Structure and function of axo-axonic inhibition

Perisomatic ultrastructure efficiently classifies cells in mouse cortex

Mammalian neocortex contains a highly diverse set of cell types. These cell types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches1-4. Each modality offers new perspectives on the variation of biological processes underlying cell-type specialization. Cellular-scale electron microscopy provides dense ultrastructural examination and an unbiased perspective on the subcellular organization of brain cells, including their synaptic connectivity and nanometre-scale morphology. In data that contain tens of thousands of neurons, most of which have incomplete reconstructions, identifying cell types becomes a clear challenge for analysis5. Here, to address this challenge, we present a systematic survey of the somatic region of all cells in a cubic millimetre of cortex using quantitative features obtained from electron microscopy. This analysis demonstrates that the perisomatic region is sufficient to identify cell types, including types defined primarily on the basis of their connectivity patterns. We then describe how this classification facilitates cell-type-specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

NEURD offers automated proofreading and feature extraction for connectomics

We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution1,2. Dense reconstruction of cellular compartments in these electron microscopy volumes has been enabled by recent advances in machine learning3-6. Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post hoc proofreading is still required to generate large connectomes that are free of merge and split errors. The elaborate 3D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Here, building on existing open source software for mesh manipulation, we present Neural Decomposition (NEURD), a software package that decomposes meshed neurons into compact and extensively annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state-of-the-art automated proofreading of merge errors, cell classification, spine detection, axonal-dendritic proximities and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers.

The MICrONS Consortium, Bae JA, Baptiste M, Baptiste MR, Bishop CA, Bodor AL, Brittain D, Brooks V, Buchanan J, Bumbarger DJ, Castro MA, Celii B, Cobos E, Collman F, da Costa NM, Danskin B, Dorkenwald S, Elabbady L, Fahey PG, Fliss T, Froudarakis E, Gager J, Gamlin C, Gray-Roncal W, Halageri A, Hebditch J, Jia Z, Joyce E, Ellis-Joyce J, Jordan C, Kapner D, Kemnitz N, Kinn S, Kitchell LM, Koolman S, Kuehner K, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Matelsky J, McReynolds S, Miranda E, Mitchell E, Mondal SS, Moore M, Mu S, Muhammad T, Nehoran B, Neace E, Ogedengbe O, Papadopoulos C, Papadopoulos S, Patel S, Vega GJYP, Pitkow X, Popovych S, Ramos A, Reid RC, Reimer J, Rivlin PK, Rose V, Sauter ZM, Schneider-Mizell CM, Seung HS, Silverman B, Silversmith W, Sterling A, Sinz FH, Smith CL, Swanstrom R, Suckow S, Takeno M, Tan ZH, Tolias AS, Torres R, Turner NL, Walker EY, Wang T, Wanner A, Wester BA, Williams G, Williams S, Willie K, Willie R, Wong W, Wu J, Xu C, Yang R, Yatsenko D, Ye F, Yin W, Young R, Yu SC, Xenes D, Zhang C. Functional connectomics spanning multiple areas of mouse visual cortex

Understanding the brain requires understanding neurons' functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis1,2. Accompanying studies describe its use for comprehensive characterization of cell types3-6, a synaptic level connectivity diagram of a cortical column4, and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data4,7. Functionally, we identify new computational principles of how information is integrated across visual space8, characterize novel types of neuronal invariances9 and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas10,11.

Integrating multimodal data to understand cortical circuit architecture and function

In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function.

Theory of axo-axonic inhibition

The axon initial segment of principal cells of the cortex and hippocampus is contacted by GABAergic interneurons called chandelier cells. The anatomy, as well as alterations in neurological diseases such as epilepsy, suggest that chandelier cells exert an important inhibitory control on action potential initiation. However, their functional role remains unclear, including whether their effect is indeed inhibitory or excitatory. One reason is that there is a relative gap in electrophysiological theory about the electrical effect of axo-axonic synapses. This contribution uses resistive coupling theory, a simplification of cable theory based on the observation that the small initial segment is resistively coupled to the large cell body acting as a current sink, to fill this gap. The main theoretical finding is that a synaptic input at the proximal axon shifts the action potential threshold by an amount equal to the product of synaptic conductance, driving force at threshold, and axial axonal resistance between the soma and either the synapse or of the middle of the initial segment, whichever is closer. The theory produces quantitative estimates useful to interpret experimental observations, and supports the idea that axo-axonic cells can potentially exert powerful inhibitory control on action potential initiation.

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. In recent years, manual model tuning has been gradually replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently, without any fine-tuning, found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. We also demonstrate the versatility of Neuroptimus by applying it to an additional use case that involves tuning the parameters of a subcellular model of biochemical pathways. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.

