The Influence of Wiring Economy on Nervous System Evolution

An escape-enhancing circuit involving subthalamic CRH neurons mediates stress-induced anhedonia in mice

Chronic predator stress (CPS) is an important and ecologically relevant tool for inducing anhedonia in animals, but the neural circuits underlying the associated neurobiological changes remain to be identified. Using cell-type-specific manipulations, we found that corticotropin-releasing hormone (CRH) neurons in the medial subthalamic nucleus (mSTN) enhance struggle behaviors in inescapable situations and lead to anhedonia, predominately through projections to the external globus pallidus (GPe). Recordings of in vivo neuronal activity revealed that CPS distorted mSTN-CRH neuronal responsivity to negative and positive stimuli, which may underlie CPS-induced behavioral despair and anhedonia. Furthermore, we discovered presynaptic inputs from the bed nucleus of the stria terminalis (BNST) to mSTN-CRH neurons projecting to the GPe that were enhanced following CPS, and these inputs may mediate such behaviors. This study identifies a neurocircuitry that co-regulates escape response and anhedonia in response to predator stress. This new understanding of the neural basis of defensive behavior in response to predator stress will likely benefit our understanding of neuropsychiatric diseases.

Dendrite architecture determines mitochondrial distribution patterns in vivo

Neuronal morphology influences synaptic connectivity and neuronal signal processing. However, it remains unclear how neuronal shape affects steady-state distributions of organelles like mitochondria. In this work, we investigated the link between mitochondrial transport and dendrite branching patterns by combining mathematical modeling with in vivo measurements of dendrite architecture, mitochondrial motility, and mitochondrial localization patterns in Drosophila HS (horizontal system) neurons. In our model, different forms of morphological and transport scaling rules-which set the relative thicknesses of parent and daughter branches at each junction in the dendritic arbor and link mitochondrial motility to branch thickness-predict dramatically different global mitochondrial localization patterns. We show that HS dendrites obey the specific subset of scaling rules that, in our model, lead to realistic mitochondrial distributions. Moreover, we demonstrate that neuronal activity does not affect mitochondrial transport or localization, indicating that steady-state mitochondrial distributions are hard-wired by the architecture of the neuron.

Could theropod dinosaurs have evolved to a human level of intelligence?

Noting that some theropod dinosaurs had large brains, large grasping hands, and likely binocular vision, paleontologist Dale Russell suggested that a branch of these dinosaurs might have evolved to a human intelligence level, had dinosaurs not become extinct. I offer reasons why the likely pallial organization in dinosaurs would have made this improbable, based on four assumptions. First, it is assumed that achieving human intelligence requires evolving an equivalent of the about 200 functionally specialized cortical areas characteristic of humans. Second, it is assumed that dinosaurs had an avian nuclear type of pallial organization, in contrast to the mammalian cortical organization. Third, it is assumed that the interactions between the different neuron types making up an information processing unit within pallium are critical to its role in analyzing information. Finally, it is assumed that increasing axonal length between the neuron sets carrying out this operation impairs its efficacy. Based on these assumptions, I present two main reasons why dinosaur pallium might have been unable to add the equivalent of 200 efficiently functioning cortical areas. First, a nuclear pattern of pallial organization would require increasing distances between the neuron groups corresponding to the separate layers of any given mammalian cortical area, as more sets of nuclei equivalent to a cortical area are interposed between the existing sets, increasing axon length and thereby impairing processing efficiency. Second, because of its nuclear organization, dinosaur pallium could not reduce axon length by folding to bring adjacent areas closer together, as occurs in cerebral cortex.

Biomechanical models and mechanisms of cellular morphogenesis and cerebral cortical expansion and folding

Morphogenesis of the nervous system involves a highly complex spatio-temporal pattern of physical forces (mainly tension and pressure) acting on cells and tissues that are pliable but have an intricately organized cytoskeletal infrastructure. This review begins by covering basic principles of biomechanics and the core cytoskeletal toolkit used to regulate the shapes of cells and tissues during embryogenesis and neural development. It illustrates how the principle of 'tensegrity' provides a useful conceptual framework for understanding how cells dynamically respond to forces that are generated internally or applied externally. The latter part of the review builds on this foundation in considering the development of mammalian cerebral cortex. The main focus is on cortical expansion and folding - processes that take place over an extended period of prenatal and postnatal development. Cortical expansion and folding are likely to involve many complementary mechanisms, some related to regulating cell proliferation and migration and others related to specific types and patterns of mechanical tension and pressure. Three distinct multi-mechanism models are evaluated in relation to a set of 18 key experimental observations and findings. The Composite Tension Plus (CT+) model is introduced as an updated version of a previous multi-component Differential Expansion Sandwich Plus (DES+) model (Van Essen, 2020); the new CT+ model includes 10 distinct mechanisms and has the greatest explanatory power among published models to date. Much needs to be done in order to validate specific mechanistic components and to assess their relative importance in different species, and important directions for future research are suggested.

