Mesoscopic organization reveals the constraints governing C. elegans nervous system

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations, such as optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis, we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.Comment: Published version, Minor modifications, 16 pages, 9 figure

Structural Properties of the Caenorhabditis elegans Neuronal Network

Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation

Connectomes as constitutively epistemic objects: critical perspectives on modeling in current neuroanatomy

in a nervous system of a given species. This chapter provides a critical perspective on the role of connectomes in neuroscientific practice and asks how the connectomic approach fits into a larger context in which network thinking permeates technology, infrastructure, social life, and the economy. In the first part of this chapter, we argue that, seen from the perspective of ongoing research, the notion of connectomes as “complete descriptions” is misguided. Our argument combines Rachel Ankeny’s analysis of neuroanatomical wiring diagrams as “descriptive models” with Hans-Joerg Rheinberger’s notion of “epistemic objects,” i.e., targets of research that are still partially unknown. Combining these aspects we conclude that connectomes are constitutively epistemic objects: there just is no way to turn them into permanent and complete technical standards because the possibilities to map connection properties under different modeling assumptions are potentially inexhaustible. In the second part of the chapter, we use this understanding of connectomes as constitutively epistemic objects in order to critically assess the historical and political dimensions of current neuroscientific research. We argue that connectomics shows how the notion of the “brain as a network” has become the dominant metaphor of contemporary brain research. We further point out that this metaphor shares (potentially problematic) affinities to the form of contemporary “network societies.” We close by pointing out how the relation between connectomes and networks in society could be used in a more fruitful manner

Sensory Coding and Olfactory Integration in Caenorhabditis Elegans

Animals must sense their external environments to guide meaningful behavior. The nematode Caenorhabditis elegans, for example, uses volatile cues to navigate toward food from a distance. How does an animal integrate the olfactory information from its environment? Here, I ask how multiple sensory neurons drive and shape one interneuron’s activity. C. elegans senses several odors, including the bacterial metabolite diacetyl, using the AWA sensory neurons. AWA forms chemical and electrical synapses onto several interconnected interneurons, which contribute to chemotaxis toward attractive odors like diacetyl. One AWA target is the interneuron AIA, which is connected to AWA via a putative electrical synapse. Both AWA and AIA are robustly activated by diacetyl, but the reliability of their responses decreases at low concentrations. AIA relies on AWA for its reliable response to diacetyl. However, directly activating AWA is not sufficient to evoke reliable AIA responses. Instead, AIA responses to optogenetic AWA stimulation had high and variable latencies and low probabilities. AIA responses, when they did occur, had stereotyped on-dynamics to all concentrations of diacetyl tested, to AWA optogenetic stimulation, and to several additional attractive odors, suggesting all-or-none AIA activation to sensory input. In animals lacking chemical synaptic transmission, AIA responses to direct AWA optogenetic stimulation were fast and reliable, resembling those evoked by diacetyl. AWA-to-AIA communication is thus regulated by inhibitory synaptic input from surrounding neurons. This inhibition comes from a small set of glutamatergic sensory neurons that work together to gate AIA responses to AWA activation. Consistently, two of these glutamatergic sensory neurons directly sense and are inhibited by diacetyl. Their responses are less reliable, or even non-existent, at low concentrations of diacetyl. The difference in the reliability of AIA responses to different diacetyl concentrations may be explained by differences in the composition of the upstream sensory responses. Reliable AIA responses appear to require both activation from AWA through an electrical synapse and the release of inhibition from glutamatergic sensory neurons through chemical synapses. AIA acts as a coincidence detector, and its activity represents a readout of global sensory state, providing insight into how AIA represents “food” signals that are sensed by multiple sensory neurons

Connectomics of extrasynaptic signalling: applications to the nervous system of Caenorhabditis elegans

Connectomics – the study of neural connectivity – is primarily concerned with the mapping and characterisation of wired synaptic links; however, it is well established that long-distance chemical signalling via extrasynaptic volume transmission is also critical to brain function. As these interactions are not visible in the physical structure of the nervous system, current approaches to connectomics are unable to capture them. This work addresses the problem of missing extrasynaptic interactions by demonstrating for the first time that whole-animal volume transmission networks can be mapped from gene expression and ligand-receptor interaction data, and analysed as part of the connectome. Complete networks are presented for the monoamine systems of Caenorhabditis elegans, along with a representative sample of selected neuropeptide systems. A network analysis of the synaptic (wired) and extrasynaptic (wireless) connectomes is presented which reveals complex topological properties, including extrasynaptic rich-club organisation with interconnected hubs distinct from those in the synaptic and gap junction networks, and highly significant multilink motifs pinpointing locations in the network where aminergic and neuropeptide signalling is likely to modulate synaptic activity. Thus, the neuronal connectome can be modelled as a multiplex network with synaptic, gap junction, and neuromodulatory layers representing inter-neuronal interactions with different dynamics and polarity. This represents a prototype for understanding how extrasynaptic signalling can be integrated into connectomics research, and provides a novel dataset for the development of multilayer network algorithms.This work was supported by the Medical Research Council (MRC)

Towards Executable Biology

Heringa, J. [Promotor]Fokkink, W.J. [Promotor]Feenstra, K.A. [Copromotor

AN EDGE-CENTRIC PERSPECTIVE FOR BRAIN NETWORK COMMUNITIES

Thesis (Ph.D.) - Indiana University, Department of Psychological and Brain Sciences and Program in Neuroscience, 2021The brain is a complex system organized on multiple scales and operating in both a local and distributed manner. Individual neurons and brain regions participate in specific functions, while at the same time existing in the context of a larger network, supporting a range of different functionalities. Building brain networks comprised of distinct neural elements (nodes) and their interrelationships (edges), allows us to model the brain from both local and global perspectives, and to deploy a wide array of computational network tools. A popular network analysis approach is community detection, which aims to subdivide a network’s nodes into clusters that can used to represent and evaluate network organization. Prevailing community detection approaches applied to brain networks are designed to find densely interconnected sets of nodes, leading to the notion that the brain is organized in an exclusively modular manner. Furthermore, many brain network analyses tend to focus on the nodes, evidenced by the search for modular groupings of neural elements that might serve a common function. In this thesis, we describe the application of community detection algorithms that are sensitive to alternative cluster configurations, enhancing our understanding of brain network organization. We apply a framework called the stochastic block model, which we use to uncover evidence of non-modular organization in human anatomical brain networks across the life span, and in the informatically-collated rat cerebral cortex. We also propose a framework to cluster functional brain network edges in human data, which naturally results in an overlapping organization at the level of nodes that bridges canonical functional systems. These alternative methods utilize the connection patterns of brain network edges in ways that prevailing approaches do not. Thus, we motivate an alternative outlook which focuses on the importance of information provided by the brain’s interconnections, or edges. We call this an edge-centric perspective. The edge-centric approaches developed here offer new ways to characterize distributed brain organization and contribute to a fundamental change in perspective in our thinking about the brain

A Brief History of Simulation Neuroscience

Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain