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Synopsis Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, i.e. dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations—an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales. 

Descriptions of crustacean brains have focused mainly on three highly derived lineages of malacostracans: the reptantian infraorders represented by spiny lobsters, lobsters, and crayfish. Those descriptions advocate the view that dome- or cap-like neuropils, referred to as ‘hemiellipsoid bodies,’ are the ground pattern organization of centers that are comparable to insect mushroom bodies in processing olfactory information. Here we challenge the doctrine that hemiellipsoid bodies are a derived trait of crustaceans, whereas mushroom bodies are a derived trait of hexapods. We demonstrate that mushroom bodies typify lineages that arose before Reptantia and exist in Reptantia thereby indicating that the mushroom body, not the hemiellipsoid body, provides the ground pattern for both crustaceans and hexapods. We show that evolved variations of the mushroom body ground pattern are, in some lineages, defined by extreme diminution or loss and, in others, by the incorporation of mushroom body circuits into lobeless centers. Such transformations are ascribed to modifications of the columnar organization of mushroom body lobes that, as shown in Drosophila and other hexapods, contain networks essential for learning and memory. 

Mantis shrimps (Stomatopoda) possess in common with other crustaceans, and with Hexapoda, specific neuroanatomical attributes of the protocerebrum, the most anterior part of the arthropod brain. These attributes include assemblages of interconnected centers called the central body complex and in the lateral protocerebra, situated in the eyestalks, paired mushroom bodies. The phenotypic homologues of these centers across Panarthropoda support the view that ancestral integrative circuits crucial to action selection and memory have persisted since the early Cambrian or late Ediacaran. However, the discovery of another prominent integrative neuropil in the stomatopod lateral protocerebrum raises the question whether it is unique to Stomatopoda or at least most developed in this lineage, which may have originated in the upper Ordovician or early Devonian. Here, we describe the neuroanatomical structure of this center, called the reniform body. Using confocal microscopy and classical silver staining, we demonstrate that the reniform body receives inputs from multiple sources, including the optic lobe's lobula. Although the mushroom body also receives projections from the lobula, it is entirely distinct from the reniform body, albeit connected to it by discrete tracts. We discuss the implications of their coexistence in Stomatopoda, the occurrence of the reniform body in another eumalacostracan lineage and what this may mean for our understanding of brain functionality in Pancrustacea.