Specific and Plastic: Chandelier Cell-to-Axon Initial Segment Connections in Shaping Functional Cortical Network

Axon initial segment (AIS) is the most excitable subcellular domain of a neuron for action potential initiation. AISs of cortical projection neurons (PNs) receive GABAergic synaptic inputs primarily from chandelier cells (ChCs), which are believed to regulate action potential generation and modulate neuronal excitability. As individual ChCs often innervate hundreds of PNs, they may alter the activity of PN ensembles and even impact the entire neural network. During postnatal development or in response to changes in network activity, the AISs and axo-axonic synapses undergo dynamic structural and functional changes that underlie the wiring, refinement, and adaptation of cortical microcircuits. Here we briefly introduce the history of ChCs and review recent research advances employing modern genetic and molecular tools. Special attention will be attributed to the plasticity of the AIS and the ChC-PN connections, which play a pivotal role in shaping the dynamic network under both physiological and pathological conditions.

Impaired axon initial segment structure and function in a model of ARHGEF9 developmental and epileptic encephalopathy

Developmental and epileptic encephalopathies (DEE) are rare but devastating and largely intractable childhood epilepsies. Genetic variants in ARHGEF9, encoding a scaffolding protein important for the organization of the postsynaptic density of inhibitory synapses, are associated with DEE accompanied by complex neurological phenotypes. In a mouse model carrying a patient-derived ARHGEF9 variant associated with severe disease, we observed aggregation of postsynaptic proteins and loss of functional inhibitory synapses at the axon initial segment (AIS), altered axo-axonic synaptic inhibition, disrupted action potential generation, and complex seizure phenotypes consistent with clinical observations. These results illustrate diverse roles of ARHGEF9 that converge on regulation of the structure and function of the AIS, thus revealing a pathological mechanism for ARHGEF9-associated DEE. This unique example of a neuropathological condition associated with multiple AIS dysfunctions may inform strategies for treating neurodevelopmental diseases.

NEURD offers automated proofreading and feature extraction for connectomics

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Feedback inhibition by a descending GABAergic neuron regulates timing of escape behavior in Drosophila larvae

Escape behaviors help animals avoid harm from predators and other threats in the environment. Successful escape relies on integrating information from multiple stimulus modalities (of external or internal origin) to compute trajectories toward safe locations, choose between actions that satisfy competing motivations, and execute other strategies that ensure survival. To this end, escape behaviors must be adaptive. When a Drosophila melanogaster larva encounters a noxious stimulus, such as the focal pressure a parasitic wasp applies to the larval cuticle via its ovipositor, it initiates a characteristic escape response. The escape sequence consists of an initial abrupt bending, lateral rolling, and finally rapid crawling. Previous work has shown that the detection of noxious stimuli primarily relies on class IV multi-dendritic arborization neurons (Class IV neurons) located beneath the body wall, and more recent studies have identified several important components in the nociceptive neural circuitry involved in rolling. However, the neural mechanisms that underlie the rolling-escape sequence remain unclear. Here, we present both functional and anatomical evidence suggesting that bilateral descending neurons within the subesophageal zone of D. melanogaster larva play a crucial role in regulating the termination of rolling and subsequent transition to escape crawling. We demonstrate that these descending neurons (designated SeIN128) are inhibitory and receive inputs from a second-order interneuron upstream (Basin-2) and an ascending neuron downstream of Basin-2 (A00c). Together with optogenetic experiments showing that co-activation of SeIN128 neurons and Basin-2 influence the temporal dynamics of rolling, our findings collectively suggest that the ensemble of SeIN128, Basin-2, and A00c neurons forms a GABAergic feedback loop onto Basin-2, which inhibits rolling and thereby facilitates the shift to escape crawling.

Specific and comprehensive genetic targeting reveals brain-wide distribution and synaptic input patterns of GABAergic axo-axonic interneurons

Axo-axonic cells (AACs), also called chandelier cells (ChCs) in the cerebral cortex, are the most distinctive type of GABAergic interneurons described in the neocortex, hippocampus, and basolateral amygdala (BLA). AACs selectively innervate glutamatergic projection neurons (PNs) at their axon initial segment (AIS), thus may exert decisive control over PN spiking and regulate PN functional ensembles. However, the brain-wide distribution, synaptic connectivity, and circuit function of AACs remain poorly understood, largely due to the lack of specific and reliable experimental tools. Here, we have established an intersectional genetic strategy that achieves specific and comprehensive targeting of AACs throughout the mouse brain based on their lineage (Nkx2.1) and molecular (Unc5b, Pthlh) markers. We discovered that AACs are deployed across essentially all the pallium-derived brain structures, including not only the dorsal pallium-derived neocortex and medial pallium-derived hippocampal formation, but also the lateral pallium-derived claustrum-insular complex, and the ventral pallium-derived extended amygdaloid complex and olfactory centers. AACs are also abundant in anterior olfactory nucleus, taenia tecta, and lateral septum. AACs show characteristic variations in density across neocortical areas and layers and across subregions of the hippocampal formation. Neocortical AACs comprise multiple laminar subtypes with distinct dendritic and axonal arborization patterns. Retrograde monosynaptic tracing from AACs across neocortical, hippocampal, and BLA regions reveal shared as well as distinct patterns of synaptic input. Specific and comprehensive targeting of AACs facilitates the study of their developmental genetic program and circuit function across brain structures, providing a ground truth platform for understanding the conservation and variation of a bona fide cell type across brain regions and species.

