INTRODUCTION A brainwide, synaptic-resolution connectivity map—a connectome—is essential for understanding how the brain generates behavior. However because of technological constraints imaging entire brains with electron microscopy (EM) and reconstructing circuits from such datasets has been challenging. To date, complete connectomes have been mapped for only three organisms, each with several hundred brain neurons: the nematode C. elegans , the larva of the sea squirt Ciona intestinalis , and of the marine annelid Platynereis dumerilii . Synapse-resolution circuit diagrams of larger brains, such as insects, fish, and mammals, have been approached by considering select subregions in isolation. However, neural computations span spatially dispersed but interconnected brain regions, and understanding any one computation requires the complete brain connectome with all its inputs and outputs. RATIONALE We therefore generated a connectome of an entire brain of a small insect, the larva of the fruit fly, Drosophila melanogaster. This animal displays a rich behavioral repertoire, including learning, value computation, and action selection, and shares homologous brain structures with adult Drosophila and larger insects. Powerful genetic tools are available for selective manipulation or recording of individual neuron types. In this tractable model system, hypotheses about the functional roles of specific neurons and circuit motifs revealed by the connectome can therefore be readily tested. RESULTS The complete synaptic-resolution connectome of the Drosophila larval brain comprises 3016 neurons and 548,000 synapses. We performed a detailed analysis of the brain circuit architecture, including connection and neuron types, network hubs, and circuit motifs. Most of the brain’s in-out hubs (73%) were postsynaptic to the learning center or presynaptic to the dopaminergic neurons that drive learning. We used graph spectral embedding to hierarchically cluster neurons based on synaptic connectivity into 93 neuron types, which were internally consistent based on other features, such as morphology and function. We developed an algorithm to track brainwide signal propagation across polysynaptic pathways and analyzed feedforward (from sensory to output) and feedback pathways, multisensory integration, and cross-hemisphere interactions. We found extensive multisensory integration throughout the brain and multiple interconnected pathways of varying depths from sensory neurons to output neurons forming a distributed processing network. The brain had a highly recurrent architecture, with 41% of neurons receiving long-range recurrent input. However, recurrence was not evenly distributed and was especially high in areas implicated in learning and action selection. Dopaminergic neurons that drive learning are amongst the most recurrent neurons in the brain. Many contralateral neurons, which projected across brain hemispheres, were in-out hubs and synapsed onto each other, facilitating extensive interhemispheric communication. We also analyzed interactions between the brain and nerve cord. We found that descending neurons targeted a small fraction of premotor elements that could play important roles in switching between locomotor states. A subset of descending neurons targeted low-order post-sensory interneurons likely modulating sensory processing. CONCLUSION The complete brain connectome of the Drosophila larva will be a lasting reference study, providing a basis for a multitude of theoretical and experimental studies of brain function. The approach and computational tools generated in this study will facilitate the analysis of future connectomes. Although the details of brain organization differ across the animal kingdom, many circuit architectures are conserved. As more brain connectomes of other organisms are mapped in the future, comparisons between them will reveal both common and therefore potentially optimal circuit architectures, as well as the idiosyncratic ones that underlie behavioral differences between organisms. Some of the architectural features observed in the Drosophila larval brain, including multilayer shortcuts and prominent nested recurrent loops, are found in state-of-the-art artificial neural networks, where they can compensate for a lack of network depth and support arbitrary, task-dependent computations. Such features could therefore increase the brain’s computational capacity, overcoming physiological constraints on the number of neurons. Future analysis of similarities and differences between brains and artificial neural networks may help in understanding brain computational principles and perhaps inspire new machine learning architectures. The connectome of the Drosophila larval brain. The morphologies of all brain neurons, reconstructed from a synapse-resolution EM volume, and the synaptic connectivity matrix of an entire brain. This connectivity information was used to hierarchically cluster all brains into 93 cell types, which were internally consistent based on morphology and known function.
