Facial emotion processing in schizophrenia : a non-specific neuropsychological deficit?

Original article can be found at : http://journals.cambridge.org/ Copyright Cambridge University PressBackground: Identification of facial emotions has been found to be impaired in schizophrenia but there are uncertainties about the neuropsychological specificity of the finding. Method: Twenty-two patients with schizophrenia and 20 healthy controls were given tests requiring identification of facial emotion, judgement of the intensity of emotional expressions without identification, familiar face recognition and the Benton Facial Recognition Test (BFRT). The schizophrenia patients were selected to be relatively intellectually preserved. Results: The patients with schizophrenia showed no deficit in identifying facial emotion, although they were slower than the controls. They were, however, impaired on judging the intensity of emotional expression without identification. They showed impairment in recognizing familiar faces but not on the BFRT. Conclusions: When steps are taken to reduce the effects of general intellectual impairment, there is no deficit in identifying facial emotions in schizophrenia. There may, however, be a deficit in judging emotional intensity. The impairment found in naming familiar faces is consistent with other evidence of semantic memory impairment in the disorder.Peer reviewe

A Network Neuroscience Approach to Typical and Atypical Brain Development.

Human brain networks based on neuroimaging data have already proven useful in characterizing both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here, we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders that first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorder. In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder, and we conclude the review with some suggestions for future directions

Complex networks: new trends for the analysis of brain connectivity

Today, the human brain can be studied as a whole. Electroencephalography, magnetoencephalography, or functional magnetic resonance imaging techniques provide functional connectivity patterns between different brain areas, and during different pathological and cognitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently manage local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy changing neural demands.Comment: Tutorial paper to appear in the Int. J. Bif. Chao

Salinity Tolerance of Selected Ectomycorrhizal Fungi (Pisolithus tinctorius Pers.) and Ectomycorrhizal Eucalypts

Increasing soil salinity has become a major problem worldwide. It has led to a reduction in the amount of arable land, has put at risk the supply of freshwater and threatens the existence of many natural habitats. The major increase in salinity has been attributed to human activities such as clearing of natural vegetation and large-scale irrigation programmes. The alleviation of this problem has focussed on changed management strategies. the most significant of which is the re-establishment of deep rooted plants in sail affected areas. This, however, is difficult because of the variation in salt tolerance of such plants and the problems created through nutrient deficiencies characteristic of such sites. This study investigated the role of ectomycorrhizal (ECM) associations in assisting eucalypts tolerate soil salt. The response of specific isolates of P. tinctorius Pers. To salinity in vitro was used to determine which may be the most effective when transferrred to saline soils. All isolates tested appeared to be at least tolerant or semi-tolerant to 150 mM NaCI. However, the different isolates produced different patterns of colony growth, making assessment of growth rates, and therefore salt tolerance, difficult. Inoculation of E. camaldulensis Dehnh. and E. diversicolor F. Muell. with spores of field collected Scleroderma species and Pisolithus tinctorius improved salt tolerance of E. camaldulensis but not E. diversicolor. Inoculation of E. diversicolor and E. camaldulensis seedlings and clones with P. tinctorius isolates used in in vitro studies, showed no significant growth response to salinity. This may be attributed to poor development of ECM structures within the root zone of these plants. Root and shoot proline content showed significant responses to both inoculation with ECM Fungi and salt treatment. These results did vary between experiments. Further research into the use of ECM to alleviate the problem of soil salinity is justified by this study. The development of new, and improvement of current techniques is discussed in light of these findings

A generative network model of neurodevelopmental diversity in structural brain organization.

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition

A unifying framework for measuring weighted rich clubs.

Network analysis can help uncover meaningful regularities in the organization of complex systems. Among these, rich clubs are a functionally important property of a variety of social, technological and biological networks. Rich clubs emerge when nodes that are somehow prominent or 'rich' (e.g., highly connected) interact preferentially with one another. The identification of rich clubs is non-trivial, especially in weighted networks, and to this end multiple distinct metrics have been proposed. Here we describe a unifying framework for detecting rich clubs which intuitively generalizes various metrics into a single integrated method. This generalization rests upon the explicit incorporation of randomized control networks into the measurement process. We apply this framework to real-life examples, and show that, depending on the selection of randomized controls, different kinds of rich-club structures can be detected, such as topological and weighted rich clubs.J.A. is supported by the NIH-Oxford-Cambridge Scholarship Program. P.P. is employed by Queen Mary University of London. M.R. is supported by the NARSAD Young Investigator and Isaac Newton Trust grants. E.T.B. is employed half-time by the University of Cambridge, UK, and half-time by GlaxoSmithKline (GSK). P.E.V. is supported by the Medical Research Council (grant number MR/K020706/1).This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/srep0725

The discovery of population differences in network community structure: New methods and applications to brain functional networks in schizophrenia

The modular organization of the brain network can vary in two fundamental ways. The amount of interversus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science

Topological Isomorphisms of Human Brain and Financial Market Networks

Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets

Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available lifespan study. We demonstrate changes in rich-club membership with age alongside a shift in importance from 'peripheral' seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span