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.

Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

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.

Neural and behavioural state switching during hippocampal dentate spikes

Distinct brain and behavioural states are associated with organized neural population dynamics that are thought to serve specific cognitive functions1-3. Memory replay events, for example, occur during synchronous population events called sharp-wave ripples in the hippocampus while mice are in an 'offline' behavioural state, enabling cognitive mechanisms such as memory consolidation and planning4-11. But how does the brain re-engage with the external world during this behavioural state and permit access to current sensory information or promote new memory formation? Here we found that the hippocampal dentate spike, an understudied population event that frequently occurs between sharp-wave ripples12, may underlie such a mechanism. We show that dentate spikes are associated with distinctly elevated brain-wide firing rates, primarily observed in higher order networks, and couple to brief periods of arousal. Hippocampal place coding during dentate spikes aligns to the mouse's current spatial location, unlike the memory replay accompanying sharp-wave ripples. Furthermore, inhibiting neural activity during dentate spikes disrupts associative memory formation. Thus, dentate spikes represent a distinct brain state and support memory during non-locomotor behaviour, extending the repertoire of cognitive processes beyond the classical offline functions.

Specific and comprehensive genetic targeting reveals brain-wide distribution and synaptic input patterns of GABAergic axo-axonic interneurons

Axo-axonic cells (AACs), also called chandelier cells (ChCs) in the cerebral cortex, are the most distinctive type of GABAergic interneurons described in the neocortex, hippocampus, and basolateral amygdala (BLA). AACs selectively innervate glutamatergic projection neurons (PNs) at their axon initial segment (AIS), thus may exert decisive control over PN spiking and regulate PN functional ensembles. However, the brain-wide distribution, synaptic connectivity, and circuit function of AACs remains poorly understood, largely due to the lack of specific and reliable experimental tools. Here, we have established an intersectional genetic strategy that achieves specific and comprehensive targeting of AACs throughout the mouse brain based on their lineage (Nkx2.1) and molecular (Unc5b, Pthlh) markers. We discovered that AACs are deployed across essentially all the pallium-derived brain structures, including not only the dorsal pallium-derived neocortex and medial pallium-derived hippocampal formation, but also the lateral pallium-derived claustrum-insular complex, and the ventral pallium-derived extended amygdaloid complex and olfactory centers. AACs are also abundant in anterior olfactory nucleus, taenia tecta and lateral septum. AACs show characteristic variations in density across neocortical areas and layers and across subregions of the hippocampal formation. Neocortical AACs comprise multiple laminar subtypes with distinct dendritic and axonal arborization patterns. Retrograde monosynaptic tracing from AACs across neocortical, hippocampal and BLA regions reveal shared as well as distinct patterns of synaptic input. Specific and comprehensive targeting of AACs facilitates the study of their developmental genetic program and circuit function across brain structures, providing a ground truth platform for understanding the conservation and variation of a bona fide cell type across brain regions and species.

CHRNA5 links chandelier cells to severity of amyloid pathology in aging and Alzheimer's disease

Changes in high-affinity nicotinic acetylcholine receptors are intricately connected to neuropathology in Alzheimer's Disease (AD). Protective and cognitive-enhancing roles for the nicotinic α5 subunit have been identified, but this gene has not been closely examined in the context of human aging and dementia. Therefore, we investigate the nicotinic α5 gene CHRNA5 and the impact of relevant single nucleotide polymorphisms (SNPs) in prefrontal cortex from 922 individuals with matched genotypic and post-mortem RNA sequencing in the Religious Orders Study and Memory and Aging Project (ROS/MAP). We find that a genotype robustly linked to increased expression of CHRNA5 (rs1979905A2) predicts significantly reduced cortical β-amyloid load. Intriguingly, co-expression analysis suggests CHRNA5 has a distinct cellular expression profile compared to other nicotinic receptor genes. Consistent with this prediction, single nucleus RNA sequencing from 22 individuals reveals CHRNA5 expression is disproportionately elevated in chandelier neurons, a distinct subtype of inhibitory neuron known for its role in excitatory/inhibitory (E/I) balance. We show that chandelier neurons are enriched in amyloid-binding proteins compared to basket cells, the other major subtype of PVALB-positive interneurons. Consistent with the hypothesis that nicotinic receptors in chandelier cells normally protect against β-amyloid, cell-type proportion analysis from 549 individuals reveals these neurons show amyloid-associated vulnerability only in individuals with impaired function/trafficking of nicotinic α5-containing receptors due to homozygosity of the missense CHRNA5 SNP (rs16969968A2). Taken together, these findings suggest that CHRNA5 and its nicotinic α5 subunit exert a neuroprotective role in aging and Alzheimer's disease centered on chandelier interneurons.