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Neurotransmission and neuromodulation systems in the learning and memory network of Octopus vulgaris
The vertical lobe (VL) in the octopus brain plays an essential role in its sophisticated learning and memory. Early anatomical studies suggested that the VL is organized in a “fan-out fan-in” connectivity matrix comprising only three morphologically identified neuron types; input axons from the superior frontal lobe (SFL) innervating en passant millions of small amacrine interneurons (AMs) which converge sharply onto large VL output neurons (LNs). Recent physiological studies confirmed the feedforward excitatory connectivity: a glutamatergic synapse at the first SFL-to-AM synaptic layer and a cholinergic AM-to-LNs synapse. SFL-to-AMs synapses show a robust hippocampal-like activity-dependent long-term potentiation (LTP) of transmitter release. 5-HT, octopamine, dopamine, and nitric oxide modulate short- and long-term VL synaptic plasticity. Here we present a comprehensive histolabeling study to better characterize the neural elements in the VL. We generally confirmed glutamatergic SFLs and cholinergic AMs. Intense labeling for NOS activity in the AMs neurites fitted with the NO-dependent presynaptic LTP mechanism at the SFL-to-AM synapse. New discoveries here reveal more heterogeneity of the VL neurons than previously thought. GABAergic AMs suggest a subpopulation of inhibitory interneurons in the first input layer. Clear GABA labeling in the cell bodies of LNs supported an inhibitory VL output yet the LNs co-expressed FMRFamide-like neuropeptides suggesting an additional neuromodulatory role of the VL output. Furthermore, a group of LNs was glutamatergic. A new cluster of cells organized in a “deep nucleus” showed rich catecholaminergic labeling and may play a role in intrinsic neuromodulation. In situ hybridization and immunolabeling allowed characterization and localization of a rich array of neuropeptides and neuromodulators, likely involved in reward/punishment signals. This analysis of the fast transmission system, together with the newly found cellular elements helps integrate behavioral, physiological, pharmacological, and connectome findings into a more comprehensive understanding of an efficient learning and memory network.
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- Award ID(s):
- 1645219
- NSF-PAR ID:
- 10312816
- Date Published:
- Journal Name:
- bioRxiv
- ISSN:
- 2692-8205
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The structure of neural circuitry plays a crucial role in brain function. Previous studies of brain organization generally had to trade off between coarse descriptions at a large scale and fine descriptions on a small scale. Researchers have now reconstructed tens to hundreds of thousands of neurons at synaptic resolution, enabling investigations into the interplay between global, modular organization, and cell type-specific wiring. Analyzing data of this scale, however, presents unique challenges. To address this problem, we applied novel community detection methods to analyze the synapse-level reconstruction of an adult female
Drosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Using a machine-learning algorithm, we find the most densely connected communities of neurons by maximizing a generalized modularity density measure. We resolve the community structure at a range of scales, from large (on the order of thousands of neurons) to small (on the order of tens of neurons). We find that the network is organized hierarchically, and larger-scale communities are composed of smaller-scale structures. Our methods identify well-known features of the fly brain, including its sensory pathways. Moreover, focusing on specific brain regions, we are able to identify subnetworks with distinct connectivity types. For example, manual efforts have identified layered structures in the fan-shaped body. Our methods not only automatically recover this layered structure, but also resolve finer connectivity patterns to downstream and upstream areas. We also find a novel modular organization of the superior neuropil, with distinct clusters of upstream and downstream brain regions dividing the neuropil into several pathways. These methods show that the fine-scale, local network reconstruction made possible by modern experimental methods are sufficiently detailed to identify the organization of the brain across scales, and enable novel predictions about the structure and function of its parts.Significance Statement The Hemibrain is a partial connectome of an adult femaleDrosophila melanogaster brain containing >20,000 neurons and 10 million synapses. Analyzing the structure of a network of this size requires novel and efficient computational tools. We applied a new community