Correlation between axonal morphologies and synaptic input kinetics of interneurons from mouse visual cortex

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis

The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphological analysis, such as identifying neuron types and large-scale neuron retrieval, all of which require accurate measuring and efficient matching algorithms. Recently, many studies have been conducted to describe neuronal morphologies quantitatively using predefined measurements. However, hand-crafted features are usually inadequate for distinguishing fine-grained differences among massive neurons. In this article, we propose a novel morphology-aware contrastive graph neural network (MACGNN) for unsupervised neuronal morphological representation learning. To improve the retrieval efficiency in large-scale neuronal morphological datasets, we further propose Hash-MACGNN by introducing an improved deep hash algorithm to train the network end-to-end to learn binary hash representations of neurons. We conduct extensive experiments on the largest dataset, NeuroMorpho, which contains more than 100 000 neurons. The experimental results demonstrate the effectiveness and superiority of our MACGNN and Hash-MACGNN for large-scale neuronal morphological analysis.

Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons

GABAergic interneurons tend to diversify into similar classes across telencephalic regions. However, it remains unclear whether the electrophysiological and molecular properties commonly used to define these classes are discriminant in the hilus of the dentate gyrus. Here, using patch-clamp combined with single cell RT-PCR, we compare the relevance of commonly used electrophysiological and molecular features for the clustering of GABAergic interneurons sampled from the mouse hilus and primary sensory cortex. While unsupervised clustering groups cortical interneurons into well-established classes, it fails to provide a convincing partition of hilar interneurons. Statistical analysis based on resampling indicates that hilar and cortical GABAergic interneurons share limited homology. While our results do not invalidate the use of classical molecular marker in the hilus, they indicate that classes of hilar interneurons defined by the expression of molecular markers do not exhibit strongly discriminating electrophysiological properties.

Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex predicted by deep learning of experimental recordings

Biologically realistic computer simulations of neuronal circuits require systematic data-driven modeling of neuron type-specific synaptic activity. However, limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal identification have so far prevented the consistent characterization of synaptic signals for all connections of any neural system. We introduce a strategy to overcome these challenges and report a comprehensive synaptic quantification among all known neuron types of the hippocampal-entorhinal network. First, we reconstructed >2600 synaptic traces from ∼1200 publications into a unified computational representation of synaptic dynamics. We then trained a deep learning architecture with the resulting parameters, each annotated with detailed metadata such as recording method, solutions, and temperature. The model learned to predict the synaptic properties of all 3,120 circuit connections in arbitrary conditions with accuracy approaching the intrinsic experimental variability. Analysis of data normalized and completed with the deep learning model revealed that synaptic signals are controlled by few latent variables associated with specific molecular markers and interrelating conductance, decay time constant, and short-term plasticity. We freely release the tools and full dataset of unitary synaptic values in 32 covariate settings. Normalized synaptic data can be used in brain simulations, and to predict and test experimental hypothesis.

A deep learning model trained on roughly 2,600 synaptic traces from hippocampal electrophysiology datasets demonstrates how specific covariates influence synaptic signals.

Postnatal development of inner lamina II interneurons of the rat medullary dorsal horn

ABSTRACT

Pain processing in young mammals is immature. Despite the central role of the medullary dorsal horn (MDH) in processing orofacial sensory information, the maturation of the neurons within the MDH has been largely overlooked. Combining in vitro electrophysiological recordings and 3D morphological analysis over the first postnatal month in rats, we investigated the age-dependent development of the neurons within the inner lamina II (IIi) of the MDH. We show the lamina IIi neuronal population transition into a more hyperpolarized state, with modification of the action potential waveform, and a shift from single spiking, at early postnatal ages, to tonic firing and initial bursting at later stages. These physiological changes are associated with a strong structural remodelling of the neuronal morphology with most of the modifications occurring after the third postnatal week. Among the lamina IIi neuronal population, the subpopulation of interneurons expressing the γ isoform of the protein kinase C (PKCγ+) are key elements for the circuits underlying facial mechanical allodynia. How do they develop from the rest of the lamina IIi constitute an important question that remained to be addressed. Here, we show that PKCγ+ interneurons display electrophysiological changes over time comparable with the PKCγ- population. However, they show a distinctive increase of the soma volume and primary branches length, as opposed to the PKCγ- population. Together, our data demonstrate a novel pattern of late postnatal maturation of lamina IIi interneurons, with a spotlight on PKCγ+ interneurons, that may be relevant for the development of orofacial sensitivity.

A community-based transcriptomics classification and nomenclature of neocortical cell types

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification

Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions.

Methamphetamine Learning Induces Persistent and Selective Nonmuscle Myosin II-Dependent Spine Motility in the Basolateral Amygdala

Nonmuscle myosin II inhibition (NMIIi) in the basolateral amygdala (BLA), but not dorsal hippocampus (CA1), selectively disrupts memories associated with methamphetamine (METH) days after learning, without retrieval. However, the molecular mechanisms underlying this selective vulnerability remain poorly understood. A known function of NMII is to transiently activate synaptic actin dynamics with learning. Therefore, we hypothesized that METH-associated learning perpetuates NMII-driven actin dynamics in synapses, leading to an extended window of vulnerability for memory disruption. We used time-lapse two-photon imaging of dendritic spine motility in acutely prepared brain slices from female and male mice following METH-associated learning as a readout of actin-myosin dynamics. Spine motility was persistently increased in the BLA, but not in CA1. Consistent with the memory disrupting effect of intra-BLA NMII inhibition, METH-induced changes to BLA spine dynamics were reversed by a single systemic injection of an NMII inhibitor. Intra-CA1 NMII inhibition, on the other hand, did not disrupt METH-associated memory. Thus, we report identification of a previously unknown ability for spine actin dynamics to persist days after stimulation and that this is under the control of NMII. Further, these perpetual NMII-driven spine actin dynamics in BLA neurons may contribute to the unique susceptibility of METH-associated memories.SIGNIFICANCE STATEMENT There are no Food and Drug Administration-approved pharmacotherapies to prevent relapse to the use of stimulants, such as methamphetamine (METH). Environmental cues become associated with drug use, such that the memories can elicit strong motivation to seek the drug during abstinence. We previously reported that the storage of METH-associated memories is uniquely vulnerable to immediate, retrieval-independent, and lasting disruption by direct actin depolymerization or by inhibiting the actin driver nonmuscle myosin II (NMII) in the BLA or systemically. Here we report a potential structural mechanism responsible for the unique vulnerability of METH-associated memories and METH-seeking behavior to NMII inhibition within the BLA.

