Academic literature on the topic 'Network morphospace'

The structure of complex networks has attracted much attention in recent years. It has been noted that many real-world examples of networked systems share a set of common architectural features. This raises important questions about their origin, for example whether such network attributes reflect common design principles or constraints imposed by selectional forces that have shaped the evolution of network topology. Is it possible to place the many patterns and forms of complex networks into a common space that reveals their relations, and what are the main rules and driving forces that determine which positions in such a space are occupied by systems that have actually evolved? We suggest that these questions can be addressed by combining concepts from two currently relatively unconnected fields. One is theoretical morphology, which has conceptualized the relations between morphological traits defined by mathematical models of biological form. The second is network science, which provides numerous quantitative tools to measure and classify different patterns of local and global network architecture across disparate types of systems. Here, we explore a new theoretical concept that lies at the intersection between both fields, the ‘network morphospace’. Defined by axes that represent specific network traits, each point within such a space represents a location occupied by networks that share a set of common ‘morphological’ characteristics related to aspects of their connectivity. Mapping a network morphospace reveals the extent to which the space is filled by existing networks, thus allowing a distinction between actual and impossible designs and highlighting the generative potential of rules and constraints that pervade the evolution of complex systems.

We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs, and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC of the motif distribution is correlated with efficiency and inversely correlated with transitivity. Hence this axis approximately conforms to the well-known economical small-world trade-off between integration and segregation in brain networks. Finally, we show that the economical clustering generative model proposed by Vértes et al. ( 2012 ) can approximately reproduce the motif morphospace of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualize the relationships between network properties and to investigate generative or constraining factors in the formation of complex human brain functional networks.

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.

Abstract The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.

Lobe-fins transformed into limbs during the Devonian period, facilitating the water-to-land transition in tetrapods. We traced the evolution of well-articulated skeletons across the fins-to-limbs transition, using a network-based approach to quantify and compare topological features of fins and limbs. We show that the topological arrangement of bones in pectoral and pelvic appendages evolved in parallel during the fins-to-limbs transition, occupying overlapping regions of the morphospace, following a directional trend, and decreasing their disparity over time. We identify the presence of digits as the morphological novelty triggering topological changes that discriminated limbs from fins. The origin of digits caused an evolutionary shift toward appendages that were less densely and heterogeneously connected, but more assortative and modular. Disparity likewise decreased for both appendages, more markedly until a time concomitant with the earliest-known tetrapod tracks. Last, we rejected the presence of a pectoral-pelvic similarity bottleneck at the origin of tetrapods.

Background Along with the transition to the aquatic environment, cetaceans experienced profound changes in their skeletal anatomy, especially in the skull, including the posterodorsal migration of the external bony nares, the reorganization of skull bones (= telescoping) and the development of an extreme cranial asymmetry (in odontocetes). Telescoping represents an important anatomical shift in the topological organization of cranial bones and their sutural contacts; however, the impact of these changes in the connectivity pattern and integration of the skull has never been addressed. Methods Here, we apply the novel framework provided by the Anatomical Network Analysis to quantify the organization and integration of cetacean skulls, and the impact of the telescoping process in the connectivity pattern of the skull. We built anatomical networks for 21 cetacean skulls (three stem cetaceans, three extinct and 10 extant mysticetes, and three extinct and two extant odontocetes) and estimated network parameters related to their anatomical integration, complexity, heterogeneity, and modularity. This dataset was analyzed in the context of a broader tetrapod skull sample as well (43 species of 13 taxonomic groups). Results The skulls of crown cetaceans (Neoceti) occupy a new tetrapod skull morphospace, with better integrated, more heterogeneous and simpler skulls in comparison to other tetrapods. Telescoping adds connections and improves the integration of those bones involved in the telescoping process (e.g., maxilla, supraoccipital) as well as other ones (e.g., vomer) not directly affected by telescoping. Other underlying evolutionary processes (such as basicranial specializations linked with hearing/breathing adaptations) could also be responsible for the changes in the connectivity and integration of palatal bones. We also find prograde telescoped skulls of mysticetes distinct from odontocetes by an increased heterogeneity and modularity, whereas retrograde telescoped skulls of odontocetes are characterized by higher complexity. In mysticetes, as expected, the supraoccipital gains importance and centrality in comparison to odontocetes, increasing the heterogeneity of the skull network. In odontocetes, an increase in the number of connections and complexity is probably linked with the dominant movement of paired bones, such as the maxilla, in retrograde telescoping. Crown mysticetes (Eubalaena, Caperea, Piscobalaena, and Balaenoptera)are distinguished by having more integrated skulls in comparison to stem mysticetes (Aetiocetus and Yamatocetus), whereas crown odontocetes (Waipatia, Notocetus, Physeter, and Tursiops) have more complex skulls than stem forms (Albertocetus). Telescoping along with feeding, hearing and echolocation specializations could have driven the evolution of the different connectivity patterns of living lineages.

