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

Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks

We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.

Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint

We present a quantitative strategy to identify all projection neuron types from a given region with statistically different patterns of anatomical targeting. We first validate the technique with mouse primary motor cortex layer 6 data, yielding two clusters consistent with cortico-thalamic and intra-telencephalic neurons. We next analyze the presubiculum, a less-explored region, identifying five classes of projecting neurons with unique patterns of divergence, convergence, and specificity. We report several findings: individual classes target multiple subregions along defined functions; all hypothalamic regions are exclusively targeted by the same class also invading midbrain and agranular retrosplenial cortex; Cornu Ammonis receives input from a single class of presubicular axons also projecting to granular retrosplenial cortex; path distances from the presubiculum to the same targets differ significantly between classes, as do the path distances to distinct targets within most classes; the identified classes have highly non-uniform abundances; and presubicular somata are topographically segregated among classes. This study thus demonstrates that statistically distinct projections shed light on the functional organization of their circuit.

Afferent convergence to a shared population of interneuron AMPA receptors

Precise alignment of pre- and postsynaptic elements optimizes the activation of glutamate receptors at excitatory synapses. Nonetheless, glutamate that diffuses out of the synaptic cleft can have actions at distant receptors, a mode of transmission called spillover. To uncover the extrasynaptic actions of glutamate, we localized AMPA receptors (AMPARs) mediating spillover transmission between climbing fibers and molecular layer interneurons in the cerebellar cortex. We found that climbing fiber spillover generates calcium transients mediated by Ca2+-permeable AMPARs at parallel fiber synapses. Spillover occludes parallel fiber synaptic currents, indicating that separate, independently regulated afferent pathways converge onto a common pool of AMPARs. Together these findings demonstrate a circuit motif wherein glutamate 'spill-in' from an unconnected afferent pathway co-opts synaptic receptors, allowing activation of postsynaptic AMPARs even when canonical glutamate release is suppressed.

A Method for Estimating the Potential Synaptic Connections Between Axons and Dendrites From 2D Neuronal Images

Computational neuroscience aims to model, reproduce, and predict network dynamics for different neuronal ensembles by distilling knowledge derived from electrophysiological and morphological evidence. However, analyses and simulations often remain critically limited by the sparsity of direct experimental constraints on essential parameters, such as electron microscopy and electrophysiology pair/multiple recording evidence of connectivity statistics. Notably, available data are particularly scarce regarding quantitative information on synaptic connections among identified neuronal types. Here, we present a user-friendly data-driven pipeline to estimate connection probabilities, number of contacts per connected pair, and distances from the pre- and postsynaptic somas along the axonal and dendritic paths from commonly available two-dimensional tracings and other broadly accessible measurements. The described procedure does not require any computational background and is accessible to all neuroscientists. This protocol therefore fills the important gap from neuronal morphology to circuit organization and can be applied to many different neural systems, brain regions, animal species, and data sources. Graphic abstract: The processing protocol from 2D reconstructions to quantitated synaptic connections.

Rack1 Controls Parallel Fiber-Purkinje Cell Synaptogenesis and Synaptic Transmission

Purkinje cells (PCs) in the cerebellum receive two excitatory afferents including granule cells-derived parallel fiber (PF) and the climbing fiber. Scaffolding protein Rack1 is highly expressed in the cerebellar PCs. Here, we found delayed formation of specific cerebellar vermis lobule and impaired motor coordination in PC-specific Rack1 conditional knockout mice. Our studies further revealed that Rack1 is essential for PF–PC synapse formation. In addition, Rack1 plays a critical role in regulating synaptic plasticity and long-term depression (LTD) induction of PF–PC synapses without changing the expression of postsynaptic proteins. Together, we have discovered Rack1 as the crucial molecule that controls PF–PC synaptogenesis and synaptic plasticity. Our studies provide a novel molecular insight into the mechanisms underlying the neural development and neuroplasticity in the cerebellum.

An open repository for single-cell reconstructions of the brain forest

NeuroMorpho.Org was launched in 2006 to provide unhindered access to any and all digital tracings of neuronal morphology that researchers were willing to share freely upon request. Today this database is the largest public inventory of cellular reconstructions in neuroscience with a content of over 80,000 neurons and glia from a representative diversity of animal species, anatomical regions, and experimental methods. Datasets continuously contributed by hundreds of laboratories worldwide are centrally curated, converted into a common non-proprietary format, morphometrically quantified, and annotated with comprehensive metadata. Users download digital reconstructions for a variety of scientific applications including visualization, classification, analysis, and simulations. With more than 1,000 peer-reviewed publications describing data stored in or utilizing data retrieved from NeuroMorpho.Org, this ever-growing repository can already be considered a mature resource for neuroscience.

Metrics for comparing neuronal tree shapes based on persistent homology

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities-Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework.