Conditioning by subthreshold synaptic input changes the intrinsic firing pattern of CA3 hippocampal neurons

Unlike synaptic strength, intrinsic excitability is assumed to be a stable property of neurons. For example, learning of somatic conductances is generally not incorporated into computational models, and the discharge pattern of neurons in response to test stimuli is frequently used as a basis for phenotypic classification. However, it is increasingly evident that signal processing properties of neurons are more generally plastic on the timescale of minutes. Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions toward adapting and intrinsic burst behaviors, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade and found that the observed transitions are likely due to recruitment of low-voltage calcium and Kv7 potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits.NEW & NOTEWORTHY Although firing patterns are widely conserved across the animal phyla, it is still a mystery why nerve cells present such diversity of discharge dynamics upon somatic step currents. Adding a new timing dimension to the intrinsic plasticity literature, here we show that CA3 neurons rapidly adapt through the space of known firing patterns in response to the subthreshold signals that they receive from their embedding circuit, potentially adjusting their network processing to the temporal statistics of their circuit.

Electrophysiology as a theoretical and methodological hub for the neural sciences

Electrophysiology is a direct measure of neuronal processes, and it is uniquely sensitive to canonical neural operations that underlie emergent psychological operations. These qualities make it well suited for discovery of aberrant neural mechanisms that underlie complicated disease states. This technique is routinely utilized in vitro, in vivo, and in outpatient neurological clinics, offering a translatable bridge between animal models and human patients. The bench-to-bedside potential of this approach is unparalleled, yet it also remains undeveloped due to the slow inertia of legacy techniques and interpretations. In this review, I discuss these strengths of the method, and I detail compelling reasons why future advancements can have a direct and tangible influence over clinical practice. I hope to motivate a blurring of traditional boundaries between preclinical, computational, imaging, and clinical fields by advancing electrophysiology as a common hub for methodological integration and theoretical advancement.

Towards a supervised classification of neocortical interneuron morphologies

BACKGROUND

The challenge of classifying cortical interneurons is yet to be solved. Data-driven classification into established morphological types may provide insight and practical value.

RESULTS

We trained models using 217 high-quality morphologies of rat somatosensory neocortex interneurons reconstructed by a single laboratory and pre-classified into eight types. We quantified 103 axonal and dendritic morphometrics, including novel ones that capture features such as arbor orientation, extent in layer one, and dendritic polarity. We trained a one-versus-rest classifier for each type, combining well-known supervised classification algorithms with feature selection and over- and under-sampling. We accurately classified the nest basket, Martinotti, and basket cell types with the Martinotti model outperforming 39 out of 42 leading neuroscientists. We had moderate accuracy for the double bouquet, small and large basket types, and limited accuracy for the chandelier and bitufted types. We characterized the types with interpretable models or with up to ten morphometrics.

CONCLUSION

Except for large basket, 50 high-quality reconstructions sufficed to learn an accurate model of a type. Improving these models may require quantifying complex arborization patterns and finding correlates of bouton-related features. Our study brings attention to practical aspects important for neuron classification and is readily reproducible, with all code and data available online.

Electrophysiological Profiling of Neocortical Neural Subtypes: A Semi-Supervised Method Applied to in vivo Whole-Cell Patch-Clamp Data

A lot of efforts have been made to understand the structure and function of neocortical circuits. In fact, a promising way to understand the functions of cortical circuits is the classification of the neural types, based on their different properties. Recent studies focused on applying modern computational methods to classify neurons based on molecular, morphological, physiological, or mixed of these criteria. Although there are studies in the literature on in vitro/vivo extracellular or in vitro intracellular recordings, a study on the classification of neuronal types using in vivo whole-cell patch-clamp recordings is still lacking. We thus proposed a novel semi-supervised classification method based on waveform shape of neurons' spikes using in vivo whole-cell patch-clamp recordings. We, first, detected spike candidates. Then discriminative features were extracted from the time samples of the spikes using discrete cosine transform. We then extracted the center of clusters using fuzzy c-mean clustering and finally, the neurons were classified using the minimum distance classifier. We distinguished three types of neurons: excitatory pyramidal cells (Pyr) and two types of inhibitory neurons: GABAergic- parvalbumin positive (PV), and somatostatin positive (SST) non-pyramidal cells in layer II/III of the mice primary visual cortex. We used 10-fold cross validation in our study. The classification accuracy for PV, Pyr, and SST was 91.59 ± 1.69, 97.47 ± 0.67, and 89.06 ± 1.99, respectively. Overall, the algorithm correctly classified 92.67 ± 0.54% of the cells, confirming the relative robustness of the discriminant functions. The performance of the method was further assessed on in vitro recordings by using a pool of 50 neurons from Allen institute Cell Types Database (5 major subtypes of neurons: Pyr, PV, SST, 5HT3a, and vasoactive intestinal peptide (VIP) cells). Its overall accuracy was 84.13 ± 0.81% on this data set using cross validation framework. The proposed algorithm is thus a promising new tool in recognizing cell's type with high accuracy in laboratories using in vivo/vitro whole-cell patch-clamp recording technique. The developed programs and the entire dataset are available online to interested readers.

The Microtubule Regulator NEK7 Coordinates the Wiring of Cortical Parvalbumin Interneurons

Functional networks in the mammalian cerebral cortex rely on the interaction between glutamatergic pyramidal cells and GABAergic interneurons. Both neuronal populations exhibit an extraordinary divergence in morphology and targeting areas, which ultimately dictate their precise function in cortical circuits. How these prominent morphological differences arise during development is not well understood. Here, we conducted a high-throughput screen for genes differentially expressed by pyramidal cells and interneurons during cortical wiring. We found that NEK7, a kinase involved in microtubule polymerization, is mostly expressed in parvalbumin (PV+) interneurons at the time when they establish their connectivity. Functional experiments revealed that NEK7-deficient PV+ interneurons show altered microtubule dynamics, axon growth cone steering and reduced axon length, arbor complexity, and total number of synaptic contacts formed with pyramidal cells. Altogether, our results reveal a molecular mechanism by which the microtubule-associated kinase NEK7 regulates the wiring of PV+ interneurons.

Mining Big Neuron Morphological Data

The advent of automatic tracing and reconstruction technology has led to a surge in the number of neurons 3D reconstruction data and consequently the neuromorphology research. However, the lack of machine-driven annotation schema to automatically detect the types of the neurons based on their morphology still hinders the development of this branch of science. Neuromorphology is important because of the interplay between the shape and functionality of neurons and the far-reaching impact on the diagnostics and therapeutics in neurological disorders. This survey paper provides a comprehensive research in the field of automatic neurons classification and presents the existing challenges, methods, tools, and future directions for automatic neuromorphology analytics. We summarize the major automatic techniques applicable in the field and propose a systematic data processing pipeline for automatic neuron classification, covering data capturing, preprocessing, analyzing, classification, and retrieval. Various techniques and algorithms in machine learning are illustrated and compared to the same dataset to facilitate ongoing research in the field.

Quantitative Association of Anatomical and Functional Classes of Olfactory Bulb Neurons

Juxtaglomerular cells (JGCs) of the olfactory bulb (OB) glomerular layer (GL) play a fundamental role in olfactory information processing. Their variability in morphology, physiology, and connectivity suggests distinct functions. The quantitative understanding of population-wise morphological and physiological properties and a comprehensive classification based on quantitative parameters, however, is still lacking, impeding the analysis of microcircuits. Here, we provide multivariate clustering of 95 in vitro sampled cells from the GL of the mouse (male or female C57BL/6) OB and perform detailed morphological and physiological characterization for the seven computed JGC types. Using a classifier based on a subselection of parameters, we identified the neuron types in paired recordings to characterize their functional connectivity. We found that 4 of the 7 clusters comply with prevailing concepts of GL cell types, whereas the other 3 represent own distinct entities. We have labeled these entities horizontal superficial tufted cell (hSTC), vertical superficial tufted cell, and microglomerular cell (MGC): The hSTC is a tufted cell with a lateral dendrite that much like mitral cells and tufted cells receives excitatory inputs from the external tufted cell but likewise serves as an excitatory element for glomerular interneurons. The vertical superficial tufted cell, on the other hand, represents a tufted cell type with vertically projecting basal dendrites. We further define the MGC, characterized by a small dendritic tree and plateau action potentials. In addition to olfactory nerve-driven and external tufted cell driven interneurons, these MGCs represent a third functionally distinct type, the hSTC-driven interneurons. The presented correlative analysis helps to bridge the gap between branching patterns and cellular functional properties, permitting the integration of results from in vivo recordings, advanced morphological tools, and connectomics.

SIGNIFICANCE STATEMENT The variance of neuron properties is a feature across mammalian cerebral circuits, contributing to signal processing and adding computational robustness to the networks. It is particularly noticeable in the glomerular layer of the olfactory bulb, the first site of olfactory information processing. We provide the first unbiased population-wise multivariate analysis to correlate morphological and physiological parameters of juxtaglomerular cells. We identify seven cell types, including four previously described neuron types, and identify further three distinct classes. The presented correlative analysis of morphological and physiological parameters gives an opportunity to predict morphological classes from physiological measurements or the functional properties of neurons from morphology and opens the way to integrate results from in vivo recordings, advanced morphological tools, and connectomics.

Diversity and Connectivity of Layer 5 Somatostatin-Expressing Interneurons in the Mouse Barrel Cortex

Inhibitory interneurons represent 10-15% of the neurons in the somatosensory cortex, and their activity powerfully shapes sensory processing. Three major groups of GABAergic interneurons have been defined according to developmental, molecular, morphological, electrophysiological, and synaptic features. Dendritic-targeting somatostatin-expressing interneurons (SST-INs) have been shown to display diverse morphological, electrophysiological, and molecular properties and activity patterns in vivo However, the correlation between these properties and SST-IN subtype is unclear. In this study, we aimed to correlate the morphological diversity of layer 5 (L5) SST-INs with their electrophysiological and molecular diversity in mice of either sex. Our morphological analysis demonstrated the existence of three subtypes of L5 SST-INs with distinct electrophysiological properties: T-shaped Martinotti cells innervate L1, and are low-threshold spiking; fanning-out Martinotti cells innervate L2/3 and the lower half of L1, and show adapting firing patterns; non-Martinotti cells innervate L4, and show a quasi-fast spiking firing pattern. We estimated the proportion of each subtype in L5 and found that T-shaped Martinotti, fanning-out Martinotti, and Non-Martinotti cells represent ∼10, ∼50, and ∼40% of L5 SST-INs, respectively. Last, we examined the connectivity between the three SST-IN subtypes and L5 pyramidal cells (PCs). We found that L5 T-shaped Martinotti cells inhibit the L1 apical tuft of nearby PCs; L5 fanning-out Martinotti cells also inhibit nearby PCs but they target the dendrite mainly in L2/3. On the other hand, non-Martinotti cells inhibit the dendrites of L4 neurons while avoiding L5 PCs. Our data suggest that morphologically distinct SST-INs gate different excitatory inputs in the barrel cortex.SIGNIFICANCE STATEMENT Morphologically diverse layer 5 SST-INs show different patterns of activity in behaving animals. However, little is known about the abundance and connectivity of each morphological type and the correlation between morphological subtype and spiking properties. We demonstrate a correlation between the morphological and electrophysiological diversity of layer 5 SST-INs. Based on these findings we built a classifier to infer the abundance of each morphological subtype. Last, using paired recordings combined with morphological analysis, we investigated the connectivity of each morphological subtype. Our data suggest that, by targeting different cell types and cellular compartments, morphologically diverse SST-INs might gate different excitatory inputs in the mouse barrel cortex.

Anatomical and Electrophysiological Clustering of Superficial Medial Entorhinal Cortex Interneurons

Local GABAergic interneurons regulate the activity of spatially-modulated principal cells in the medial entorhinal cortex (MEC), mediating stellate-to-stellate connectivity and possibly enabling grid formation via recurrent inhibitory circuitry. Despite the important role interneurons seem to play in the MEC cortical circuit, the combination of low cell counts and functional diversity has made systematic electrophysiological studies of these neurons difficult. For these reasons, there remains a paucity of knowledge on the electrophysiological profiles of superficial MEC interneuron populations. Taking advantage of glutamic acid decarboxylase 2 (GAD2)-IRES-tdTomato and PV-tdTomato transgenic mice, we targeted GABAergic interneurons for whole-cell patch-clamp recordings and characterized their passive membrane features, basic input/output properties and action potential (AP) shape. These electrophysiologically characterized cells were then anatomically reconstructed, with emphasis on axonal projections and pial depth. K-means clustering of interneuron anatomical and electrophysiological data optimally classified a population of 106 interneurons into four distinct clusters. The first cluster is comprised of layer 2- and 3-projecting, slow-firing interneurons. The second cluster is comprised largely of PV+ fast-firing interneurons that project mainly to layers 2 and 3. The third cluster contains layer 1- and 2-projecting interneurons, and the fourth cluster is made up of layer 1-projecting horizontal interneurons. These results, among others, will provide greater understanding of the electrophysiological characteristics of MEC interneurons, help guide future in vivo studies, and may aid in uncovering the mechanism of grid field formation.

