Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

Linking brain activity across scales with simultaneous opto- and electrophysiology

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.

Ultrastructural differences impact cilia shape and external exposure across cell classes in the visual cortex

A primary cilium is a membrane-bound extension from the cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. Primary cilia in the brain are less accessible than cilia on cultured cells or epithelial tissues because in the brain they protrude into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs) but were absent from oligodendrocytes and microglia. Ultrastructural comparisons revealed that the base of the cilium and the microtubule organization differed between neurons and glia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting that cilia are poised to encounter locally released signaling molecules. Our analysis indicated that synapse proximity is likely due to random encounters in the neuropil, with no evidence that cilia modulate synapse activity as would be expected in tetrapartite synapses. The observed cell class differences in proximity to synapses were largely due to differences in external cilia length. Many key structural features that differed between neuronal and glial cilia influenced both cilium placement and shape and, thus, exposure to processes and synapses outside the cilium. Together, the ultrastructure both within and around neuronal and glial cilia suggest differences in cilia formation and function across cell types in the brain.

Dynamics of synaptic damage in severe traumatic brain injury revealed by cerebrospinal fluid SNAP-25 and VILIP-1

BACKGROUND

Biomarkers of neuronal, glial cells and inflammation in traumatic brain injury (TBI) are available but they do not specifically reflect the damage to synapses, which represent the bulk volume of the brain. Experimental models have demonstrated extensive involvement of synapses in acute TBI, but biomarkers of synaptic damage in human patients have not been explored.

METHODS

Single-molecule array assays were used to measure synaptosomal-associated protein-25 (SNAP-25) and visinin-like protein 1 (VILIP-1) (along with neurofilament light chain (NFL), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), glial fibrillar acidic protein (GFAP), interleukin-6 (IL-6) and interleukin-8 (IL-8)) in ventricular cerebrospinal fluid (CSF) samples longitudinally acquired during the intensive care unit (ICU) stay of 42 patients with severe TBI or 22 uninjured controls.

RESULTS

CSF levels of SNAP-25 and VILIP-1 are strongly elevated early after severe TBI and decline in the first few days. SNAP-25 and VILIP-1 correlate with inflammatory markers at two distinct timepoints (around D1 and then again at D5) in follow-up. SNAP-25 and VILIP-1 on the day-of-injury have better sensitivity and specificity for unfavourable outcome at 6 months than NFL, UCH-L1 or GFAP. Later elevation of SNAP-25 was associated with poorer outcome.

CONCLUSION

Synaptic damage markers are acutely elevated in severe TBI and predict long-term outcomes, as well as, or better than, markers of neuroaxonal injury. Synaptic damage correlates with initial injury and with a later phase of secondary inflammatory injury.

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.

Electromagnetic Modeling within a Microscopically Realistic Brain - Implications for Brain Stimulation

Across all domains of brain stimulation (neuromodulation), conventional analysis of neuron activation involves two discrete steps: i) prediction of macroscopic electric field, ignoring presence of cells and; ii) prediction of cell activation from tissue electric fields. The first step assumes that current flow is not distorted by the dense tortuous network of cell structures. The deficiencies of this assumption have long been recognized, but - except for trivial geometries - ignored, because it presented intractable computation hurdles. This study introduces a novel approach for analyzing electric fields within a microscopically realistic brain volume. Our pipeline overcomes the technical intractability that prevented such analysis while also showing significant implications for brain stimulation. Contrary to the standard finite element method (FEM), we suggest using a nested iterative boundary element method (BEM) coupled with the fast multipole method (FMM). The nested iterative BEM-FMM approach allows for solving problems with multiple length scales efficiently. It could potentially handle any number of electromagnetically coupled closely spaced neurons and blood microcapillaries. A target application is a subvolume of the L2/3 P36 mouse primary visual cortex containing approximately 400 detailed neuronal meshes at a resolution of 100 nm, which is obtained from scanning electron microscopy data. Our immediate result is a reduction of the stimulation field strength necessary for neuron activation by a factor of 0.85-0.55 (by 15%-45%) as compared to macroscopic predictions. This is in line with experimental data stating that existing macroscopic theories substantially overestimate electric field levels necessary for brain stimulation.