Experience shapes chandelier cell function and structure in the visual cortex

Detailed characterization of interneuron types in primary visual cortex (V1) has greatly contributed to understanding visual perception, yet the role of chandelier cells (ChCs) in visual processing remains poorly characterized. Using viral tracing we found that V1 ChCs predominantly receive monosynaptic input from local layer 5 pyramidal cells and higher-order cortical regions. Two-photon calcium imaging and convolutional neural network modeling revealed that ChCs are visually responsive but weakly selective for stimulus content. In mice running in a virtual tunnel, ChCs respond strongly to events known to elicit arousal, including locomotion and visuomotor mismatch. Repeated exposure of the mice to the virtual tunnel was accompanied by reduced visual responses of ChCs and structural plasticity of ChC boutons and axon initial segment length. Finally, ChCs only weakly inhibited pyramidal cells. These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity and/or gate plasticity of their axon initial segments during behaviorally relevant events.

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

Complementation testing identifies causal genes at quantitative trait loci underlying fear related behavior

Knowing the genes involved in quantitative traits provides a critical entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. Here we address a key step towards that goal by deploying a test that directly queries whether a gene mediates the effect of a quantitative trait locus (QTL). To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six QTLs, and identified six genes. Four genes, Lsamp, Ptprd, Nptx2 and Sh3gl, have known roles in synapse function; the fifth gene, Psip1, is a transcriptional co-activator not previously implicated in behavior; the sixth is a long non-coding RNA 4933413L06Rik with no known function. Single nucleus transcriptomic and epigenetic analyses implicated excitatory neurons as likely mediating the genetic effects. Surprisingly, variation in transcriptome and epigenetic modalities between inbred strains occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results open a bottleneck in using genetic mapping of QTLs to find novel biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

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.

Antibody-directed extracellular proximity biotinylation reveals that Contactin-1 regulates axo-axonic innervation of axon initial segments

Axon initial segment (AIS) cell surface proteins mediate key biological processes in neurons including action potential initiation and axo-axonic synapse formation. However, few AIS cell surface proteins have been identified. Here, we use antibody-directed proximity biotinylation to define the cell surface proteins in close proximity to the AIS cell adhesion molecule Neurofascin. To determine the distributions of the identified proteins, we use CRISPR-mediated genome editing for insertion of epitope tags in the endogenous proteins. We identify Contactin-1 (Cntn1) as an AIS cell surface protein. Cntn1 is enriched at the AIS through interactions with Neurofascin and NrCAM. We further show that Cntn1 contributes to assembly of the AIS extracellular matrix, and regulates AIS axo-axonic innervation by inhibitory basket cells in the cerebellum and inhibitory chandelier cells in the cortex.

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.

Axon Initial Segment GABA Inhibits Action Potential Generation throughout Periadolescent Development

Neurons are remarkably polarized structures: dendrites spread and branch to receive synaptic inputs while a single axon extends and transmits action potentials (APs) to downstream targets. Neuronal polarity is maintained by the axon initial segment (AIS), a region between the soma and axon proper that is also the site of action potential (AP) generation. This polarization between dendrites and axons extends to inhibitory neurotransmission. In adulthood, the neurotransmitter GABA hyperpolarizes dendrites but instead depolarizes axons. These differences in function collide at the AIS. Multiple studies have shown that GABAergic signaling in this region can share properties of either the mature axon or mature dendrite, and that these properties evolve over a protracted period encompassing periadolescent development. Here, we explored how developmental changes in GABAergic signaling affect AP initiation. We show that GABA at the axon initial segment inhibits action potential initiation in layer (L)2/3 pyramidal neurons in prefrontal cortex from mice of either sex across GABA reversal potentials observed in periadolescence. These actions occur largely through current shunts generated by GABAA receptors and changes in voltage-gated channel properties that affected the number of channels that could be recruited for AP electrogenesis. These results suggest that GABAergic neurons targeting the axon initial segment provide an inhibitory "veto" across the range of GABA polarity observed in normal adolescent development, regardless of GABAergic synapse reversal potential.Significance Statement GABA receptors are a major class of neurotransmitter receptors in the brain. Typically, GABA receptors inhibit neurons by allowing influx of negatively charged chloride ions into the cell. However, there are cases where local chloride concentrations promote chloride efflux through GABA receptors. Such conditions exist early in development in neocortical pyramidal cell axon initial segments (AISs), where action potentials (APs) initiate. Here, we examined how chloride efflux in early development interacts with mechanisms that support action potential initiation. We find that this efflux, despite moving membrane potential closer to action potential threshold, is nevertheless inhibitory. Thus, GABA at the axon initial segment is likely to be inhibitory for action potential initiation independent of whether chloride flows out or into neurons via these receptors.