detection method to automatically uncover the modular structure in the Hemibrain dataset by maximizing a generalized modularity measure. This allowed us to resolve the community structure of the fly hemibrain at a range of spatial scales revealing a hierarchical organization of the network, where larger-scale modules are composed of smaller-scale structures. The method also allowed us to identify subnetworks with distinct cell and connectivity structures, such as the layered structures in the fan-shaped body, and the modular organization of the superior neuropil. Thus, network analysis methods can be adopted to the connectomes being reconstructed using modern experimental methods to reveal the organization of the brain across scales. This supports the view that such connectomes will allow us to uncover the organizational structure of the brain, which can ultimately lead to a better understanding of its function. -
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Abstract Nicotinic acetylcholine receptors (nAChRs) are known to play a role in cognitive functions of the hippocampus, such as memory consolidation. Given that they conduct Ca2+and are capable of regulating the release of glutamate and γ‐aminobutyric acid (GABA) within the hippocampus, thereby shifting the excitatory‐inhibitory ratio, we hypothesized that the activation of nAChRs will result in the potentiation of hippocampal networks and alter synchronization. We used nicotine as a tool to investigate the impact of activation of nAChRs on neuronal network dynamics in primary embryonic rat hippocampal cultures prepared from timed‐pregnant Sprague‐Dawley rats. We perturbed cultured hippocampal networks with increasing concentrations of bath‐applied nicotine and performed network extracellular recordings of action potentials using a microelectrode array. We found that nicotine modulated network dynamics in a concentration‐dependent manner; it enhanced firing of action potentials as well as facilitated bursting activity. In addition, we used pharmacological agents to determine the contributions of discrete nAChR subtypes to the observed network dynamics. We found that β4‐containing nAChRs are necessary for the observed increases in spiking, bursting, and synchrony, while the activation of α7 nAChRs augments nicotine‐mediated network potentiation but is not necessary for its manifestation. We also observed that antagonists of N‐methyl‐D‐aspartate receptors (NMDARs) and group I metabotropic glutamate receptors (mGluRs) partially blocked the effects of nicotine. Furthermore, nicotine exposure promoted autophosphorylation of Ca2+/calmodulin‐dependent kinase II (CaMKII) and serine 831 phosphorylation of the α‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionic acid receptor (AMPAR) subunit GluA1. These results suggest that nicotinic receptors induce potentiation and synchronization of hippocampal networks and glutamatergic synaptic transmission. Findings from this work highlight the impact of cholinergic signaling in generating network‐wide potentiation in the form of enhanced spiking and bursting dynamics that coincide with molecular correlates of memory such as increased phosphorylation of CaMKII and GluA1.
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Abstract Synapse clustering facilitates circuit integration, learning, and memory. Long-term potentiation (LTP) of mature neurons produces synapse enlargement balanced by fewer spines, raising the question of how clusters form despite this homeostatic regulation of total synaptic weight. Three-dimensional reconstruction from serial section electron microscopy (3DEM) revealed the shapes and distributions of smooth endoplasmic reticulum (SER) and polyribosomes, subcellular resources important for synapse enlargement and spine outgrowth. Compared to control stimulation, synapses were enlarged two hours after LTP on resource-rich spines containing polyribosomes (4% larger than control) or SER (15% larger). SER in spines shifted from a single tubule to complex spine apparatus after LTP. Negligible synapse enlargement (0.6%) occurred on resource-poor spines lacking SER and polyribosomes. Dendrites were divided into discrete synaptic clusters surrounded by asynaptic segments. Spine density was lowest in clusters having only resource-poor spines, especially following LTP. In contrast, resource-rich spines preserved neighboring resource-poor spines and formed larger clusters with elevated total synaptic weight following LTP. These clusters also had more shaft SER branches, which could sequester cargo locally to support synapse growth and spinogenesis. Thus, resources appear to be redistributed to synaptic clusters with LTP-related synapse enlargement while homeostatic regulation suppressed spine outgrowth in resource-poor synaptic clusters.