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

Synaptic architecture of a memory engram in the mouse hippocampus

Memory engrams are formed through experience-dependent remodeling of neural circuits, but their detailed architectures have remained unresolved. Using 3D electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway following chemogenetic labeling of cellular ensembles with a remote history of correlated excitation during associative learning. Projection neurons involved in memory acquisition expanded their connectomes via multi-synaptic boutons without altering the numbers and spatial arrangements of individual axonal terminals and dendritic spines. This expansion was driven by presynaptic activity elicited by specific negative valence stimuli, regardless of the co-activation state of postsynaptic partners. The rewiring of initial ensembles representing an engram coincided with local, input-specific changes in the shapes and organelle composition of glutamatergic synapses, reflecting their weights and potential for further modifications. Our findings challenge the view that the connectivity among neuronal substrates of memory traces is governed by Hebbian mechanisms, and offer a structural basis for representational drifts.

Directed and acyclic synaptic connectivity in the human layer 2-3 cortical microcircuit

The computational capabilities of neuronal networks are fundamentally constrained by their specific connectivity. Previous studies of cortical connectivity have mostly been carried out in rodents; whether the principles established therein also apply to the evolutionarily expanded human cortex is unclear. We studied network properties within the human temporal cortex using samples obtained from brain surgery. We analyzed multineuron patch-clamp recordings in layer 2-3 pyramidal neurons and identified substantial differences compared with rodents. Reciprocity showed random distribution, synaptic strength was independent from connection probability, and connectivity of the supragranular temporal cortex followed a directed and mostly acyclic graph topology. Application of these principles in neuronal models increased dimensionality of network dynamics, suggesting a critical role for cortical computation.

Multidimensional analysis of cortical interneuron synaptic features reveals underlying synaptic heterogeneity

Cortical interneurons represent a diverse set of neuronal subtypes characterized in part by their striking degree of synaptic specificity. However, little is known about the extent of synaptic diversity because of the lack of unbiased methods to extract synaptic features among interneuron subtypes. Here, we develop an approach to aggregate image features from fluorescent confocal images of interneuron synapses and their post-synaptic targets, in order to characterize the heterogeneity of synapses at fine scale. We started by training a model that recognizes pre- and post-synaptic compartments and then determines the target of each genetically-identified interneuron synapse in vitro and in vivo. Our model extracts hundreds of spatial and intensity features from each analyzed synapse, constructing a multidimensional data set, consisting of millions of synapses, which allowed us to perform an unsupervised analysis on this dataset, uncovering novel synaptic subgroups. The subgroups were spatially distributed in a highly structured manner that revealed the local underlying topology of the postsynaptic environment. Dendrite-targeting subgroups were clustered onto subdomains of the dendrite along the proximal to distal axis. Soma-targeting subgroups were enriched onto different postsynaptic cell types. We also find that the two main subclasses of interneurons, basket cells and somatostatin interneurons, utilize distinct strategies to enact inhibitory coverage. Thus, our analysis of multidimensional synaptic features establishes a conceptual framework for studying interneuron synaptic diversity.

Unambiguous identification of asymmetric and symmetric synapses using volume electron microscopy

The brain contains thousands of millions of synapses, exhibiting diverse structural, molecular, and functional characteristics. However, synapses can be classified into two primary morphological types: Gray's type I and type II, corresponding to Colonnier's asymmetric (AS) and symmetric (SS) synapses, respectively. AS and SS have a thick and thin postsynaptic density, respectively. In the cerebral cortex, since most AS are excitatory (glutamatergic), and SS are inhibitory (GABAergic), determining the distribution, size, density, and proportion of the two major cortical types of synapses is critical, not only to better understand synaptic organization in terms of connectivity, but also from a functional perspective. However, several technical challenges complicate the study of synapses. Potassium ferrocyanide has been utilized in recent volume electron microscope studies to enhance electron density in cellular membranes. However, identifying synaptic junctions, especially SS, becomes more challenging as the postsynaptic densities become thinner with increasing concentrations of potassium ferrocyanide. Here we describe a protocol employing Focused Ion Beam Milling and Scanning Electron Microscopy for studying brain tissue. The focus is on the unequivocal identification of AS and SS types. To validate SS observed using this protocol as GABAergic, experiments with immunocytochemistry for the vesicular GABA transporter were conducted on fixed mouse brain tissue sections. This material was processed with different concentrations of potassium ferrocyanide, aiming to determine its optimal concentration. We demonstrate that using a low concentration of potassium ferrocyanide (0.1%) improves membrane visualization while allowing unequivocal identification of synapses as AS or SS.