Shared and divergent principles of synaptic transmission between cortical excitatory neurons in rodent and human brain

Information transfer between principal neurons in neocortex occurs through (glutamatergic) synaptic transmission. In this focussed review, we provide a detailed overview on the strength of synaptic neurotransmission between pairs of excitatory neurons in human and laboratory animals with a specific focus on data obtained using patch clamp electrophysiology. We reach two major conclusions: (1) the synaptic strength, measured as unitary excitatory postsynaptic potential (or uEPSP), is remarkably consistent across species, cortical regions, layers and/or cell-types (median 0.5 mV, interquartile range 0.4-1.0 mV) with most variability associated with the cell-type specific connection studied (min 0.1-max 1.4 mV), (2) synaptic function cannot be generalized across human and rodent, which we exemplify by discussing the differences in anatomical and functional properties of pyramidal-to-pyramidal connections within human and rodent cortical layers 2 and 3. With only a handful of studies available on synaptic transmission in human, it is obvious that much remains unknown to date. Uncovering the shared and divergent principles of synaptic transmission across species however, will almost certainly be a pivotal step toward understanding human cognitive ability and brain function in health and disease.

CACTUS: a computational framework for generating realistic white matter microstructure substrates

Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm3 with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.

An adaptive behavioral control motif mediated by cortical axo-axonic inhibition

Genetically defined subgroups of inhibitory interneurons are thought to play distinct roles in learning, but heterogeneity within these subgroups has limited our understanding of the scope and nature of their specific contributions. Here we reveal that the chandelier cell (ChC), an interneuron type that specializes in inhibiting the axon-initial segment (AIS) of pyramidal neurons, establishes cortical microcircuits for organizing neural coding through selective axo-axonic synaptic plasticity. We found that organized motor control is mediated by enhanced population coding of direction-tuned premotor neurons, with tuning refined through suppression of irrelevant neuronal activity. ChCs contribute to learning-dependent refinements by providing selective inhibitory control over individual pyramidal neurons rather than global suppression. Quantitative analysis of structural plasticity across axo-axonic synapses revealed that ChCs redistributed inhibitory weights to individual pyramidal neurons during learning. These results demonstrate an adaptive logic of the inhibitory circuit motif responsible for organizing distributed neural representations. Thus, ChCs permit efficient cortical computation in a targeted cell-specific manner.

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.

Projection-Specific Heterogeneity of the Axon Initial Segment of Pyramidal Neurons in the Prelimbic Cortex

The axon initial segment (AIS) is a highly specialized axonal compartment where the action potential is initiated. The heterogeneity of AISs has been suggested to occur between interneurons and pyramidal neurons (PyNs), which likely contributes to their unique spiking properties. However, whether the various characteristics of AISs can be linked to specific PyN subtypes remains unknown. Here, we report that in the prelimbic cortex (PL) of the mouse, two types of PyNs with axon projections either to the contralateral PL or to the ipsilateral basal lateral amygdala, possess distinct AIS properties reflected by morphology, ion channel expression, action potential initiation, and axo-axonic synaptic inputs from chandelier cells. Furthermore, projection-specific AIS diversity is more prominent in the superficial layer than in the deep layer. Thus, our study reveals the cortical layer- and axon projection-specific heterogeneity of PyN AISs, which may endow the spiking of various PyN types with exquisite modulation.

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.

microRNA-dependent regulation of gene expression in GABAergic interneurons

Information processing within neuronal circuits relies on their proper development and a balanced interplay between principal and local inhibitory interneurons within those circuits. Gamma-aminobutyric acid (GABA)ergic inhibitory interneurons are a remarkably heterogeneous population, comprising subclasses based on their morphological, electrophysiological, and molecular features, with differential connectivity and activity patterns. microRNA (miRNA)-dependent post-transcriptional control of gene expression represents an important regulatory mechanism for neuronal development and plasticity. miRNAs are a large group of small non-coding RNAs (21-24 nucleotides) acting as negative regulators of mRNA translation and stability. However, while miRNA-dependent gene regulation in principal neurons has been described heretofore in several studies, an understanding of the role of miRNAs in inhibitory interneurons is only beginning to emerge. Recent research demonstrated that miRNAs are differentially expressed in interneuron subclasses, are vitally important for migration, maturation, and survival of interneurons during embryonic development and are crucial for cognitive function and memory formation. In this review, we discuss recent progress in understanding miRNA-dependent regulation of gene expression in interneuron development and function. We aim to shed light onto mechanisms by which miRNAs in GABAergic interneurons contribute to sculpting neuronal circuits, and how their dysregulation may underlie the emergence of numerous neurodevelopmental and neuropsychiatric disorders.

SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning

3D electron microscopy (EM) connectomics image volumes are surpassing 1 mm3, providing information-dense, multi-scale visualizations of brain circuitry and necessitating scalable analysis techniques. We present SynapseCLR, a self-supervised contrastive learning method for 3D EM data, and use it to extract features of synapses from mouse visual cortex. SynapseCLR feature representations separate synapses by appearance and functionally important structural annotations. We demonstrate SynapseCLR's utility for valuable downstream tasks, including one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using manual annotation of only 0.2% of the dataset's synapses. In particular, excitatory versus inhibitory neuronal types can be assigned with >99.8% accuracy to individual synapses and highly truncated neurites, enabling neurite-enhanced connectomics analysis. Finally, we present a data-driven, unsupervised study of synaptic structural variation on the representation manifold, revealing its intrinsic axes of variation and showing that representations contain inhibitory subtype information.

An adaptive behavioral control motif mediated by cortical axo-axonic inhibition

Neural circuits are reorganized with specificity during learning. Genetically-defined subgroups of inhibitory interneurons are thought to play distinct roles in learning, but heterogeneity within these subgroups has limited our understanding of the scope and nature of their specific contributions to learning. Here we reveal that the chandelier cell (ChC), an interneuron type that specializes in inhibiting the axon-initial segment (AIS) of pyramidal neurons, establishes cortical microcircuits for organizing neural coding through selective axo-axonic synaptic plasticity. We find that organized motor control is mediated by enhanced population coding of direction-tuned premotor neurons, whose tuning is refined through suppression of irrelevant neuronal activity. ChCs are required for learning-dependent refinements via providing selective inhibitory control over pyramidal neurons rather than global suppression. Quantitative analysis on structural plasticity of axo-axonic synapses revealed that ChCs redistributed inhibitory weights to individual pyramidal neurons during learning. These results demonstrate an adaptive logic of the inhibitory circuit motif responsible for organizing distributed neural representations. Thus, ChCs permit efficient cortical computation in a target cell specific manner, which highlights the significance of interneuron diversity.

The connectome of an insect brain

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

Antibody-directed extracellular proximity biotinylation reveals Contactin-1 regulates axo-axonic innervation of axon initial segments

Axon initial segment (AIS) cell surface proteins mediate key biological processes in neurons including action potential initiation and axo-axonic synapse formation. However, few AIS cell surface proteins have been identified. Here, we used antibody-directed proximity biotinylation to define the cell surface proteins in close proximity to the AIS cell adhesion molecule Neurofascin. To determine the distributions of the identified proteins, we used CRISPR-mediated genome editing for insertion of epitope tags in the endogenous proteins. We found Contactin-1 (Cntn1) among the previously unknown AIS proteins we identified. Cntn1 is enriched at the AIS through interactions with Neurofascin and NrCAM. We further show that Cntn1 contributes to assembly of the AIS-extracellular matrix, and is required for AIS axo-axonic innervation by inhibitory basket cells in the cerebellum and inhibitory chandelier cells in the cortex.

The Development of Synapses in Mouse and Macaque Primary Sensory Cortices

We report that the rate of synapse development in primary sensory cortices of mice and macaques is unrelated to lifespan, as was previously thought. We analyzed 28,084 synapses over multiple developmental time points in both species and find, instead, that net excitatory synapse development of mouse and macaque neurons primarily increased at similar rates in the first few postnatal months, and then decreased over a span of 1-1.5 years of age. The development of inhibitory synapses differed qualitatively across species. In macaques, net inhibitory synapses first increase and then decrease on excitatory soma at similar ages as excitatory synapses. In mice, however, such synapses are added throughout life. These findings contradict the long-held belief that the cycle of synapse formation and pruning occurs earlier in shorter-lived animals. Instead, our results suggest more nuanced rules, with the development of different types of synapses following different timing rules or different trajectories across species.

Local shape descriptors for neuron segmentation

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

Endothelial structure contributes to heterogeneity in brain capillary diameter

The high metabolic demand of brain tissue is supported by a constant supply of blood flow through dense microvascular networks. Capillaries are the smallest class of vessels in the brain and their lumens vary in diameter between ~2 and 5 μm. This diameter range plays a significant role in optimizing blood flow resistance, blood cell distribution, and oxygen extraction. The control of capillary diameter has largely been ascribed to pericyte contractility, but it remains unclear if the architecture of the endothelial wall also contributes to capillary diameter. Here, we use public, large-scale volume electron microscopy data from mouse cortex (MICrONS Explorer, Cortical mm3) to examine how endothelial cell number, endothelial cell thickness, and pericyte coverage relates to microvascular lumen size. We find that transitional vessels near the penetrating arteriole and ascending venule are composed of two to six interlocked endothelial cells, while the capillaries intervening these zones are composed of either one or two endothelial cells, with roughly equal proportions. The luminal area and diameter are on average slightly larger with capillary segments composed of two interlocked endothelial cells vs one endothelial cell. However, this difference is insufficient to explain the full range of capillary diameters seen in vivo. This suggests that both endothelial structure and other influences, including pericyte tone, contribute to the basal diameter and optimized perfusion of brain capillaries.

FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis

BACKGROUND

Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory's use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2's CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.

FINDINGS

For more precise alignment, we add a landmark-based atlas registration to ClearMap's functions as well as include young mouse reference atlases for developmental studies. We provide an alternative cell segmentation method besides ClearMap's threshold-based approach: Ilastik's Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3-dimensional visualization of the annotated cells.

CONCLUSIONS

As a proof of principle, we use FriendlyClearMap to quantify the distribution of the 3 main GABAergic interneuron subclasses (parvalbumin+ [PV+], somatostatin+, and vasoactive intestinal peptide+) in the mouse forebrain and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.

Oligodendrocyte precursor cells ingest axons in the mouse neocortex

Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form. This physical transformation of neurons is facilitated by the engulfment and degradation of axonal branches and synapses by surrounding glial cells, including microglia and astrocytes. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia have made it difficult to define the contribution of these and other glial cell types to this crucial process. Here, we used large-scale, serial section transmission electron microscopy (TEM) with computational volume segmentation to reconstruct the complete 3D morphologies of distinct glial types in the mouse visual cortex, providing unprecedented resolution of their morphology and composition. Unexpectedly, we discovered that the fine processes of oligodendrocyte precursor cells (OPCs), a population of abundant, highly dynamic glial progenitors, frequently surrounded small branches of axons. Numerous phagosomes and phagolysosomes (PLs) containing fragments of axons and vesicular structures were present inside their processes, suggesting that OPCs engage in axon pruning. Single-nucleus RNA sequencing from the developing mouse cortex revealed that OPCs express key phagocytic genes at this stage, as well as neuronal transcripts, consistent with active axon engulfment. Although microglia are thought to be responsible for the majority of synaptic pruning and structural refinement, PLs were ten times more abundant in OPCs than in microglia at this stage, and these structures were markedly less abundant in newly generated oligodendrocytes, suggesting that OPCs contribute substantially to the refinement of neuronal circuits during cortical development.

Binary and analog variation of synapses between cortical pyramidal neurons

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.

The geometry of representational drift in natural and artificial neural networks

Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks. We examine stimulus representations from fluorescence recordings across hundreds of neurons in the visual cortex using in vivo two-photon calcium imaging and we corroborate previous studies finding that such representations change as experimental trials are repeated across days. This phenomenon has been termed "representational drift". In this study we geometrically characterize the properties of representational drift in the primary visual cortex of mice in two open datasets from the Allen Institute and propose a potential mechanism behind such drift. We observe representational drift both for passively presented stimuli, as well as for stimuli which are behaviorally relevant. Across experiments, the drift differs from in-session variance and most often occurs along directions that have the most in-class variance, leading to a significant turnover in the neurons used for a given representation. Interestingly, despite this significant change due to drift, linear classifiers trained to distinguish neuronal representations show little to no degradation in performance across days. The features we observe in the neural data are similar to properties of artificial neural networks where representations are updated by continual learning in the presence of dropout, i.e. a random masking of nodes/weights, but not other types of noise. Therefore, we conclude that a potential reason for the representational drift in biological networks is driven by an underlying dropout-like noise while continuously learning and that such a mechanism may be computational advantageous for the brain in the same way it is for artificial neural networks, e.g. preventing overfitting.

Regulation of Potassium and Chloride Concentrations in Nervous Tissue as a Method of Anticonvulsant Therapy

Abstract

Under some pathological conditions, such as pharmacoresistant epilepsy, status epilepticus or certain forms of genetic abnormalities, spiking activity of GABAergic interneurons may enhance excitation processes in neuronal circuits and provoke the generation of ictal discharges. As a result, anticonvulsants acting on the GABAergic system may be ineffective or even increase seizure activity. This paradoxical effect of the inhibitory system is due to ionic imbalances in nervous tissue. This review addresses the mechanisms of ictal discharge initiation in neuronal networks due to the imbalance of chloride and potassium ions, as well as possible ways to regulate ionic concentrations. Both the enhancement (or attenuation) of the activity of certain neuronal ion transporters and ion pumps and their additional expression via gene therapy can be effective in suppressing seizure activity caused by ionic imbalances. The Na+–K+-pump, NKCC1 and KCC2 cotransporters are important for maintaining proper K+ and Cl– concentrations in nervous tissue, having been repeatedly considered as pharmacological targets for antiepileptic exposures. Further progress in this direction is hampered by the lack of sufficiently selective pharmacological tools and methods for providing effective drug delivery to the epileptic focus. The use of the gene therapy techniques, such as overexpressing of the KCC2 transporter in the epileptic focus, seems to be a more promising approach. Another possible direction could be the use of optogenetic tools, namely specially designed light-activated ion pumps or ion channels. In this case, photon energy can be used to create the required gradients of chloride and potassium ions, although these methods also have significant limitations which complicate their rapid introduction into medicine.

