Perspective: network-guided pattern formation of neural dynamics

The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain''s network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings and lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatio-temporal pattern formation and propose a novel perspective for analysing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.     

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.     

An edge-centric perspective on the human connectome: link communities in the brain

Brain function depends on efficient processing and integration of information within a complex network of neural interactions, known as the connectome. An important aspect of connectome architecture is the existence of community structure, providing an anatomical basis for the occurrence of functional specialization. Typically, communities are defined as groups of densely connected network nodes, representing clusters of brain regions. Looking at the connectome from a different perspective, instead focusing on the interconnecting links or edges, we find that the white matter pathways between brain regions also exhibit community structure. Eleven link communities were identified: five spanning through the midline fissure, three through the left hemisphere and three through the right hemisphere. We show that these link communities are consistently identifiable and investigate the network characteristics of their underlying white matter pathways. Furthermore, examination of the relationship between link communities and brain regions revealed that the majority of brain regions participate in multiple link communities. In particular, the highly connected and central hub regions showed a rich level of community participation, supporting the notion that these hubs play a pivotal role as confluence zones in which neural information from different domains merges.     

Network medicine analysis of chondrocyte proteins towards new treatments of osteoarthritis

Osteoarthritis (OA) is a progressive disorder with high incidence in the ageing human population that still has no treatment currently. This disorder induces the breakdown of articular cartilage, leading to the exposure and damage of bone surfaces. For a global understanding of OA development, the systematic integration of known OA-related proteins with protein–protein interaction (PPI) networks is required. In this work, the OA-related interactome was reconstructed using multiple data sources to have the most up-to-date information on OA-related proteins and their interactions. We then combined emergent concepts in network medicine to detect new unclassified OA-related proteins. The mapping of known OA-related proteins with PPI networks showed that these proteins are locally connected to each other and agglomerated in a large component. To expand this module, we applied a diffusion-based algorithm that probabilistically induces more searches in the vicinity of the seed OA-related proteins. As a result, the 10 topmost ranked proteins were connected to the OA disease module, supporting the local hypothesis. We computed structural modules and selected those that had the highest enrichment of OA-related proteins. The identified molecules show a link between structural topology and disease dysfunctionality. Interestingly, the protein {"type":"entrez-protein","attrs":{"text":"Q6EEV6","term_id":"81175019","term_text":"Q6EEV6"}}Q6EEV6 was highlighted for OA association by both methods, reinforcing the potential involvement of this protein. These results suggest that similar disease-connected modules may exist in different human disorders, which could lead to systematic identification of genes or proteins that have a joint role in specific disease phenotypes.     

利用信息化手段的医院后勤综合调度平台“一体化”建设

医院后勤建设是医院整体工作的重要一环,但后勤本身发展往往与医院整体发展速度不同步。从改变后勤服务理念的角度入手,利用信息化的手段,建立符合当前后勤管理需要的“一体化”调度平台是一条新思路。通过一系列的流程再造和干预,实现报修和申请的全过程质量控制是解决目前后勤服务效率较低、服务能力不足、多头管理、圆圈现象、绩效考核与实际脱离等问题的有效途径。     

A genetic interaction network model of a complex neurological disease

Absence epilepsy (AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike‐wave discharges (SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use combined analysis of pleiotropy and epistasis (CAPE) to detect and interpret genetic interactions in a meta‐population derived from three C3H × B6J strain crosses, each of which is fixed for a different SWD‐causing mutation. Although each mutation causes SWD through a different molecular mechanism, the phenotypes caused by each mutation are exacerbated on the C3H genetic background compared with B6J, suggesting common modifiers. By combining information across two phenotypic measures – SWD duration and frequency – CAPE showed a large, directed genetic network consisting of suppressive and enhancing interactions between loci on 10 chromosomes. These results illustrate the power of CAPE in identifying novel modifier loci and interactions in a complex neurological disease, toward a more comprehensive view of its underlying genetic architecture.     

Modelling land use changes for landscape connectivity: The role of plantation forestry and highways

Landscape connectivity is a key aspect for the maintenance of biodiversity and ecosystem viability. Nowadays, the competition between economic development and nature conservation is intense. In most territories natural vegetation is being replaced by exotic tree plantations, which have a better performance in terms of timber productivity but often, a lower ecological value. We evaluated potential natural forest connectivity improvement in the Cantabria region (Northern Spain) through two main actions: protection of environmentally valuable forest areas, and reforestation with indigenous species of those patches of exotic plantation trees with a particularly important role for the connectivity of the forest network. We established a variety of scenarios to calculate least cost paths, considering the presence or absence of plantation forestry and highways to examine connectivity. Then, we applied two habitat availability indices (integral index of connectivity and probability of connectivity) attending to different dispersal distances. Our analyses show a great potential for improving connectivity using plantation forests in the natural forest network, and a dramatic impact of the highway in the north–south connectivity of the study area. Based on these results, we identified those patches of plantation forest and natural forest that are more important for the maintenance of overall landscape connectivity, and propose their protection or conversion through reforestation. The final proposed network constitutes a larger and better connected natural forested landscape than the existing one.