Activity-dependent compartmentalization of dendritic mitochondria morphology through local regulation of fusion-fission balance in neurons in vivo

Neuronal mitochondria play important roles beyond ATP generation, including Ca2+ uptake, and therefore have instructive roles in synaptic function and neuronal response properties. Mitochondrial morphology differs significantly between the axon and dendrites of a given neuronal subtype, but in CA1 pyramidal neurons (PNs) of the hippocampus, mitochondria within the dendritic arbor also display a remarkable degree of subcellular, layer-specific compartmentalization. In the dendrites of these neurons, mitochondria morphology ranges from highly fused and elongated in the apical tuft, to more fragmented in the apical oblique and basal dendritic compartments, and thus occupy a smaller fraction of dendritic volume than in the apical tuft. However, the molecular mechanisms underlying this striking degree of subcellular compartmentalization of mitochondria morphology are unknown, precluding the assessment of its impact on neuronal function. Here, we demonstrate that this compartment-specific morphology of dendritic mitochondria requires activity-dependent, Ca2+ and Camkk2-dependent activation of AMPK and its ability to phosphorylate two direct effectors: the pro-fission Drp1 receptor Mff and the recently identified anti-fusion, Opa1-inhibiting protein, Mtfr1l. Our study uncovers a signaling pathway underlying the subcellular compartmentalization of mitochondrial morphology in dendrites of neurons in vivo through spatially precise and activity-dependent regulation of mitochondria fission/fusion balance.

Ultrastructural analysis reveals mitochondrial placement independent of synapse placement in fine caliber C. elegans neurons

Neurons rely on mitochondria for an efficient supply of ATP and other metabolites. However, while neurons are highly elongated, mitochondria are discrete and limited in number. Due to the slow rates of diffusion over long distances it follows that neurons would benefit from an ability to control the distribution of mitochondria to sites of high metabolic activity, such as synapses. It is assumed that neurons' possess this capacity, but ultrastructural data over substantial portions of a neuron's extent that would allow for tests of such hypotheses are scarce. Here, we mined the Caenorhabditis elegans electron micrographs of John White and Sydney Brenner and found systematic differences in average mitochondrial length (ranging from 1.3 to 2.4 μm), volume density (3.7% to 6.5%) and diameter (0.18 to 0.24 μm) between neurons of different neurotransmitter type and function, but found limited differences in mitochondrial morphometrics between axons and dendrites of the same neurons. Analyses of distance intervals found mitochondria to be distributed randomly with respect to presynaptic specializations, and an indication that mitochondria were displaced from postsynaptic specializations. Presynaptic specializations were primarily localized to varicosities, but mitochondria were no more likely to be found in synaptic varicosities than non-synaptic varicosities. Consistently, mitochondrial volume density was no greater in varicosities with synapses. Therefore, beyond the capacity to disperse mitochondria throughout their length, at least in C. elegans, fine caliber neurons manifest limited sub-cellular control of mitochondrial size and distribution.

Synaptic wiring motifs in posterior parietal cortex support decision-making

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons

GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning, and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo, and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed, and make available several key data sets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The data set includes detailed scanning electron microscopy (SEM)-based 3D reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core data set with other anatomical data, patch-clamp electrophysiology, and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For example, we estimated that an OLM cell receives about 8,400, whereas an HS cell about 15,600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.

Network Statistics of the Whole-Brain Connectome of Drosophila

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Most axonal mitochondria in cortical pyramidal neurons lack mitochondrial DNA and consume ATP

In neurons of the mammalian central nervous system (CNS), axonal mitochondria are thought to be indispensable for supplying ATP during energy-consuming processes such as neurotransmitter release. Here, we demonstrate using multiple, independent, in vitro and in vivo approaches that the majority (~80-90%) of axonal mitochondria in cortical pyramidal neurons (CPNs), lack mitochondrial DNA (mtDNA). Using dynamic, optical imaging analysis of genetically encoded sensors for mitochondrial matrix ATP and pH, we demonstrate that in axons of CPNs, but not in their dendrites, mitochondrial complex V (ATP synthase) functions in a reverse way, consuming ATP and protruding H+ out of the matrix to maintain mitochondrial membrane potential. Our results demonstrate that in mammalian CPNs, axonal mitochondria do not play a major role in ATP supply, despite playing other functions critical to regulating neurotransmission such as Ca2+ buffering.