What is a cell type and how to define it?

Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism's function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes1-6, but it is not known whether these subtypes have correspondingly diverse patterns of activity in the living brain. Here we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, which are organized by a single factor: position along the main axis of transcriptomic variation. We combined in vivo two-photon calcium imaging of mouse V1 with a transcriptomic method to identify mRNA for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1-3 into a three-level hierarchy of 5 subclasses, 11 types and 35 subtypes using previously defined transcriptomic clusters3. Responses to visual stimuli differed significantly only between subclasses, with cells in the Sncg subclass uniformly suppressed, and cells in the other subclasses predominantly excited. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory subtypes that fired more in resting, oscillatory brain states had a smaller fraction of their axonal projections in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro7, and expressed more inhibitory cholinergic receptors. Subtypes that fired more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 subtypes shape state-dependent cortical processing.

Public Volume Electron Microscopy Data: An Essential Resource to Study the Brain Microvasculature

Electron microscopy is the primary approach to study ultrastructural features of the cerebrovasculature. However, 2D snapshots of a vascular bed capture only a small fraction of its complexity. Recent efforts to synaptically map neuronal circuitry using volume electron microscopy have also sampled the brain microvasculature in 3D. Here, we perform a meta-analysis of 7 data sets spanning different species and brain regions, including two data sets from the MICrONS consortium that have made efforts to segment vasculature in addition to all parenchymal cell types in mouse visual cortex. Exploration of these data have revealed rich information for detailed investigation of the cerebrovasculature. Neurovascular unit cell types (including, but not limited to, endothelial cells, mural cells, perivascular fibroblasts, microglia, and astrocytes) could be discerned across broad microvascular zones. Image contrast was sufficient to identify subcellular details, including endothelial junctions, caveolae, peg-and-socket interactions, mitochondria, Golgi cisternae, microvilli and other cellular protrusions of potential significance to vascular signaling. Additionally, non-cellular structures including the basement membrane and perivascular spaces were visible and could be traced between arterio-venous zones along the vascular wall. These explorations revealed structural features that may be important for vascular functions, such as blood-brain barrier integrity, blood flow control, brain clearance, and bioenergetics. They also identified limitations where accuracy and consistency of segmentation could be further honed by future efforts. The purpose of this article is to introduce these valuable community resources within the framework of cerebrovascular research. We do so by providing an assessment of their vascular contents, identifying features of significance for further study, and discussing next step ideas for refining vascular segmentation and analysis.

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

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

Microglia regulate chandelier cell axo-axonic synaptogenesis

Chandelier cells (ChCs) are a unique type of GABAergic interneuron that form axo-axonic synapses exclusively on the axon initial segment (AIS) of neocortical pyramidal neurons (PyNs), allowing them to exert powerful yet precise control over PyN firing and population output. The importance of proper ChC function is further underscored by the association of ChC connectivity defects with various neurological conditions. Despite this, the cellular mechanisms governing ChC axo-axonic synapse formation remain poorly understood. Here, we identify microglia as key regulators of ChC axonal morphogenesis and AIS synaptogenesis, and show that disease-induced aberrant microglial activation perturbs proper ChC synaptic development/connectivity in the neocortex. In doing so, such findings highlight the therapeutic potential of manipulating microglia to ensure proper brain wiring.

Microglia have emerged as critical regulators of synapse development and circuit formation in the healthy brain. To date, examination of microglia in such processes has largely been focused on excitatory synapses. Their roles, however, in the modulation of GABAergic interneuron synapses—particularly those targeting the axon initial segment (AIS)—during development remain enigmatic. Here, we identify a synaptogenic/growth-promoting role for microglia in regulating pyramidal neuron (PyN) AIS synapse formation by chandelier cells (ChCs), a unique interneuron subtype whose axonal terminals, called cartridges, selectively target the AIS. We show that a subset of microglia contacts PyN AISs and ChC cartridges and that such tripartite interactions, which rely on the unique AIS cytoskeleton and microglial GABA_B1 receptors, are associated with increased ChC cartridge length and bouton number and AIS synaptogenesis. Conversely, microglia depletion or disease-induced aberrant microglia activation impairs the proper development and maintenance of ChC cartridges and boutons, as well as AIS synaptogenesis. These findings unveil key roles for homeostatic, AIS-associated microglia in regulating proper ChC axonal morphogenesis and synaptic connectivity in the neocortex.