Multimodal Fingerprints of Resting State Networks as assessed by Simultaneous Trimodal MR-PET-EEG Imaging

Simultaneous MR-PET-EEG (magnetic resonance imaging - positron emission tomography – electroencephalography), a new tool for the investigation of neuronal networks in the human brain, is presented here for the first time. It enables the assessment of molecular metabolic information with high spatial and temporal resolution in a given brain simultaneously. Here, we characterize the brain’s default mode network (DMN) in healthy male subjects using multimodal fingerprinting by quantifying energy metabolism via 2- [¹⁸F]fluoro-2-desoxy-D-glucose PET (FDG-PET), the inhibition – excitation balance of neuronal activation via magnetic resonance spectroscopy (MRS), its functional connectivity via fMRI and its electrophysiological signature via EEG. The trimodal approach reveals a complementary fingerprint. Neuronal activation within the DMN as assessed with fMRI is positively correlated with the mean standard uptake value of FDG. Electrical source localization of EEG signals shows a significant difference between the dorsal DMN and sensorimotor network in the frequency range of δ, θ, α and β–1, but not with β–2 and β–3. In addition to basic neuroscience questions addressing neurovascular-metabolic coupling, this new methodology lays the foundation for individual physiological and pathological fingerprints for a wide research field addressing healthy aging, gender effects, plasticity and different psychiatric and neurological diseases.

An optogenetic toolbox for unbiased discovery of functionally connected cells in neural circuits

Optical imaging approaches have revolutionized our ability to monitor neural network dynamics, but by themselves are unable to link a neuron’s activity to its functional connectivity. We present a versatile genetic toolbox, termed ‘Optobow’, for all-optical discovery of excitatory connections in vivo. By combining the Gal4-UAS system with Cre/lox recombination, we target the optogenetic actuator ChrimsonR and the sensor GCaMP6 to stochastically labeled, nonoverlapping and sparse subsets of neurons. Photostimulation of single cells using two-photon computer-generated holography evokes calcium responses in downstream neurons. Morphological reconstruction of neurite arbors, response latencies and localization of presynaptic markers suggest that some neuron pairs recorded here are directly connected, while others are two or more synapses apart from each other. With this toolbox, we discover wiring principles between specific cell types in the larval zebrafish tectum. Optobow should be useful for identification and manipulation of networks of interconnected neurons, even in dense neural tissues.

Mechanisms of neural processing can only be understood by revealing patterns of connectivity among the cellular components of the circuit. Here the authors report a new genetic toolbox, ‘Optobow’, which enables simultaneous optogenetic activation of single neurons in zebrafish and measuring the activity of downstream neurons in the network.

Somatostatin-positive interneurons in the dentate gyrus of mice provide local- and long-range septal synaptic inhibition

Somatostatin-expressing-interneurons (SOMIs) in the dentate gyrus (DG) control formation of granule cell (GC) assemblies during memory acquisition. Hilar-perforant-path-associated interneurons (HIPP cells) have been considered to be synonymous for DG-SOMIs. Deviating from this assumption, we show two functionally contrasting DG-SOMI-types. The classical feedback-inhibitory HIPPs distribute axon fibers in the molecular layer. They are engaged by converging GC-inputs and provide dendritic inhibition to the DG circuitry. In contrast, SOMIs with axon in the hilus, termed hilar interneurons (HILs), provide perisomatic inhibition onto GABAergic cells in the DG and project to the medial septum. Repetitive activation of glutamatergic inputs onto HIPP cells induces long-lasting-depression (LTD) of synaptic transmission but long-term-potentiation (LTP) of synaptic signals in HIL cells. Thus, LTD in HIPPs may assist flow of spatial information from the entorhinal cortex to the DG, whereas LTP in HILs may facilitate the temporal coordination of GCs with activity patterns governed by the medial septum.

DOI:http://dx.doi.org/10.7554/eLife.21105.001

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

Selective Maturation of Temporal Dynamics of Intracortical Excitatory Transmission at the Critical Period Onset

Although the developmental maturation of cortical inhibitory synapses is known to be a critical factor in gating the onset of critical period (CP) for experience-dependent cortical plasticity, how synaptic transmission dynamics of other cortical synapses are regulated during the transition to CP remains unknown. Here, by systematically examining various intracortical synapses within layer 4 of the mouse visual cortex, we demonstrate that synaptic temporal dynamics of intracortical excitatory synapses on principal cells (PCs) and inhibitory parvalbumin- or somatostatin-expressing cells are selectively regulated before the CP onset, whereas those of intracortical inhibitory synapses and long-range thalamocortical excitatory synapses remain unchanged. This selective maturation of synaptic dynamics results from a ubiquitous reduction of presynaptic release and is dependent on visual experience. These findings provide an additional essential circuit mechanism for regulating CP timing in the developing visual cortex.

The Current Status of Somatostatin-Interneurons in Inhibitory Control of Brain Function and Plasticity

The mammalian neocortex contains many distinct inhibitory neuronal populations to balance excitatory neurotransmission. A correct excitation/inhibition equilibrium is crucial for normal brain development, functioning, and controlling lifelong cortical plasticity. Knowledge about how the inhibitory network contributes to brain plasticity however remains incomplete. Somatostatin- (SST-) interneurons constitute a large neocortical subpopulation of interneurons, next to parvalbumin- (PV-) and vasoactive intestinal peptide- (VIP-) interneurons. Unlike the extensively studied PV-interneurons, acknowledged as key components in guiding ocular dominance plasticity, the contribution of SST-interneurons is less understood. Nevertheless, SST-interneurons are ideally situated within cortical networks to integrate unimodal or cross-modal sensory information processing and therefore likely to be important mediators of experience-dependent plasticity. The lack of knowledge on SST-interneurons partially relates to the wide variety of distinct subpopulations present in the sensory neocortex. This review informs on those SST-subpopulations hitherto described based on anatomical, molecular, or electrophysiological characteristics and whose functional roles can be attributed based on specific cortical wiring patterns. A possible role for these subpopulations in experience-dependent plasticity will be discussed, emphasizing on learning-induced plasticity and on unimodal and cross-modal plasticity upon sensory loss. This knowledge will ultimately contribute to guide brain plasticity into well-defined directions to restore sensory function and promote lifelong learning.