The fractal brain: scale-invariance in structure and dynamics

The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.

Long-term morphological and functional dynamics of human stem cell-derived neuronal networks on high-density micro-electrode arrays

Comprehensive electrophysiological characterizations of human induced pluripotent stem cell (hiPSC)-derived neuronal networks are essential to determine to what extent these in vitro models recapitulate the functional features of in vivo neuronal circuits. High-density micro-electrode arrays (HD-MEAs) offer non-invasive recording with the best spatial and temporal resolution possible to date. For 3 months, we tracked the morphology and activity features of developing networks derived from a transgenic hiPSC line in which neurogenesis is inducible by neurogenic transcription factor overexpression. Our morphological data revealed large-scale structural changes from homogeneously distributed neurons in the first month to the formation of neuronal clusters over time. This led to a constant shift in position of neuronal cells and clusters on HD-MEAs and corresponding changes in spatial distribution of the network activity maps. Network activity appeared as scarce action potentials (APs), evolved as local bursts with longer duration and changed to network-wide synchronized bursts with higher frequencies but shorter duration over time, resembling the emerging burst features found in the developing human brain. Instantaneous firing rate data indicated that the fraction of fast spiking neurons (150–600 Hz) increases sharply after 63 days post induction (dpi). Inhibition of glutamatergic synapses erased burst features from network activity profiles and confirmed the presence of mature excitatory neurotransmission. The application of GABAergic receptor antagonists profoundly changed the bursting profile of the network at 120 dpi. This indicated a GABAergic switch from excitatory to inhibitory neurotransmission during circuit development and maturation. Our results suggested that an emerging GABAergic system at older culture ages is involved in regulating spontaneous network bursts. In conclusion, our data showed that long-term and continuous microscopy and electrophysiology readouts are crucial for a meaningful characterization of morphological and functional maturation in stem cell-derived human networks. Most importantly, assessing the level and duration of functional maturation is key to subject these human neuronal circuits on HD-MEAs for basic and biomedical applications.

Functional network topography of the medial entorhinal cortex

The investigation of the topographic organization of spatially coding cell types in the medial entorhinal cortex (MEC) has so far been held back by the lack of appropriate tools that enable the precise recording of both the anatomical location and activity of large populations of cells while animals forage in open environments. In this study, we use the newest generation of head-mounted, miniaturized two-photon microscopes to image grid, head-direction, border, as well as object-vector cells in MEC and neighboring parasubiculum within the same animals. The majority of cell types were intermingled, but grid and object-vector cells exhibited little overlap. The results have implications for network models of spatial coding.

The medial entorhinal cortex (MEC) creates a map of local space, based on the firing patterns of grid, head-direction (HD), border, and object-vector (OV) cells. How these cell types are organized anatomically is debated. In-depth analysis of this question requires collection of precise anatomical and activity data across large populations of neurons during unrestrained behavior, which neither electrophysiological nor previous imaging methods fully afford. Here, we examined the topographic arrangement of spatially modulated neurons in the superficial layers of MEC and adjacent parasubiculum using miniaturized, portable two-photon microscopes, which allow mice to roam freely in open fields. Grid cells exhibited low levels of co-occurrence with OV cells and clustered anatomically, while border, HD, and OV cells tended to intermingle. These data suggest that grid cell networks might be largely distinct from those of border, HD, and OV cells and that grid cells exhibit strong coupling among themselves but weaker links to other cell types.

Neural optimization: Understanding trade-offs with Pareto theory

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function, as well as additional factors such as robustness to environmental changes and energetic limitations. Oftentimes these objectives compete, and quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems from biophysically detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.

