceptibility helps maintain phenotypic variation, using experiments conducted with a baculovirus that infects gypsy moth (Lymantria dispar) larvae. We found that an empirically observed tradeoff between mean transmission rate and variation in transmission, which results from host heterogeneity, promotes long-term coexistence of two pathogen types in simulations of a population model. This tradeoff introduces an alternative strategy for the pathogen: a low-transmission, low-variability type can coexist with the high-transmission type favoured by classical non-heterogeneity models. In addi- tion, this tradeoff can help explain the extensive phenotypic variation we observed in field-col- lected pathogen isolates, in traits affecting virus fitness including transmission and environmental persistence. Similar heterogeneity tradeoffs might be a general mechanism promoting phenotypic variation in any pathogen for which hosts vary continuously in susceptibility. +
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Phytoplankton are key components of aquatic ecosystems, fixing CO2 from the atmosphere through photosynthesis and supporting secondary production, yet relatively little is known about how future global warming might alter their biodiversity and associated ecosystem functioning. Here, we explore how the structure, function, and biodiversity of a planktonic metacommunity was altered after five years of experimental warming. Our outdoor mesocosm experiment was open to natural dispersal from the regional species pool, allowing us to explore the effects of experimental warming in the context of metacommunity dynamics. Warming of 4°C led to a 67% increase in the species richness of the phytoplankton, more evenly-distributed abundance, and higher rates of gross primary productivity. Warming elevated productivity indirectly, by increasing the biodiversity and biomass of the local phytoplankton communities. Warming also systematically shifted the taxonomic and functional trait composition of the phytoplankton, favoring large, colonial, inedible phytoplankton taxa, suggesting stronger top-down control, mediated by zooplankton grazing played an important role. Overall, our findings suggest that temperature can modulate species coexistence, and through such mechanisms, global warming could, in some cases, increase the species richness and productivity of phytoplankton communities. +
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Pollination systems are recognized as critical for the maintenance of biodiversity in terrestrial ecosystems. Therefore, the understanding of mechanisms that promote the integrity of those mutualistic assemblages is an important issue for the conservation of biodiversity and ecosystem function. In this study we present a new population dynamics model for plant–pollinator interactions that is based on the consumer–resource approach and incorporates a few essential features of pollination ecology. The model was used to project the temporal dynamics of three empirical pollination network, in order to analyze how adaptive foraging of pollinators (AF) shapes the outcome of community dynamics in terms of biodiversity and network robustness to species loss. We found that the incorporation of AF into the dynamics of the pollination networks increased the persistence and diversity of its constituent species, and reduced secondary extinctions of both plants and animals. These findings were best explained by the following underlying processes: 1) AF increased the amount of floral resources extracted by specialist pollinators, and 2) AF raised the visitation rates received by specialist plants. We propose that the main mechanism by which AF enhanced those processes is (trophic) niche partitioning among animals, which in turn generates (pollen vector) niche partitioning among plants. Our results suggest that pollination networks can maintain their stability and diversity by the adaptive foraging of generalist pollinators. +
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Prediction of post-vaccine population structure of Streptococcus pneumoniae using accessory gene frequencies +
Predictions of how a population will respond to a selective pressure are valuable, especially in the case of infectious diseases, which often adapt to the interventions we use to control them. Yet attempts to predict how pathogen populations will change, for example in response to vaccines, are challenging. Such has been the case with Streptococcus pneumoniae , an important human colonizer and pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use recent advances in knowledge of negative-frequency dependent selection (NFDS) acting on frequencies of accessory genes (i.e., flexible genome) to predict the changes in the pneumococcal population after intervention. Implementing a deterministic NFDS model using the replicator equation, we can accurately predict which pneumococcal lineages will increase after intervention. Analyzing a population genomic sample of pneumococci collected before and after vaccination, we find that the predicted fitness of a lineage post-vaccine is significantly and positively correlated with the observed change in its prevalence. Then, using quadratic programming to numerically solve the frequencies of non-vaccine type lineages that best restored the pre-vaccine accessory gene frequencies, we accurately predict the post-vaccine population composition. Additionally, we also test the predictive ability of frequencies of core genome loci, a subset of metabolic loci, and naive estimates of prevalence change based on pre-vaccine lineages frequencies. Finally, we show how this approach can assess the migration and invasion capacity of emerging lineages, on the basis of their accessory genome. In general, we provide a method for predicting the impact of an intervention on pneumococcal populations and other bacterial pathogens for which NFDS is a main driving force. +
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Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters. +
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Recent results have shown that deep neural networks (DNN) may have significant potential to serve as quantitatively precise models of sensory cortex neural populations. However, the implications these results have for our conceptual understanding of neural mechanisms are subtle. This is because many modern DNN brain models are best understood as the products of task-constrained optimization processes, unlike the intuitively simpler hand-crafted models from earlier approaches. In this chapter, we illustrate these issues by first discussing the nature of information processing in the primate ventral visual pathway, and review results comparing the response properties of units in goal-optimized DNN models to neural responses found throughout the ventral pathway. We then show how DNN visual system models are just one instance of a more general optimization framework whose logic may be applicable to understanding the underlying constraints that shape neural mechanisms throughout the brain.
An important part of a scientist's job is to answer "why" questions. For cognitive neuroscientists, a core objective is to uncover the underlying reasons why the structures of the human brain are as they are. Since brains are biological systems, answering such questions is ultimately a matter of identifying the evolutionary and developmental constraints that shape brain structure and function. Such constraints are in part architectural: what large-scale brain structures are put in place genetically to help a brain help its host organism better meet evolutionary challenges? In light of the centrality of behavior in understanding the brain, an ethological investigation is also indicated: what behavioral goals most strongly constrain a given neural system? And since many complex behaviors in higher organisms are not entirely genetically determined and must instead be partly derived through experience of the world, a core question of learning is also involved: how do learning rules that absorb experiential data constrain what brains look like?
The interactions between architectural structure, behavioral goals, and learning rules suggest a quantitative optimization framework as one route toward answering these "why" questions. Put simply, this means: postulating one or several goal behavior(s) as driving the evolution and/or development of a neural system of interest; finding architecturally plausible computational models that (attempt to) optimize for the behavior; and then quantitatively comparing the internal structures arrived at in the optimized models to measurements
from large-scale neuroscience experiments. To the extent that there is a match between optimized models and the real data that is very substantially better than that found for various controls (e.g. models designed by hand or optimized for other tasks), this is evidence that something important has been understood about the underlying constraints that shape the brain system under investigation. Though it might sound challenging to
put this approach into practice, recent successes suggest we might add to our list of maxims the observation that nothing in computational cognitive neuroscience makes sense except in light of optimization.
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The maps problem and the mapping problem: Two challenges for a cognitive neuroscience of speech and language +
Research on the brain basis of speech and language faces theoretical and empirical challenges. Most current research, dominated by imaging, deficit-lesion, and electrophysiological techniques, seeks to identify regions that underpin aspects of language processing such as phonology, syntax, or semantics. The emphasis lies on localization and spatial characterization of function. The first part of the paper deals with a practical challenge that arises in the context of such a research programme. This maps problem concerns the extent to which spatial information and localization can satisfy the explanatory needs for perception and cognition. Several areas of investigation exemplify how the neural basis of speech and language is discussed in those terms (regions, streams, hemispheres, networks). The second part of the paper turns to a more troublesome challenge, namely how to formulate the formal links between neurobiology and cognition. This principled problem thus addresses the relation between the primitives of cognition (here speech, language) and neurobiology. Dealing with this mapping problem invites the development of linking hypotheses between the domains. The cognitive sciences provide granular, theoretically motivated claims about the structure of various domains (the "cognome"); neurobiology, similarly, provides a list of the available neural structures. However, explanatory connections will require crafting of computationally explicit linking hypotheses at the right level of abstraction. For both the practical maps problem and the principled mapping problem, developmental approaches and evidence can play a central role in the resolution. © 2012 Copyright Psychology Press. +
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Sleep is crucial for daytime functioning, cognitive performance and general well-being. These aspects of daily life are known to be impaired after extended wake, yet, the underlying neuronal correlates have been difficult to identify. Accumulating evidence suggests that normal functioning of the brain is characterized by long-range temporal correlations (LRTCs) in cortex, which are supportive for decision-making and working memory tasks. Here we assess LRTCs in resting state human EEG data during a 40-hour sleep deprivation experiment by evaluating the decay in autocorrelation and the scaling exponent of the detrended fluctuation analysis from EEG amplitude fluctuations. We find with both measures that LRTCs decline as sleep deprivation progresses. This decline becomes evident when taking changes in signal power into appropriate consideration. Our results demonstrate the importance of sleep to maintain LRTCs in the human brain. In complex networks, LRTCs naturally emerge in the vicinity of a critical state. The observation of declining LRTCs during wake thus provides additional support for our hypothesis that sleep reorganizes cortical networks towards critical dynamics for optimal functioning. +
