Showing papers by "Nicholas J. Strausfeld published in 2021"
TL;DR: In this paper, the authors demonstrate unsurpassed preservation of cerebral tissue in Kaili leanchoiliids revealing near-identical arrangements of bilaterally symmetric ganglia identified as the proto-, deuto-, and tritocerebra disposed behind an asegmental frontal domain, the prosocerebrum, from which paired nerves extend to labral ganglia flanking the stomodeum.
13 citations
TL;DR: In this article, the authors review the optic lobes in Malacostraca and Insects to inquire which of their characters correspond genealogically across Pancrustacea and which characters do not, and demonstrate that neuroanatomical characters pertaining to the third optic lobe neuropil may indicate convergent evolution.
7 citations
TL;DR: This essay defines the traits that designate the lobula plate and argues against a homologue in Crustacea, and proposes that the origin of the lobulas plate is relatively recent and may relate to theorigin of flight.
7 citations
TL;DR: In Brachyura, the most recent malacostracan lineage in the shore crab Hemigrapsus nudus, instead of occupying the rostral surface of the lateral protocerebrum, mushroom body calyces are buried deep within it with their columns extending outwards to an expansive system of gyri on the brain's surface as discussed by the authors.
Abstract: Neural organization of mushroom bodies is largely consistent across insects, whereas the ancestral ground pattern diverges broadly across crustacean lineages resulting in successive loss of columns and the acquisition of domed centers retaining ancestral Hebbian-like networks and aminergic connections We demonstrate here a major departure from this evolutionary trend in Brachyura, the most recent malacostracan lineage In the shore crab Hemigrapsus nudus, instead of occupying the rostral surface of the lateral protocerebrum, mushroom body calyces are buried deep within it with their columns extending outwards to an expansive system of gyri on the brain's surface The organization amongst mushroom body neurons reaches extreme elaboration throughout its constituent neuropils The calyces, columns, and especially the gyri show DC0 immunoreactivity, an indicator of extensive circuits involved in learning and memory
6 citations
TL;DR: In one species of shore crab (Brachyura, Varunidae), a center that supports long-term visual habituation and that matches the reniform body's morphology has been claimed as a homolog of the insect mushroom body despite lacking traits that define it as such.
Abstract: In one species of shore crab (Brachyura, Varunidae), a center that supports long-term visual habituation and that matches the reniform body's morphology has been claimed as a homolog of the insect mushroom body despite lacking traits that define it as such. The discovery in a related species of shore crab of a mushroom body possessing those defining traits renders that interpretation unsound. Two phenotypically distinct, coexisting centers cannot both be homologs of the insect mushroom body. The present commentary outlines the history of research leading to misidentification of the reniform body as a mushroom body. One conclusion is that if both centers support learning and memory, this would be viewed as a novel and fascinating attribute of the pancrustacean brain.
5 citations
University of Southern California1, University of Illinois at Urbana–Champaign2, Oregon State University3, Arizona State University4, University of South Carolina5, Michigan State University6, Rensselaer Polytechnic Institute7, Hauptman-Woodward Medical Research Institute8, University of Washington9, University of Arizona10
TL;DR: In this article, the authors 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, from highly stable to exquisitely sensitive to perturbation.
Abstract: 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.
3 citations