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INTRODUCTION The analysis of the human brain is a central goal of neuroscience, but for methodological reasons, research has focused on model organisms, the mouse in particular. Because substantial homology was found at the level of ion channels, transcriptional programs, and basic neuronal types, a strong similarity of neuronal circuits across species has also been assumed. However, a rigorous test of the configuration of local neuronal circuitry in mouse versus human—in particular, in the gray matter of the cerebral cortex—is missing. The about 1000-fold increase in number of neurons is the most obvious evolutionary change of neuronal network properties from mouse to human. Whether the structure of the local cortical circuitry has changed as well is, however, unclear. Recent data from transcriptomic analyses has indicated an increase in the proportion of inhibitory interneurons from mouse to human. But what the effect of such a change is on the circuit configurations found in the human cerebral cortex is not known. This is, however, of particular interest also to the study of neuropsychiatric disorders because in these, the alteration of inhibitory-to-excitatory synaptic balance has been identified as one possible mechanistic underpinning. RATIONALE We used recent methodological improvements in connectomics to acquire data from one macaque and two human individuals, using biopsies of the temporal, parietal, and frontal cortex. Human tissue was obtained from neurosurgical interventions related to tumor removal, in which access path tissue was harvested that was not primarily affected by the underlying disease. A key concern in the analysis of human patient tissue has been the relation to epilepsy surgery, when the underlying disease has required often year-long treatment with pharmaceuticals, plausibly altering synaptic connectivity. Therefore, the analysis of nonepileptic surgery tissue seemed of particular importance. We also included data from one macaque individual, who was not known to have any brain-related pathology. RESULTS We acquired three-dimensional electron microscopy data from temporal and frontal cortex of human and temporal and parietal cortex of macaque. From these, we obtained connectomic reconstructions and compared these with five connectomes from mouse cortex. On the basis of these data, we were able to determine the effect of the about 2.5-fold expansion of the interneuron pool in macaque and human cortex compared with that of mouse. Contrary to expectation, the inhibitory-to-excitatory synaptic balance on pyramidal neurons in macaque and human cortex was not substantially altered. Rather, the interneuron pool was selectively expanded for bipolar-type interneurons, which prefer the innervation of other interneurons, and which further increased their preference for interneuron innervation from mouse to human. These changes were each multifold, yielding in effect an about 10-fold expanded interneuron-to-interneuron network in the human cortex that is only sparsely present in mouse. The total amount of synaptic input to pyramidal neurons, however, did not change according to the threefold thickening of the cortex; rather, a modest increase from about 12,000 synaptic inputs in mouse to about 15,000 in human was found. CONCLUSION The principal cells of the cerebral cortex, pyramidal neurons, maintain almost constant inhibitory-to-excitatory input balance and total synaptic input across 100 million years of evolutionary divergence, which is particularly noteworthy with the concomitant 1000-fold expansion of the neuronal network size and the 2.5-fold increase of inhibitory interneurons from mouse to human. Rather, the key network change from mouse to human is an expansion of almost an order of magnitude of an interneuron-to-interneuron network that is virtually absent in mouse but constitutes a substantial part of the human cortical network. Whether this new network is primarily created through the expansion of existing neuronal types, or is related to the creation of new interneuron subtypes, requires further study. The discovery of this network component in human cortex encourages detailed analysis of its function in health and disease. Connectomic screening across mammalian species: Comparison of five mouse, two macaque, and two human connectomic datasets from the cerebral cortex. ( A ) Automated reconstructions of all neurons with their cell bodies in the volume shown, using random colors. The analyzed connectomes comprised a total of ~1.6 million synapses. Arrows indicate evolutionary divergence: the last common ancestor between human and mouse, approximately 100 million years ago, and the last common ancestor between human and macaque, about 20 million years ago. ( B ) Illustration of the about 10-fold expansion of the interneuron-to-interneuron network from mouse to human.more » « less
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- Journal Article:
- https://doi.org/10.1002/jmor.21459