The RNA-binding protein Musashi controls axon compartment-specific synaptic connectivity through ptp69D mRNA poly(A)-tailing

Synaptic targeting with subcellular specificity is essential for neural circuit assembly. Developing neurons use mechanisms to curb promiscuous synaptic connections and to direct synapse formation to defined subcellular compartments. How this selectivity is achieved molecularly remains enigmatic. Here, we discover a link between mRNA poly(A)-tailing and axon collateral branch-specific synaptic connectivity within the CNS. We reveal that the RNA-binding protein Musashi binds to the mRNA encoding the receptor protein tyrosine phosphatase Ptp69D, thereby increasing poly(A) tail length and Ptp69D protein levels. This regulation specifically promotes synaptic connectivity in one axon collateral characterized by a high degree of arborization and strong synaptogenic potential. In a different compartment of the same axon, Musashi prevents ectopic synaptogenesis, revealing antagonistic, compartment-specific functions. Moreover, Musashi-dependent Ptp69D regulation controls synaptic connectivity in the olfactory circuit. Thus, Musashi differentially shapes synaptic connectivity at the level of individual subcellular compartments and within different developmental and neuron type-specific contexts.

Primate neuronal connections are sparse in cortex as compared to mouse

Detailing how primate and mouse neurons differ is critical for creating generalized models of how neurons process information. We reconstruct 15,748 synapses in adult Rhesus macaques and mice and ask how connectivity differs on identified cell types in layer 2/3 of primary visual cortex. Primate excitatory and inhibitory neurons receive 2-5 times fewer excitatory and inhibitory synapses than similar mouse neurons. Primate excitatory neurons have lower excitatory-to-inhibitory (E/I) ratios than mouse but similar E/I ratios in inhibitory neurons. In both species, properties of inhibitory axons such as synapse size and frequency are unchanged, and inhibitory innervation of excitatory neurons is local and specific. Using artificial recurrent neural networks (RNNs) optimized for different cognitive tasks, we find that penalizing networks for creating and maintaining synapses, as opposed to neuronal firing, reduces the number of connections per node as the number of nodes increases, similar to primate neurons compared with mice.

Architectures of neuronal circuits

[Figure: see text].

Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number

Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843-1864 (1960)] and [J. Sawada, D. S. Modha, "Synapse: Scalable energy-efficient neurosynaptic computing" in Application of Concurrency to System Design (ACSD) (2013), pp. 14-15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a [Formula: see text]-fold discrepancy between biological and lowest possible values of a neuron's computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).

Drosophila Fezf functions as a transcriptional repressor to direct layer-specific synaptic connectivity in the fly visual system

Functionally relevant neuronal connections are often organized within discrete layers of neuropil to ensure proper connectivity and information processing. While layer-specific assembly of neuronal connectivity is a dynamic process involving stepwise interactions between different neuron types, the mechanisms underlying this critical developmental process are not well understood. Here, we investigate the role of the transcription factor dFezf in layer selection within the Drosophila visual system, which is important for synaptic specificity. Our findings show that dFezf functions as a transcriptional repressor governing the precise temporal expression pattern of downstream genes, including other transcription factors required for proper connectivity. Layer-specific assembly of neuronal connectivity in the fly visual system is thus orchestrated by precise, temporally controlled transcriptional cascades.

The layered compartmentalization of synaptic connections, a common feature of nervous systems, underlies proper connectivity between neurons and enables parallel processing of neural information. However, the stepwise development of layered neuronal connections is not well understood. The medulla neuropil of the Drosophila visual system, which comprises 10 discrete layers (M1 to M10), where neural computations underlying distinct visual features are processed, serves as a model system for understanding layered synaptic connectivity. The first step in establishing layer-specific connectivity in the outer medulla (M1 to M6) is the innervation by lamina (L) neurons of one of two broad, primordial domains that will subsequently expand and transform into discrete layers. We previously found that the transcription factor dFezf cell-autonomously directs L3 lamina neurons to their proper primordial broad domain before they form synapses within the developing M3 layer. Here, we show that dFezf controls L3 broad domain selection through temporally precise transcriptional repression of the transcription factor slp1 (sloppy paired 1). In wild-type L3 neurons, slp1 is transiently expressed at a low level during broad domain selection. When dFezf is deleted, slp1 expression is up-regulated, and ablation of slp1 fully rescues the defect of broad domain selection in dFezf-null L3 neurons. Although the early, transient expression of slp1 is expendable for broad domain selection, it is surprisingly necessary for the subsequent L3 innervation of the M3 layer. DFezf thus functions as a transcriptional repressor to coordinate the temporal dynamics of a transcriptional cascade that orchestrates sequential steps of layer-specific synapse formation.