Astrocyte ensheathment of calyx-forming axons of the auditory brainstem precedes accelerated expression of myelin genes and myelination by oligodendrocytes

Early postnatal brain development involves complex interactions among maturing neurons and glial cells that drive tissue organization. We previously analyzed gene expression in tissue from the mouse medial nucleus of the trapezoid body (MNTB) during the first postnatal week to study changes that surround rapid growth of the large calyx of Held (CH) nerve terminal. Here, we present genes that show significant changes in gene expression level during the second postnatal week, a developmental timeframe that brackets the onset of airborne sound stimulation and the early stages of myelination. Gene Ontology analysis revealed that many of these genes are related to the myelination process. Further investigation of these genes using a previously published cell type-specific bulk RNA-Seq data set in cortex and our own single-cell RNA-Seq data set in the MNTB revealed enrichment of these genes in the oligodendrocyte lineage (OL) cells. Combining the postnatal day (P)6-P14 microarray gene expression data with the previously published P0-P6 data provided fine temporal resolution to investigate the initiation and subsequent waves of gene expression related to OL cell maturation and the process of myelination. Many genes showed increasing expression levels between P2 and P6 in patterns that reflect OL cell maturation. Correspondingly, the first myelin proteins were detected by P4. Using a complementary, developmental series of electron microscopy 3D image volumes, we analyzed the temporal progression of axon wrapping and myelination in the MNTB. By employing a combination of established ultrastructural criteria to classify reconstructed early postnatal glial cells in the 3D volumes, we demonstrated for the first time that astrocytes within the mouse MNTB extensively wrap the axons of the growing CH terminal prior to OL cell wrapping and compaction of myelin. Our data revealed significant expression of several myelin genes and enrichment of multiple genes associated with lipid metabolism in astrocytes, which may subserve axon wrapping in addition to myelin formation. The transition from axon wrapping by astrocytes to OL cells occurs rapidly between P4 and P9 and identifies a potential new role of astrocytes in priming calyceal axons for subsequent myelination.

Petascale pipeline for precise alignment of images from serial section electron microscopy

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

Synaptic configuration and reconfiguration in the neocortex are spatiotemporally selective

Brain computation relies on the neural networks. Neurons extend the neurites such as dendrites and axons, and the contacts of these neurites that form chemical synapses are the biological basis of signal transmissions in the central nervous system. Individual neuronal outputs can influence the other neurons within the range of the axonal spread, while the activities of single neurons can be affected by the afferents in their somatodendritic fields. The morphological profile, therefore, binds the functional role each neuron can play. In addition, synaptic connectivity among neurons displays preference based on the characteristics of presynaptic and postsynaptic neurons. Here, the author reviews the "spatial" and "temporal" connection selectivity in the neocortex. The histological description of the neocortical circuitry depends primarily on the classification of cell types, and the development of gene engineering techniques allows the cell type-specific visualization of dendrites and axons as well as somata. Using genetic labeling of particular cell populations combined with immunohistochemistry and imaging at a subcellular spatial resolution, we revealed the "spatial selectivity" of cortical wirings in which synapses are non-uniformly distributed on the subcellular somatodendritic domains in a presynaptic cell type-specific manner. In addition, cortical synaptic dynamics in learning exhibit presynaptic cell type-dependent "temporal selectivity": corticocortical synapses appear only transiently during the learning phase, while learning-induced new thalamocortical synapses persist, indicating that distinct circuits may supervise learning-specific ephemeral synapse and memory-specific immortal synapse formation. The selectivity of spatial configuration and temporal reconfiguration in the neural circuitry may govern diverse functions in the neocortex.

Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines

Postsynaptic mitochondria are critical for the development, plasticity, and maintenance of synaptic inputs. However, their relationship to synaptic structure and functional activity is unknown. We examined a correlative dataset from ferret visual cortex with in vivo two-photon calcium imaging of dendritic spines during visual stimulation and electron microscopy reconstructions of spine ultrastructure, investigating mitochondrial abundance near functionally and structurally characterized spines. Surprisingly, we found no correlation to structural measures of synaptic strength. Instead, we found that mitochondria are positioned near spines with orientation preferences that are dissimilar to the somatic preference. Additionally, we found that mitochondria are positioned near groups of spines with heterogeneous orientation preferences. For a subset of spines with a mitochondrion in the head or neck, synapses were larger and exhibited greater selectivity to visual stimuli than those without a mitochondrion. Our data suggest mitochondria are not necessarily positioned to support the energy needs of strong spines, but rather support the structurally and functionally diverse inputs innervating the basal dendrites of cortical neurons.

Isochronic development of cortical synapses in primates and mice

The neotenous, or delayed, development of primate neurons, particularly human ones, is thought to underlie primate-specific abilities like cognition. We tested whether synaptic development follows suit-would synapses, in absolute time, develop slower in longer-lived, highly cognitive species like non-human primates than in shorter-lived species with less human-like cognitive abilities, e.g., the mouse? Instead, we find that excitatory and inhibitory synapses in the male Mus musculus (mouse) and Rhesus macaque (primate) cortex form at similar rates, at similar times after birth. Primate excitatory and inhibitory synapses and mouse excitatory synapses also prune in such an isochronic fashion. Mouse inhibitory synapses are the lone exception, which are not pruned and instead continuously added throughout life. The monotony of synaptic development clocks across species with disparate lifespans, experiences, and cognitive abilities argues that such programs are likely orchestrated by genetic events rather than experience.