Principles of connectivity among morphologically defined cell types in adult neocortex

Since the work of Ramón y Cajal in the late 19th and early 20th centuries, neuroscientists have speculated that a complete understanding of neuronal cell types and their connections is key to explaining complex brain functions. However, a complete census of the constituent cell types and their wiring diagram in mature neocortex remains elusive. By combining octuple whole-cell recordings with an optimized avidin-biotin-peroxidase staining technique, we carried out a morphological and electrophysiological census of neuronal types in layers 1, 2/3, and 5 of mature neocortex and mapped the connectivity between more than 11,000 pairs of identified neurons. We categorized 15 types of interneurons, and each exhibited a characteristic pattern of connectivity with other interneuron types and pyramidal cells. The essential connectivity structure of the neocortical microcircuit could be captured by only a few connectivity motifs.

Synapse-specific expression of calcium-permeable AMPA receptors in neocortical layer 5

Key points

In the hippocampus, calcium‐permeable AMPA receptors have been found in a restricted subset of neuronal types that inhibit other neurons, although their localization in the neocortex is less well understood.

In the present study, we looked for calcium‐permeable AMPA receptors in two distinct populations of neocortical inhibitory neurons: basket cells and Martinotti cells. We found them in the former but not in the latter. Furthermore, in basket cells, these receptors were associated with particularly fast responses.

Computer modelling predicted (and experiments verified) that fast calcium‐permeable AMPA receptors enable basket cells to respond rapidly, such that they promptly inhibit neighbouring cells and shut down activity.

The results obtained in the present study help our understanding of pathologies such as stroke and epilepsy that have been associated with disordered regulation of calcium‐permeable AMPA receptors.

Abstract

AMPA‐type glutamate receptors (AMPARs) lacking an edited GluA2 subunit are calcium‐permeable (CP) and contribute to synaptic plasticity in several hippocampal interneuron types, although their precise role in the neocortex is not well described. We explored the presence of CP‐AMPARs at pyramidal cell (PC) inputs to Martinotti cells (MCs) and basket cells (BCs) in layer 5 of the developing mouse visual cortex (postnatal days 12–21). GluA2 immunolabelling was stronger in MCs than in BCs. A differential presence of CP‐AMPARs at PC‐BC and PC‐MC synapses was confirmed electrophysiologically, based on measures of spermine‐dependent rectification and CP‐AMPAR blockade by 1‐naphtyl acetyl spermine using recordings from synaptically connected cell pairs, NPEC‐AMPA uncaging and miniature current recordings. In addition, CP‐AMPAR expression in BCs was correlated with rapidly decaying synaptic currents. Computer modelling predicted that this reduces spike latencies and sharpens suprathreshold responses in BCs, which we verified experimentally using the dynamic clamp technique. Thus, the synapse‐specific expression of CP‐AMPARs may critically influence both plasticity and information processing in neocortical microcircuits.

In the hippocampus, calcium‐permeable AMPA receptors have been found in a restricted subset of neuronal types that inhibit other neurons, although their localization in the neocortex is less well understood.

In the present study, we looked for calcium‐permeable AMPA receptors in two distinct populations of neocortical inhibitory neurons: basket cells and Martinotti cells. We found them in the former but not in the latter. Furthermore, in basket cells, these receptors were associated with particularly fast responses.

Computer modelling predicted (and experiments verified) that fast calcium‐permeable AMPA receptors enable basket cells to respond rapidly, such that they promptly inhibit neighbouring cells and shut down activity.

The results obtained in the present study help our understanding of pathologies such as stroke and epilepsy that have been associated with disordered regulation of calcium‐permeable AMPA receptors.

Diversity and overlap of parvalbumin and somatostatin expressing interneurons in mouse presubiculum

The presubiculum, located between hippocampus and entorhinal cortex, plays a fundamental role in representing spatial information, notably head direction. Little is known about GABAergic interneurons of this region. Here, we used three transgenic mouse lines, Pvalb-Cre, Sst-Cre, and X98, to examine distinct interneurons labeled with tdTomato or green fluorescent protein. The distribution of interneurons in presubicular lamina for each animal line was compared to that in the GAD67-GFP knock-in animal line. Labeling was specific in the Pvalb-Cre line with 87% of labeled interneurons immunopositive for parvalbumin (PV). Immunostaining for somatostatin (SOM) revealed good specificity in the X98 line with 89% of fluorescent cells, but a lesser specificity in Sst-Cre animals where only 71% of labeled cells were immunopositive. A minority of ∼6% of interneurons co-expressed PV and SOM in the presubiculum of Sst-Cre animals. The electrophysiological and morphological properties of fluorescent interneurons from Pvalb-Cre, Sst-Cre, and X98 mice differed. Distinct physiological groups of presubicular interneurons were resolved by unsupervised cluster analysis of parameters describing passive properties, firing patterns and AP shapes. One group consisted of SOM-positive, Martinotti type neurons with a low firing threshold (cluster 1). Fast spiking basket cells, mainly from the Pvalb-Cre line, formed a distinct group (cluster 3). Another group (cluster 2) contained interneurons of intermediate electrical properties and basket-cell like morphologies. These labeled neurons were recorded from both Sst-Cre and Pvalb-Cre animals. Thus, our results reveal a wide variation in anatomical and physiological properties for these interneurons, a real overlap of interneurons immuno-positive for both PV and SOM as well as an off-target recombination in the Sst-Cre line, possibly linked to maternal cre inheritance.

A barrel-related interneuron in layer 4 of rat somatosensory cortex with a high intrabarrel connectivity

Synaptic connections between identified fast-spiking (FS), parvalbumin (PV)-positive interneurons, and excitatory spiny neurons in layer 4 (L4) of the barrel cortex were investigated using patch-clamp recordings and simultaneous biocytin fillings. Three distinct clusters of FS L4 interneurons were identified based on their axonal morphology relative to the barrel column suggesting that these neurons do not constitute a homogeneous interneuron population. One L4 FS interneuron type had an axonal domain strictly confined to a L4 barrel and was therefore named "barrel-confined inhibitory interneuron" (BIn). BIns established reliable inhibitory synaptic connections with L4 spiny neurons at a high connectivity rate of 67%, of which 69% were reciprocal. Unitary IPSPs at these connections had a mean amplitude of 0.9 ± 0.8 mV with little amplitude variation and weak short-term synaptic depression. We found on average 3.7 ± 1.3 putative inhibitory synaptic contacts that were not restricted to perisomatic areas. In conclusion, we characterized a novel type of barrel cortex interneuron in the major thalamo-recipient layer 4 forming dense synaptic networks with L4 spiny neurons. These networks constitute an efficient and powerful inhibitory feedback system, which may serve to rapidly reset the barrel microcircuitry following sensory activation.