AbstractReconstructing patterns of macroevolution has become a central endeavor in paleobiology, because it offers insight into evolutionary models shaping the history of life. As the most diverse and abundant animals since the Cambrian period, arthropods provide copious data to elucidate the emergence of body plans in metazoan lineages. However, information provided by fossils on the tempo and mode of this phenomenon has lacked a recent synthesis. Here, I investigate macroevolutionary patterns of morphological evolution in Euarthropoda using a combined extinct and extant dataset optimized for multivariate analyses. Overall ordination patterns between the main morphogroups are consistent with another, independently coded, extant-only dataset providing molecular and morphological rates of evolution. Based on a “deep split” phylogenetic framework, total-group Mandibulata and Arachnomorpha emerge as directional morphoanatomical lineages, with basal fossil morphogroups showing heterogeneously spread-out occupations of the morphospace. In addition to a more homogeneous morphological variation, new morphogroups arose by successive reductions of translation distances; this pattern was interrupted only by terrestrialization events and the origin of pancrustaceans. A displaced optimum type of model is proposed to explain the fast assembly of canalized body plans during the Cambrian, with basal fossil morphogroups fitting intermediate fitness peaks in a moving adaptive landscape. Given time constraints imposed by the paleontological evidence, and owing to the interplay between canalization and modularity, as well as a decoupling between molecular and morphological rates, the rise of euarthropods would support the view that the swiftness of the Cambrian explosion was mostly associated with the buildup of genetic regulatory networks.

Organic design arises, on every scale, by the spontaneous self-organization of successively more complex structures from simpler subsidiary components. The resulting structures converge repeatedly on architectural designs that can be constructed by growing organisms, and that are viable but not necessarily optimal in relation to any one function. Empirical observations and theoretical models of developmental processes suggest that these recurrent elements of design are fixed point attractors which organic dissipative structures must necessarily approach. We characterize these structures as topological attractors, thereby emphasizing that they are determined by the properties of matter and the geometry of space-time.We have derived a set of potential designs for the elements of animal skeletons, in terms of geometric rules, growth processes, and the properties of materials. Skeletons or components of the skeletons of actual living and extinct organisms are matched with the possibilities defined within this theoretical morphospace. The extent to which the skeletal components of individual organisms are differentiated, exploiting various parts of the skeleton space, provides a crude metric of structural complexity. The skeleton space serves as a common context, in which we can compare the extent and pattern of exploitation of this range of potential organic designs, from one taxon to another.Our analyses show that the most evolutionarily advanced animals in a given class or phylum generally do not have the most complex skeletons; that molluscs and vertebrates are more morphologically diverse than arthropods; and that the physical constraints of life on land and in the air substantially limit the variety of skeletal structures suitable to be employed by animals living in these environments. Moreover, when the skeletons of all known animals, living and extinct, are considered together, we find that the total range of possible skeletal designs has been very fully exploited.These results strongly support the hypothesis that the essential elements of organic design are inherent in and predictable from the material properties of the universe. Environmental and demographic circumstances, invariably involving a large element of chance, together with the constraints of phylogenetic history largely determine the course of evolution in individual lineages. In contrast, structural principles such as those delineated here determine the recurrent themes of organic design, over large numbers of taxa and long periods of time. Thus, it is no accident that two major groups of animals that have most successfully exploited jointed lever skeletons, vertebrates and arthropods, have achieved the most diversified adaptive radiations, in the sea, on land and in the air.Among the structural paradigms defined within the skeleton space, stronger and weaker topological attractors may be identified empirically by the frequencies with which particular design elements have evolved independently, in unrelated taxa. Plausible functional or constructional rationales can readily be devised, ex post facto, to explain the repeated convergence of organic structures on some obvious strong topological attractors, such as branching networks, spiral cones, and bivalved shells. Unfortunately, it seems unlikely that an analytical method can be devised to predict these strong attractors a priori, as the variables involved cannot all be quantified in the same way.

Abstract The human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem only focused on trade-off between cost and global efficiency (i.e., integration) and overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-off among cost, integration, and segregation shapes human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity) (Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity [Tri-factor model (Q)] showed the best performance. They had high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.