Whole-brain Optical Imaging: A Powerful Tool for Precise Brain Mapping at the Mesoscopic Level

The mammalian brain is a highly complex network that consists of millions to billions of densely-interconnected neurons. Precise dissection of neural circuits at the mesoscopic level can provide important structural information for understanding the brain. Optical approaches can achieve submicron lateral resolution and achieve "optical sectioning" by a variety of means, which has the natural advantage of allowing the observation of neural circuits at the mesoscopic level. Automated whole-brain optical imaging methods based on tissue clearing or histological sectioning surpass the limitation of optical imaging depth in biological tissues and can provide delicate structural information in a large volume of tissues. Combined with various fluorescent labeling techniques, whole-brain optical imaging methods have shown great potential in the brain-wide quantitative profiling of cells, circuits, and blood vessels. In this review, we summarize the principles and implementations of various whole-brain optical imaging methods and provide some concepts regarding their future development.

Multi-layered maps of neuropil with segmentation-guided contrastive learning

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.

Topology recapitulates morphogenesis of neuronal dendrites

Branching allows neurons to make synaptic contacts with large numbers of other neurons, facilitating the high connectivity of nervous systems. Neuronal arbors have geometric properties such as branch lengths and diameters that are optimal in that they maximize signaling speeds while minimizing construction costs. In this work, we asked whether neuronal arbors have topological properties that may also optimize their growth or function. We discovered that for a wide range of invertebrate and vertebrate neurons the distributions of their subtree sizes follow power laws, implying that they are scale invariant. The power-law exponent distinguishes different neuronal cell types. Postsynaptic spines and branchlets perturb scale invariance. Through simulations, we show that the subtree-size distribution depends on the symmetry of the branching rules governing arbor growth and that optimal morphologies are scale invariant. Thus, the subtree-size distribution is a topological property that recapitulates the functional morphology of dendrites.

Nanometer-scale views of visual cortex reveal anatomical features of primary cilia poised to detect synaptic spillover

A primary cilium is a thin membrane-bound extension off a cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. While many cell types have a primary cilium, little is known about primary cilia in the brain, where they are less accessible than cilia on cultured cells or epithelial tissues and protrude from cell bodies into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs), but were absent from oligodendrocytes and microglia. Structural comparisons revealed that the membrane structure at the base of the cilium and the microtubule organization differed between neurons and glia. OPC cilia were distinct in that they were the shortest and contained pervasive internal vesicles only occasionally observed in neuron and astrocyte cilia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting cilia are well poised to encounter locally released signaling molecules. Cilia proximity to synapses was random, not enriched, in the synapse-rich neuropil. The internal anatomy, including microtubule changes and centriole location, defined key structural features including cilium placement and shape. Together, the anatomical insights both within and around neuron and glia cilia provide new insights into cilia formation and function across cell types in the brain.

A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus

Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-β (Aβ) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aβ, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aβ and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aβ or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aβ plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.

Vimo - Visual Analysis of Neuronal Connectivity Motifs

Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.

A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension

The immense scale and complexity of neuronal electron microscopy (EM) datasets pose significant challenges in data processing, validation, and interpretation, necessitating the development of efficient, automated, and scalable error-detection methodologies. This paper proposes a novel approach that employs mesh processing techniques to identify potential error locations near neuronal tips. Error detection at tips is a particularly important challenge since these errors usually indicate that many synapses are falsely split from their parent neuron, injuring the integrity of the connectomic reconstruction. Additionally, we draw implications and results from an implementation of this error detection in a semi-automated proofreading pipeline. Manual proofreading is a laborious, costly, and currently necessary method for identifying the errors in the machine learning based segmentation of neural tissue. This approach streamlines the process of proofreading by systematically highlighting areas likely to contain inaccuracies and guiding proofreaders towards potential continuations, accelerating the rate at which errors are corrected.