Functional properties of GABA synaptic inputs onto GABA neurons in monkey prefrontal cortex

In rodent cortex GABAA receptor (GABAAR)-mediated synapses are a significant source of input onto GABA neurons, and the properties of these inputs vary among GABA neuron subtypes that differ in molecular markers and firing patterns. Some features of cortical interneurons are different between rodents and primates, but it is not known whether inhibition of GABA neurons is prominent in the primate cortex and, if so, whether these inputs show heterogeneity across GABA neuron subtypes. We thus studied GABAAR-mediated miniature synaptic events in GABAergic interneurons in layer 3 of monkey dorsolateral prefrontal cortex (DLPFC). Interneurons were identified on the basis of their firing pattern as fast spiking (FS), regular spiking (RS), burst spiking (BS), or irregular spiking (IS). Miniature synaptic events were common in all of the recorded interneurons, and the frequency of these events was highest in FS neurons. The amplitude and kinetics of miniature inhibitory postsynaptic potentials (mIPSPs) also differed between DLPFC interneuron subtypes in a manner correlated with their input resistance and membrane time constant. FS neurons had the fastest mIPSP decay times and the strongest effects of the GABAAR modulator zolpidem, suggesting that the distinctive properties of inhibitory synaptic inputs onto FS cells are in part conferred by GABAARs containing α1 subunits. Moreover, mIPSCs differed between FS and RS interneurons in a manner consistent with the mIPSP findings. These results show that in the monkey DLPFC GABAAR-mediated synaptic inputs are prominent in layer 3 interneurons and may differentially regulate the activity of different interneuron subtypes.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists' classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Morpho-physiological criteria divide dentate gyrus interneurons into classes

GABAergic inhibitory interneurons control fundamental aspects of neuronal network function. Their functional roles are assumed to be defined by the identity of their input synapses, the architecture of their dendritic tree, the passive and active membrane properties and finally the nature of their postsynaptic targets. Indeed, interneurons display a high degree of morphological and physiological heterogeneity. However, whether their morphological and physiological characteristics are correlated and whether interneuron diversity can be described by a continuum of GABAergic cell types or by distinct classes has remained unclear. Here we perform a detailed morphological and physiological characterization of GABAergic cells in the dentate gyrus, the input region of the hippocampus. To achieve an unbiased and efficient sampling and classification we used knock-in mice expressing the enhanced green fluorescent protein (eGFP) in glutamate decarboxylase 67 (GAD67)-positive neurons and performed cluster analysis. We identified five interneuron classes, each of them characterized by a distinct set of anatomical and physiological parameters. Cross-correlation analysis further revealed a direct relation between morphological and physiological properties indicating that dentate gyrus interneurons fall into functionally distinct classes which may differentially control neuronal network activity.

Excitatory and inhibitory synaptic connectivity to layer V fast-spiking interneurons in the freeze lesion model of cortical microgyria

A variety of major developmental cortical malformations are closely associated with clinically intractable epilepsy. Pathophysiological aspects of one such disorder, human polymicrogyria, can be modeled by making neocortical freeze lesions (FL) in neonatal rodents, resulting in the formation of microgyri. Previous studies showed enhanced excitatory and inhibitory synaptic transmission and connectivity in cortical layer V pyramidal neurons in the paramicrogyral cortex. In young adult transgenic mice that express green fluorescent protein (GFP) specifically in parvalbumin positive fast-spiking (FS) interneurons, we used laser scanning photostimulation (LSPS) of caged glutamate to map excitatory and inhibitory synaptic connectivity onto FS interneurons in layer V of paramicrogyral cortex in control and FL groups. The proportion of uncaging sites from which excitatory postsynaptic currents (EPSCs) could be evoked (hotspot ratio) increased slightly but significantly in FS cells of the FL vs. control cortex, while the mean amplitude of LSPS-evoked EPSCs at hotspots did not change. In contrast, the hotspot ratio of inhibitory postsynaptic currents (IPSCs) was significantly decreased in FS neurons of the FL cortex. These alterations in synaptic inputs onto FS interneurons may result in an enhanced inhibitory output. We conclude that alterations in synaptic connectivity to cortical layer V FS interneurons do not contribute to hyperexcitability of the FL model. Instead, the enhanced inhibitory output from these neurons may partially offset an earlier demonstrated increase in synaptic excitation of pyramidal cells and thereby maintain a relative balance between excitation and inhibition in the affected cortical circuitry.

Genetic dissection of GABAergic neural circuits in mouse neocortex

Diverse and flexible cortical functions rely on the ability of neural circuits to perform multiple types of neuronal computations. GABAergic inhibitory interneurons significantly contribute to this task by regulating the balance of activity, synaptic integration, spiking, synchrony, and oscillation in a neural ensemble. GABAergic interneurons display a high degree of cellular diversity in morphology, physiology, connectivity, and gene expression. A considerable number of subtypes of GABAergic interneurons diversify modes of cortical inhibition, enabling various types of information processing in the cortex. Thus, comprehensively understanding fate specification, circuit assembly, and physiological function of GABAergic interneurons is a key to elucidate the principles of cortical wiring and function. Recent advances in genetically encoded molecular tools have made a breakthrough to systematically study cortical circuitry at the molecular, cellular, circuit, and whole animal levels. However, the biggest obstacle to fully applying the power of these to analysis of GABAergic circuits was that there were no efficient and reliable methods to express them in subtypes of GABAergic interneurons. Here, I first summarize cortical interneuron diversity and current understanding of mechanisms, by which distinct classes of GABAergic interneurons are generated. I then review recent development in genetically encoded molecular tools for neural circuit research, and genetic targeting of GABAergic interneuron subtypes, particularly focusing on our recent effort to develop and characterize Cre/CreER knockin lines. Finally, I highlight recent success in genetic targeting of chandelier cells, the most unique and distinct GABAergic interneuron subtype, and discuss what kind of questions need to be addressed to understand development and function of cortical inhibitory circuits.

Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Developmental reduction of asynchronous GABA release from neocortical fast-spiking neurons

Delayed asynchronous release (AR) evoked by bursts of presynaptic action potentials (APs) occurs in certain types of hippocampal and neocortical inhibitory interneurons. Previous studies showed that AR provides long-lasting inhibition and desynchronizes the activity in postsynaptic cells. However, whether AR undergoes developmental change remains unknown. In this study, we performed whole-cell recording from fast-spiking (FS) interneurons and pyramidal cells (PCs) in prefrontal cortical slices obtained from juvenile and adult rats. In response to AP trains in FS neurons, AR occurred at their output synapses during both age periods, including FS autapses and FS-PC synapses; however, the AR strength was significantly weaker in adults than that in juveniles. Further experiments suggested that the reduction of AR in adult animals could be attributable to the rapid clearance of residual Ca(2+) from presynaptic terminals. Together, our results revealed that the AR strength was stronger at juvenile but weaker in adult, possibly resulting from changes in presynaptic Ca(2+) dynamics. AR changes may meet the needs of the neural network to generate different types of oscillations for cortical processing at distinct behavioral states.

Contrast dependence and differential contributions from somatostatin- and parvalbumin-expressing neurons to spatial integration in mouse V1

A characteristic feature in the primary visual cortex is that visual responses are suppressed as a stimulus extends beyond the classical receptive field. Here, we examined the role of inhibitory neurons expressing somatostatin (SOM⁺) or parvalbumin (PV⁺) on surround suppression and preferred receptive field size. We recorded multichannel extracellular activity in V1 of transgenic mice expressing channelrhodopsin in SOM⁺ neurons or PV⁺ neurons. Preferred size and surround suppression were measured using drifting square-wave gratings of varying radii and at two contrasts. Consistent with findings in primates, we found that the preferred size was larger for lower contrasts across all cortical depths, whereas the suppression index (SI) showed a trend to decrease with contrast. We then examined the effect of these metrics on units that were suppressed by photoactivation of either SOM⁺ or PV⁺ neurons. When activating SOM⁺ neurons, we found a significant increase in SI at cortical depths >400 μm, whereas activating PV⁺ neurons caused a trend toward lower SIs regardless of cortical depth. Conversely, activating PV⁺ neurons significantly increased preferred size across all cortical depths, similar to lowering contrast, whereas activating SOM⁺ neurons had no systematic effect on preferred size across all depths. These data suggest that SOM⁺ and PV⁺ neurons contribute differently to spatial integration. Our findings are compatible with the notion that SOM⁺ neurons mediate surround suppression, particularly in deeper cortex, whereas PV⁺ activation decreases the drive of the input to cortex and therefore resembles the effects on spatial integration of lowering contrast.

Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons

Cortical inhibitory neurons contact each other to form a network of inhibitory synaptic connections. Our knowledge of the connectivity pattern underlying this inhibitory network is, however, still incomplete. Here we discover a simple and complementary interaction scheme between three large molecularly distinct interneuron populations in mouse visual cortex: Parvalbumin expressing interneurons strongly inhibit one another but, surprisingly, provide little inhibition to other populations. In contrast, somatostatin expressing interneurons avoid inhibiting one another, yet strongly inhibit all other populations. Finally, vasoactive intestinal peptide expressing interneurons preferentially inhibit somatostatin interneurons. This scheme occurs in supra- and infra-granular layers, suggesting that inhibitory networks operate similarly at the input and output of visual cortex. Thus, as the specificity of connections between excitatory neurons forms the basis for the cortical canonical circuit, the scheme described here outlines a standard connectivity pattern among cortical inhibitory neurons.

Compartmentalization of GABAergic inhibition by dendritic spines

γ-aminobutyric acid-mediated (GABAergic) inhibition plays a critical role in shaping neuronal activity in the neocortex. Numerous experimental investigations have examined perisomatic inhibitory synapses, which control action potential output from pyramidal neurons. However, most inhibitory synapses in the neocortex are formed onto pyramidal cell dendrites, where theoretical studies suggest they may focally regulate cellular activity. The precision of GABAergic control over dendritic electrical and biochemical signaling is unknown. By using cell type-specific optical stimulation in combination with two-photon calcium (Ca(2+)) imaging, we show that somatostatin-expressing interneurons exert compartmentalized control over postsynaptic Ca(2+) signals within individual dendritic spines. This highly focal inhibitory action is mediated by a subset of GABAergic synapses that directly target spine heads. GABAergic inhibition thus participates in localized control of dendritic electrical and biochemical signaling.

Using diffusion anisotropy to characterize neuronal morphology in gray matter: the orientation distribution of axons and dendrites in the NeuroMorpho.org database

Accurate mathematical modeling is integral to the ability to interpret diffusion magnetic resonance (MR) imaging data in terms of cellular structure in brain gray matter (GM). In previous work, we derived expressions to facilitate the determination of the orientation distribution of axonal and dendritic processes from diffusion MR data. Here we utilize neuron reconstructions available in the NeuroMorpho database (www.neuromorpho.org) to assess the validity of the model we proposed by comparing morphological properties of the neurons to predictions based on diffusion MR simulations using the reconstructed neuron models. Initially, the method for directly determining neurite orientation distributions is shown to not depend on the line length used to quantify cylindrical elements. Further variability in neuron morphology is characterized relative to neuron type, species, and laboratory of origin. Subsequently, diffusion MR signals are simulated based on human neocortical neuron reconstructions. This reveals a bias in which diffusion MR data predict neuron orientation distributions to have artificially low anisotropy. This bias is shown to arise from shortcomings (already at relatively low diffusion weighting) in the Gaussian approximation of diffusion, in the presence of restrictive barriers, and data analysis methods involving higher moments of the cumulant expansion are shown to be capable of reducing the magnitude of the observed bias.

Beyond the frontiers of neuronal types

Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets, identifies a small optimal number of model archetypes. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes.

Diminished cortical inhibition in an aging mouse model of chronic tinnitus

Flavoprotein autofluorescence imaging was used to examine auditory cortical synaptic responses in aged animals with behavioral evidence of tinnitus and hearing loss. Mice were exposed to noise trauma at 1-3 months of age and were assessed for behavioral evidence of tinnitus and hearing loss immediately after the noise trauma and again at ~24-30 months of age. Within 2 months of the final behavioral assessment, auditory cortical synaptic transmission was examined in brain slices using electrical stimulation of putative thalamocortical afferents, and flavoprotein autofluorescence imaging was used to measure cortical activation. Noise-exposed animals showed a 68% increase in amplitude of cortical activation compared with controls (p = 0.008), and these animals showed a diminished sensitivity to GABA(A)ergic blockade (p = 0.008, using bath-applied 200 nm SR 95531 [6-Imino-3-(4-methoxyphenyl)-1(6H)-p yridazinebutanoic acid hydrobromide]). The strength of cortical activation was significantly correlated to the degree of tinnitus behavior, assessed via a loss of gap detection in a startle paradigm. The decrease in GABA(A) sensitivity was greater in the regions of the cortex farther away from the stimulation site, potentially reflecting a greater sensitivity of corticocortical versus thalamocortical projections to the effects of noise trauma. Finally, there was no relationship between auditory cortical activation and activation of the somatosensory cortex in the same slices, suggesting that the increases in auditory cortical activation were not attributable to a generalized hyperexcitable state in noise-exposed animals. These data suggest that noise trauma can cause long-lasting changes in the auditory cortical physiology and may provide specific targets to ameliorate the effects of chronic tinnitus.