Interdependencies of Infrastructure Investment Decisions in Multi-Energy Systems—A Sensitivity Analysis for Urban Residential Areas

Considering the European Union (EU) climate targets, the heating sector should be decarbonized by 80% to 95% up to 2050. Thus, the macro-trends forecast increasing energy efficiency and focus on the use of renewable gas or the electrification of heat generation. This has implications for the business models of urban electricity and in particular natural gas distribution network operators (DNOs): When the energy demand decreases, a disproportionately long grid is operated, which can cause a rise of grid charges and thus the gas price. This creates a situation in which a self-reinforcing feedback loop starts, which increases the risk of gas grid defection. We present a mixed integer linear optimization model to analyze the interdependencies between the electricity and gas DNOs’ and the building owners’ investment decisions during the transformation path. The results of the investigation in a real grid area are used to validate the simulation setup of a sensitivity analysis of 27 types of building collectives and five grid topologies, which provides a systematic insight into the interrelated system. Therefore, it is possible to identify building and grid configurations that increase the risk of a complete gas grid shutdown and those that should be operated as a flexibility option in a future renewable energy system.

Neural arbors are Pareto optimal

Neural arbors (dendrites and axons) can be viewed as graphs connecting the cell body of a neuron to various pre- and post-synaptic partners. Several constraints have been proposed on the topology of these graphs, such as minimizing the amount of wire needed to construct the arbor (wiring cost), and minimizing the graph distances between the cell body and synaptic partners (conduction delay). These two objectives compete with each other-optimizing one results in poorer performance on the other. Here, we describe how well neural arbors resolve this network design trade-off using the theory of Pareto optimality. We develop an algorithm to generate arbors that near-optimally balance between these two objectives, and demonstrate that this algorithm improves over previous algorithms. We then use this algorithm to study how close neural arbors are to being Pareto optimal. Analysing 14 145 arbors across numerous brain regions, species and cell types, we find that neural arbors are much closer to being Pareto optimal than would be expected by chance and other reasonable baselines. We also investigate how the location of the arbor on the Pareto front, and the distance from the arbor to the Pareto front, can be used to classify between some arbor types (e.g. axons versus dendrites, or different cell types), highlighting a new potential connection between arbor structure and function. Finally, using this framework, we find that another biological branching structure-plant shoot architectures used to collect and distribute nutrients-are also Pareto optimal, suggesting shared principles of network design between two systems separated by millions of years of evolution.

Structural optimality and neurogenetic expression mediate functional dynamics in the human brain

The human brain exhibits a rich functional repertoire in terms of complex functional connectivity patterns during rest and tasks. However, how this is developed upon a fixed structural anatomy remains poorly understood. Here we investigated the hypothesis that resting state functional connectivity and the manner in which it changes during tasks related to a set of underlying structural connections that promote optimal communication in the brain. We used a game‐theoretic model to identify such optimal connections in the structural connectome of 50 healthy individuals and subsequently used the optimal structural connections to predict resting‐state functional connectivity with high accuracy. In contrast, we found that nonoptimal connections accurately predicted functional connectivity during a working memory task. We further found that this balance between optimal and nonoptimal connections between brain regions was associated with a specific gene expression linked to neurotransmission. This multimodal evidence shows for the first time that structure–function relationships in the human brain are related to how brain networks navigate information along different white matter connections as well as the brain's underlying genetic profile.

Impact of Natural Gas Distribution Network Structure and Operator Strategies on Grid Economy in Face of Decreasing Demand

Currently, natural gas provides more than a third of the energy used in European residential buildings. As part of the general decline of fossil fuels, this gas consumption is predicted to drop in several countries by 25–100% by 2050. We model a decline in gas consumption in 57 urban German distribution grids looking for the influence of grid-specific factors and different distribution network operator (DNO) strategies on grid charges. We find a functional relationship between grid length and customer amount described by a power law, with an exponent correlated with structural grid parameters. The disordered structure inherent to grids typically results in a decline in grid costs much slower than the corresponding demand. We introduce a simplified yearly cash flow calculation model based on the power law and validate it against mixed integer linear optimization. A comparison of the total costs of operation and resulting grid charges for several scenarios and strategies estimates the effects on DNO business models. Depending on a combination of DNO’s strategy and customers’ exit pattern, grid charges may increase, accelerating the substitution of gas-bound technologies that might develop into a self-reinforcing feedback loop, leading to grid defection.