Hippocampal GABAergic interneurons and memory

One of the most captivating questions in neuroscience revolves around the brain's ability to efficiently and durably capture and store information. It must process continuous input from sensory organs while also encoding memories that can persist throughout a lifetime. What are the cellular-, subcellular-, and network-level mechanisms that underlie this remarkable capacity for long-term information storage? Furthermore, what contributions do distinct types of GABAergic interneurons make to this process? As the hippocampus plays a pivotal role in memory, our review focuses on three aspects: (1) delineation of hippocampal interneuron types and their connectivity, (2) interneuron plasticity, and (3) activity patterns of interneurons during memory-related rhythms, including the role of long-range interneurons and disinhibition. We explore how these three elements, together showcasing the remarkable diversity of inhibitory circuits, shape the processing of memories in the hippocampus.

Volume electron microscopy reveals human retinal mitochondria that align with reflective bands in optical coherence tomography [Invited]

Mitochondria are candidate reflectivity signal sources in optical coherence tomography (OCT) retinal imaging. Here, we use deep-learning-assisted volume electron microscopy of human retina and in vivo imaging to map mitochondria networks in the outer plexiform layer (OPL), where photoreceptors synapse with second-order interneurons. We observed alternating layers of high and low mitochondrial abundance in the anatomical OPL and adjacent inner nuclear layer (INL). Subcellular resolution OCT imaging of human eyes revealed multiple reflective bands that matched the corresponding INL and combined OPL sublayers. Data linking specific mitochondria to defined bands in OCT may help improve clinical diagnosis and the evaluation of mitochondria-targeting therapies.

Probing neural circuit mechanisms in Alzheimer's disease using novel technologies

The study of Alzheimer's Disease (AD) has traditionally focused on neuropathological mechanisms that has guided therapies that attenuate neuropathological features. A new direction is emerging in AD research that focuses on the progressive loss of cognitive function due to disrupted neural circuit mechanisms. Evidence from humans and animal models of AD show that dysregulated circuits initiate a cascade of pathological events that culminate in functional loss of learning, memory, and other aspects of cognition. Recent progress in single-cell, spatial, and circuit omics informs this circuit-focused approach by determining the identities, locations, and circuitry of the specific cells affected by AD. Recently developed neuroscience tools allow for precise access to cell type-specific circuitry so that their functional roles in AD-related cognitive deficits and disease progression can be tested. An integrated systems-level understanding of AD-associated neural circuit mechanisms requires new multimodal and multi-scale interrogations that longitudinally measure and/or manipulate the ensemble properties of specific molecularly-defined neuron populations first susceptible to AD. These newly developed technological and conceptual advances present new opportunities for studying and treating circuits vulnerable in AD and represent the beginning of a new era for circuit-based AD research.

A Scalable Staining Strategy for Whole-Brain Connectomics

Mapping the complete synaptic connectivity of a mammalian brain would be transformative, revealing the pathways underlying perception, behavior, and memory. Serial section electron microscopy, via membrane staining using osmium tetroxide, is ideal for visualizing cells and synaptic connections but, in whole brain samples, faces significant challenges related to chemical treatment and volume changes. These issues can adversely affect both the ultrastructural quality and macroscopic tissue integrity. By leveraging time-lapse X-ray imaging and brain proxies, we have developed a 12-step protocol, ODeCO, that effectively infiltrates osmium throughout an entire mouse brain while preserving ultrastructure without any cracks or fragmentation, a necessary prerequisite for constructing the first comprehensive mouse brain connectome.

Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines

Postsynaptic mitochondria are critical to the development, plasticity, and maintenance of synaptic inputs. However, their relationship to synaptic structure and functional activity is unknown. We examined a correlative dataset from ferret visual cortex with in vivo two-photon calcium imaging of dendritic spines during visual stimulation and electron microscopy (EM) reconstructions of spine ultrastructure, investigating mitochondrial abundance near functionally- and structurally-characterized spines. Surprisingly, we found no correlation to structural measures of synaptic strength. Instead, we found that mitochondria are positioned near spines with orientation preferences that are dissimilar to the somatic preference. Additionally, we found that mitochondria are positioned near groups of spines with heterogeneous orientation preferences. For a subset of spines with mitochondrion in the head or neck, synapses were larger and exhibited greater selectivity to visual stimuli than those without a mitochondrion. Our data suggest mitochondria are not necessarily positioned to support the energy needs of strong spines, but rather support the structurally and functionally diverse inputs innervating the basal dendrites of cortical neurons.

Hierarchical Modular Structure of the Drosophila Connectome

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 female Drosophila 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.