Digital morphometry of rat cerebellar climbing fibers reveals distinct branch and bouton types

Cerebellar climbing fibers (CFs) provide powerful excitatory input to Purkinje cells (PCs), which represent the sole output of the cerebellar cortex. Recent discoveries suggest that CFs have information-rich signaling properties important for cerebellar function, beyond eliciting the well known all-or-none PC complex spike. CF morphology has not been quantitatively analyzed at the same level of detail as its biophysical properties. Because morphology can greatly influence function, including the capacity for information processing, it is important to understand CF branching structure in detail, as well as its variability across and within arbors. We have digitally reconstructed 68 rat CFs labeled using biotinylated dextran amine injected into the inferior olive and comprehensively quantified their morphology. CF structure was considerably diverse even within the same anatomical regions. Distinctly identifiable primary, tendril, and distal branches could be operationally differentiated by the relative size of the subtrees at their initial bifurcations. Additionally, primary branches were more directed toward the cortical surface and had fewer and less pronounced synaptic boutons, suggesting they prioritize efficient and reliable signal propagation. Tendril and distal branches were spatially segregated and bouton dense, indicating specialization in signal transmission. Furthermore, CFs systematically targeted molecular layer interneuron cell bodies, especially at terminal boutons, potentially instantiating feedforward inhibition on PCs. This study offers the most detailed and comprehensive characterization of CF morphology to date. The reconstruction files and metadata are publicly distributed at NeuroMorpho.org.

Subgroups of parvalbumin-expressing interneurons in layers 2/3 of the visual cortex

The input, processing, and output characteristics of inhibitory interneurons help shape information flow through layers 2/3 of the visual cortex. Parvalbumin (PV)-positive interneurons modulate and synchronize the gain and dynamic responsiveness of pyramidal neurons. To define the diversity of PV interneurons in layers 2/3 of the developing visual cortex, we characterized their passive and active membrane properties. Using Ward's and k-means multidimensional clustering, we identified four PV interneuron subgroups. The most notable difference between the subgroups was their firing patterns in response to moderate stimuli just above rheobase. Two subgroups showed regular and continuous firing at all stimulus intensities above rheobase. The difference between these two continuously firing subgroups was that one fired at much higher frequencies and transitioned into this high-frequency firing rate at or near rheobase. The two other subgroups showed irregular, stuttering firing patterns just above rheobase. Both of these subgroups typically transitioned to regular and continuous firing at intense stimulations, but one of these subgroups, the strongly stuttering subgroup, showed irregular firing across a wider range of stimulus intensities and firing frequencies. The four subgroups also differed in excitatory synaptic input, providing independent support for the classification of subgroups. The subgroups of PV interneurons identified here would respond differently to inputs of varying intensity and frequency, generating diverse patterns of PV inhibition in the developing neural circuit.

Characterization of Type I and Type II nNOS-Expressing Interneurons in the Barrel Cortex of Mouse

In the neocortex, neuronal nitric oxide (NO) synthase (nNOS) is essentially expressed in two classes of GABAergic neurons: type I neurons displaying high levels of expression and type II neurons displaying weaker expression. Using immunocytochemistry in mice expressing GFP under the control of the glutamic acid decarboxylase 67k (GAD67) promoter, we studied the distribution of type I and type II neurons in the barrel cortex and their expression of parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP). We found that type I neurons were predominantly located in deeper layers and expressed SOM (91.5%) while type II neurons were concentrated in layer II/III and VI and expressed PV (17.7%), SOM (18.7%), and VIP (10.2%). We then characterized neurons expressing nNOS mRNA (n = 42 cells) ex vivo, using whole-cell recordings coupled to single-cell reverse transcription-PCR and biocytin labeling. Unsupervised cluster analysis of this sample disclosed four classes. One cluster (n = 7) corresponded to large, deep layer neurons, displaying a high expression of SOM (85.7%) and was thus very likely to correspond to type I neurons. The three other clusters were identified as putative type II cells and corresponded to neurogliaform-like interneurons (n = 19), deep layer neurons expressing PV or SOM (n = 9), and neurons expressing VIP (n = 7). Finally, we performed nNOS immunohistochemistry on mouse lines in which GFP labeling revealed the expression of two specific developmental genes (Lhx6 and 5-HT_3A). We found that type I neurons expressed Lhx6 but never 5-HT_3A, indicating that they originate in the medial ganglionic eminence (MGE). Type II neurons expressed Lhx6 (63%) and 5-HT_3A (34.4%) supporting their derivation either from the MGE or from the caudal ganglionic eminence (CGE) and the entopeduncular and dorsal preoptic areas. Together, our results in the barrel cortex of mouse support the view that type I neurons form a specific class of SOM-expressing neurons while type II neurons comprise at least three classes.

Diversity of GABAergic interneurons in layer VIa and VIb of mouse barrel cortex

Neocortical layer VI modulates the thalamocortical transfer of information and has a significant impact on sensory processing. This function implicates local γ-aminobutyric acidergic (GABAergic) interneurons that have only been partly described at the present time. Here, we characterized 85 layer VI GABAergic interneurons in acute slices of mouse somatosensory barrel cortex, using whole-cell current-clamp recordings, single-cell reverse transcription-polymerase chain reaction, and biocytin labeling followed by Neurolucida reconstructions. Unsupervised clustering based on electrophysiological molecular and morphological properties disclosed 4 types of interneurons. The 2 major classes were fast-spiking cells transcribing parvalbumin (PV) (51%) and adapting interneurons transcribing somatostatin (SOM) (26%). The third population (18%) transcribed neuropeptide Y (NPY) and appeared very similar to neurogliaform cells. The last class (5%) was constituted by well-segregated GABAergic interneurons transcribing vasoactive intestinal peptide (VIP). Using transgenic mice expressing GFP under the control of the glutamic acid decarboxylase 67k (GAD67) promoter, we investigated the densities of GABAergic cells immunolabeled against PV, SOM, VIP, and NPY through the depth of layer VI. This analysis revealed that PV and NPY translating interneurons concentrate in the upper and lower parts of layer VI, respectively. This study provides an extensive characterization of the properties of layer VI interneurons.