Developmental trajectory of Caenorhabditis elegans nervous system governs its structural organization

A central problem of neuroscience involves uncovering the principles governing the organization of nervous systems which ensure robustness in brain development. The nematode Caenorhabditis elegans provides us with a model organism for studying this question. In this paper, we focus on the invariant connection structure and spatial arrangement of the neurons comprising the somatic neuronal network of this organism to understand the key developmental constraints underlying its design. We observe that neurons with certain shared characteristics-such as, neural process lengths, birth time cohort, lineage and bilateral symmetry-exhibit a preference for connecting to each other. Recognizing the existence of such homophily and their relative degree of importance in determining connection probability within neurons (for example, in synapses, symmetric pairing is the most dominant factor followed by birth time cohort, process length and lineage) helps in connecting specific neuronal attributes to the topological organization of the network. Further, the functional identities of neurons appear to dictate the temporal hierarchy of their appearance during the course of development. Providing crucial insights into principles that may be common across many organisms, our study shows how the trajectory in the developmental landscape constrains the structural organization of a nervous system.

A Model of Brain Folding Based on Strong Local and Weak Long-Range Connectivity Requirements

Throughout the animal kingdom, the structure of the central nervous system varies widely from distributed ganglia in worms to compact brains with varying degrees of folding in mammals. The differences in structure may indicate a fundamentally different circuit organization. However, the folded brain most likely is a direct result of mechanical forces when considering that a larger surface area of cortex packs into the restricted volume provided by the skull. Here, we introduce a computational model that instead of modeling mechanical forces relies on dimension reduction methods to place neurons according to specific connectivity requirements. For a simplified connectivity with strong local and weak long-range connections, our model predicts a transition from separate ganglia through smooth brain structures to heavily folded brains as the number of cortical columns increases. The model reproduces experimentally determined relationships between metrics of cortical folding and its pathological phenotypes in lissencephaly, polymicrogyria, microcephaly, autism, and schizophrenia. This suggests that mechanical forces that are known to lead to cortical folding may synergistically contribute to arrangements that reduce wiring. Our model provides a unified conceptual understanding of gyrification linking cellular connectivity and macroscopic structures in large-scale neural network models of the brain.

Whole-animal connectomes of both Caenorhabditis elegans sexes

Knowledge of connectivity in the nervous system is essential to understanding its function. Here we describe connectomes for both adult sexes of the nematode Caenorhabditis elegans, an important model organism for neuroscience research. We present quantitative connectivity matrices that encompass all connections from sensory input to end-organ output across the entire animal, information that is necessary to model behaviour. Serial electron microscopy reconstructions that are based on the analysis of both new and previously published electron micrographs update previous results and include data on the male head. The nervous system differs between sexes at multiple levels. Several sex-shared neurons that function in circuits for sexual behaviour are sexually dimorphic in structure and connectivity. Inputs from sex-specific circuitry to central circuitry reveal points at which sexual and non-sexual pathways converge. In sex-shared central pathways, a substantial number of connections differ in strength between the sexes. Quantitative connectomes that include all connections serve as the basis for understanding how complex, adaptive behavior is generated.

Topological principles and developmental algorithms might refine diffusion tractography

The identification and reconstruction of axonal pathways in the living brain or "ex-vivo" is promising a revolution in connectivity studies bridging the gap from animal to human neuroanatomy with extensions to brain structural-functional correlates. Unfortunately, the methods suffer from juvenile drawbacks. In this perspective paper we mention several computational and developmental principles, which might stimulate a new generation of algorithms and a discussion bridging the neuroimaging and neuroanatomy communities.

Evolving understanding of nervous system evolution

In this Guest Editorial, Jeremy Niven and Lars Chittka introduce our special issue on the evolution of nervous systems.

Developmental and genetic mechanisms of neural circuit evolution

Regardless of how a nervous system is genetically built, natural selection is acting on the functional outcome of its activity. To understand how nervous systems evolve, it is essential to analyze how their functional units - the neural circuits - change and adapt over time. A neural circuit can evolve in many different ways, and the underlying developmental and genetic mechanisms involve different sets of genes. Therefore, the comparison of gene expression can help reconstructing circuit evolution, as demonstrated by several examples in sensory systems. Functional constraints on neural circuit evolution suggest that in nervous systems developmental and genetic variants do not appear randomly, and that the evolution of neuroanatomy might be biased. Sensory systems, in particular, seem to evolve along trajectories that enhance their evolvability, ensuring adaptation to different environments.