Simulation-based inference for efficient identification of generative models in computational connectomics

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

Architectural organization of ∼1,500-neuron modular minicolumnar disinhibitory circuits in healthy and Alzheimer's cortices

Acquisition of neuronal circuit architectures, central to understanding brain function and dysfunction, remains prohibitively challenging. Here I report the development of a simultaneous and sequential octuple-sexdecuple whole-cell patch-clamp recording system that enables architectural reconstruction of complex cortical circuits. The method unveils the canonical layer 1 single bouquet cell (SBC)-led disinhibitory neuronal circuits across the mouse somatosensory, motor, prefrontal, and medial entorhinal cortices. The ∼1,500-neuron modular circuits feature the translaminar, unidirectional, minicolumnar, and independent disinhibition and optimize cortical complexity, subtlety, plasticity, variation, and redundancy. Moreover, architectural reconstruction uncovers age-dependent deficits at SBC-disinhibited synapses in the senescence-accelerated mouse prone 8, an animal model of Alzheimer's disease. The deficits exhibit the characteristic Alzheimer's-like cortical spread and correlation with cognitive impairments. These findings decrypt operations of the elementary processing units in healthy and Alzheimer's mouse cortices and validate the efficacy of octuple-sexdecuple patch-clamp recordings for architectural reconstruction of complex neuronal circuits.

Immersion Fixation and Staining of Multicubic Millimeter Volumes for Electron Microscopy-Based Connectomics of Human Brain Biopsies

Connectomics allows mapping of cells and their circuits at the nanometer scale in volumes of approximately 1 mm3. Given that the human cerebral cortex can be 3 mm in thickness, larger volumes are required. Larger-volume circuit reconstructions of human brain are limited by 1) the availability of fresh biopsies; 2) the need for excellent preservation of ultrastructure, including extracellular space; and 3) the requirement of uniform staining throughout the sample, among other technical challenges. Cerebral cortical samples from neurosurgical patients are available owing to lead placement for deep brain stimulation. Described here is an immersion fixation, heavy metal staining, and tissue processing method that consistently provides excellent ultrastructure throughout human and rodent surgical brain samples of volumes 2 × 2 × 2 mm3 and up to 37 mm3 with one dimension ≤2 mm. This method should allow synapse-level circuit analysis in samples from patients with psychiatric and neurologic disorders.

How to incorporate biological insights into network models and why it matters

Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.

Dense 4D nanoscale reconstruction of living brain tissue

Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure-function relationships of the brain's complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

Astrocytes as master modulators of neural networks: Synaptic functions and disease-associated dysfunction of astrocytes

Astrocytes are the most abundant glial cell type in the central nervous system and are essential to the development, plasticity, and maintenance of neural circuits. Astrocytes are heterogeneous, with their diversity rooted in developmental programs modulated by the local brain environment. Astrocytes play integral roles in regulating and coordinating neural activity extending far beyond their metabolic support of neurons and other brain cell phenotypes. Both gray and white matter astrocytes occupy critical functional niches capable of modulating brain physiology on time scales slower than synaptic activity but faster than those adaptive responses requiring a structural change or adaptive myelination. Given their many associations and functional roles, it is not surprising that astrocytic dysfunction has been causally implicated in a broad set of neurodegenerative and neuropsychiatric disorders. In this review, we focus on recent discoveries concerning the contributions of astrocytes to the function of neural networks, with a dual focus on the contribution of astrocytes to synaptic development and maturation, and on their role in supporting myelin integrity, and hence conduction and its regulation. We then address the emerging roles of astrocytic dysfunction in disease pathogenesis and on potential strategies for targeting these cells for therapeutic purposes.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

High-resolution volumetric imaging constrains compartmental models to explore synaptic integration and temporal processing by cochlear nucleus globular bushy cells

Globular bushy cells (GBCs) of the cochlear nucleus play central roles in the temporal processing of sound. Despite investigation over many decades, fundamental questions remain about their dendrite structure, afferent innervation, and integration of synaptic inputs. Here, we use volume electron microscopy (EM) of the mouse cochlear nucleus to construct synaptic maps that precisely specify convergence ratios and synaptic weights for auditory nerve innervation and accurate surface areas of all postsynaptic compartments. Detailed biophysically based compartmental models can help develop hypotheses regarding how GBCs integrate inputs to yield their recorded responses to sound. We established a pipeline to export a precise reconstruction of auditory nerve axons and their endbulb terminals together with high-resolution dendrite, soma, and axon reconstructions into biophysically detailed compartmental models that could be activated by a standard cochlear transduction model. With these constraints, the models predict auditory nerve input profiles whereby all endbulbs onto a GBC are subthreshold (coincidence detection mode), or one or two inputs are suprathreshold (mixed mode). The models also predict the relative importance of dendrite geometry, soma size, and axon initial segment length in setting action potential threshold and generating heterogeneity in sound-evoked responses, and thereby propose mechanisms by which GBCs may homeostatically adjust their excitability. Volume EM also reveals new dendritic structures and dendrites that lack innervation. This framework defines a pathway from subcellular morphology to synaptic connectivity, and facilitates investigation into the roles of specific cellular features in sound encoding. We also clarify the need for new experimental measurements to provide missing cellular parameters, and predict responses to sound for further in vivo studies, thereby serving as a template for investigation of other neuron classes.

Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit

Neuronal wiring diagrams reconstructed by electron microscopy1,2,3,4,5 pose new questions about the organization of nervous systems following the time-honored tradition of cross-species comparisons.6,7 The C. elegans connectome has been conceptualized as a sensorimotor circuit that is approximately feedforward,8,9,10,11 starting from sensory neurons proceeding to interneurons and ending with motor neurons. Overrepresentation of a 3-cell motif often known as the "feedforward loop" has provided further evidence for feedforwardness.10,12 Here, we contrast with another sensorimotor wiring diagram that was recently reconstructed from a larval zebrafish brainstem.13 We show that the 3-cycle, another 3-cell motif, is highly overrepresented in the oculomotor module of this wiring diagram. This is a first for any neuronal wiring diagram reconstructed by electron microscopy, whether invertebrate12,14 or mammalian.15,16,17 The 3-cycle of cells is "aligned" with a 3-cycle of neuronal groups in a stochastic block model (SBM)18 of the oculomotor module. However, the cellular cycles exhibit more specificity than can be explained by the group cycles-recurrence to the same neuron is surprisingly common. Cyclic structure could be relevant for theories of oculomotor function that depend on recurrent connectivity. The cyclic structure coexists with the classic vestibulo-ocular reflex arc for horizontal eye movements,19 and could be relevant for recurrent network models of temporal integration by the oculomotor system.20,21.

A conditional strategy for cell-type-specific labeling of endogenous excitatory synapses in Drosophila

Chemical neurotransmission occurs at specialized contacts where neurotransmitter release machinery apposes neurotransmitter receptors to underlie circuit function. A series of complex events underlies pre- and postsynaptic protein recruitment to neuronal connections. To better study synaptic development in individual neurons, we need cell-type-specific strategies to visualize endogenous synaptic proteins. Although presynaptic strategies exist, postsynaptic proteins remain less studied because of a paucity of cell-type-specific reagents. To study excitatory postsynapses with cell-type specificity, we engineered dlg1[4K], a conditionally labeled marker of Drosophila excitatory postsynaptic densities. With binary expression systems, dlg1[4K] labels central and peripheral postsynapses in larvae and adults. Using dlg1[4K], we find that distinct rules govern postsynaptic organization in adult neurons, multiple binary expression systems can concurrently label pre- and postsynapse in a cell-type-specific manner, and neuronal DLG1 can sometimes localize presynaptically. These results validate our strategy for conditional postsynaptic labeling and demonstrate principles of synaptic organization.

mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops

Connectomics is fundamental in propelling our understanding of the nervous system’s organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/ . With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Activity-dependent subcellular compartmentalization of dendritic mitochondria structure in CA1 pyramidal neurons

Neuronal mitochondria play important roles beyond ATP generation, including Ca2+ uptake, and therefore have instructive roles in synaptic function and neuronal response properties. Mitochondrial morphology differs significantly in the axon and dendrites of a given neuronal subtype, but in CA1 pyramidal neurons (PNs) of the hippocampus, mitochondria within the dendritic arbor also display a remarkable degree of subcellular, layer-specific compartmentalization. In the dendrites of these neurons, mitochondria morphology ranges from highly fused and elongated in the apical tuft, to more fragmented in the apical oblique and basal dendritic compartments, and thus occupy a smaller fraction of dendritic volume than in the apical tuft. However, the molecular mechanisms underlying this striking degree of subcellular compartmentalization of mitochondria morphology are unknown, precluding the assessment of its impact on neuronal function. Here, we demonstrate that this compartment-specific morphology of dendritic mitochondria requires activity-dependent, Camkk2-dependent activation of AMPK and its ability to phosphorylate two direct effectors: the pro-fission Drp1 receptor Mff and the recently identified anti-fusion, Opa1-inhibiting protein, Mtfr1l. Our study uncovers a new activity-dependent molecular mechanism underlying the extreme subcellular compartmentalization of mitochondrial morphology in dendrites of neurons in vivo through spatially precise regulation of mitochondria fission/fusion balance.