AII amacrine cells: quantitative reconstruction and morphometric analysis of electrophysiologically identified cells in live rat retinal slices imaged with multi-photon excitation microscopy

AII amacrine cells have been found in all mammalian retinas examined and play an important role for visual processing under both scotopic and photopic conditions. Whereas ultrastructural investigations have provided a detailed understanding of synaptic connectivity, there is little information available with respect to quantitative properties and variation of cellular morphology. Here, we performed whole-cell recordings from AII amacrine cells in rat retinal slices and filled the cells with fluorescent dyes. Multi-photon excitation microscopy was used to acquire image stacks and after deconvolution, we performed quantitative morphological reconstruction by computer-aided manual tracing. We reconstructed and performed morphometric analysis on 43 AII amacrine cells, with a focus on branching pattern, dendritic lengths and diameters, surface area, and number and distribution of dendritic varicosities. Compared to previous descriptions, the most surprising result was the considerable extent of branching, with the maximum branch order ranging from approximately 10-40. We found that AII amacrine cells conform to a recently described general structural design principle for neural arbors, where arbor density decreases proportionally to increasing territory size. We confirmed and quantified the bi-stratified morphology of AII amacrine cells by analyzing the arborizations as a function of retinal localization or with Sholl spheres. Principal component and cluster analysis revealed no evidence for morphological subtypes of AII amacrines. These results establish a database of morphometric properties important for studies of development, regeneration, degeneration, and disease processes, as well as a workflow compatible with compartmental modeling.

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

Reappraisal of Bergmann glial cells as modulators of cerebellar circuit function

Just as there is a huge morphological and functional diversity of neuron types specialized for specific aspects of information processing in the brain, astrocytes have equally distinct morphologies and functions that aid optimal functioning of the circuits in which they are embedded. One type of astrocyte, the Bergmann glial cell (BG) of the cerebellum, is a prime example of a highly diversified astrocyte type, the architecture of which is adapted to the cerebellar circuit and facilitates an impressive range of functions that optimize information processing in the adult brain. In this review we expand on the function of the BG in the cerebellum to highlight the importance of astrocytes not only in housekeeping functions, but also in contributing to plasticity and information processing in the cerebellum.

Towards the automatic classification of neurons

The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and the availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-laboratory integration.

The importance of metadata to assess information content in digital reconstructions of neuronal morphology

Digital reconstructions of axonal and dendritic arbors provide a powerful representation of neuronal morphology in formats amenable to quantitative analysis, computational modeling, and data mining. Reconstructed files, however, require adequate metadata to identify the appropriate animal species, developmental stage, brain region, and neuron type. Moreover, experimental details about tissue processing, neurite visualization and microscopic imaging are essential to assess the information content of digital morphologies. Typical morphological reconstructions only partially capture the underlying biological reality. Tracings are often limited to certain domains (e.g., dendrites and not axons), may be incomplete due to tissue sectioning, imperfect staining, and limited imaging resolution, or can disregard aspects irrelevant to their specific scientific focus (such as branch thickness or depth). Gauging these factors is critical in subsequent data reuse and comparison. NeuroMorpho.Org is a central repository of reconstructions from many laboratories and experimental conditions. Here, we introduce substantial additions to the existing metadata annotation aimed to describe the completeness of the reconstructed neurons in NeuroMorpho.Org. These expanded metadata form a suitable basis for effective description of neuromorphological data.

Statistical analysis and data mining of digital reconstructions of dendritic morphologies

Neuronal morphology is diverse among animal species, developmental stages, brain regions, and cell types. The geometry of individual neurons also varies substantially even within the same cell class. Moreover, specific histological, imaging, and reconstruction methodologies can differentially affect morphometric measures. The quantitative characterization of neuronal arbors is necessary for in-depth understanding of the structure-function relationship in nervous systems. The large collection of community-contributed digitally reconstructed neurons available at NeuroMorpho.Org constitutes a “big data” research opportunity for neuroscience discovery beyond the approaches typically pursued in single laboratories. To illustrate these potential and related challenges, we present a database-wide statistical analysis of dendritic arbors enabling the quantification of major morphological similarities and differences across broadly adopted metadata categories. Furthermore, we adopt a complementary unsupervised approach based on clustering and dimensionality reduction to identify the main morphological parameters leading to the most statistically informative structural classification. We find that specific combinations of measures related to branching density, overall size, tortuosity, bifurcation angles, arbor flatness, and topological asymmetry can capture anatomically and functionally relevant features of dendritic trees. The reported results only represent a small fraction of the relationships available for data exploration and hypothesis testing enabled by sharing of digital morphological reconstructions.

The Cav3-Kv4 complex acts as a calcium sensor to maintain inhibitory charge transfer during extracellular calcium fluctuations

Synaptic transmission and neuronal excitability depend on the concentration of extracellular calcium ([Ca](o)), yet repetitive synaptic input is known to decrease [Ca](o) in numerous brain regions. In the cerebellar molecular layer, synaptic input reduces [Ca](o) by up to 0.4 mm in the vicinity of stellate cell interneurons and Purkinje cell dendrites. The mechanisms used to maintain network excitability and Purkinje cell output in the face of this rapid change in calcium gradient have remained an enigma. Here we use single and dual patch recordings in an in vitro slice preparation of Sprague Dawley rats to investigate the effects of physiological decreases in [Ca](o) on the excitability of cerebellar stellate cells and their inhibitory regulation of Purkinje cells. We find that a Ca(v)3-K(v)4 ion channel complex expressed in stellate cells acts as a calcium sensor that responds to a decrease in [Ca]o by dynamically adjusting stellate cell output to maintain inhibitory charge transfer to Purkinje cells. The Ca(v)3-K(v)4 complex thus enables an adaptive regulation of inhibitory input to Purkinje cells during fluctuations in [Ca](o), providing a homeostatic control mechanism to regulate Purkinje cell excitability during repetitive afferent activity.

New insights on vertebrate olivo-cerebellar climbing fibers from computerized morphological reconstructions

Characterization of neuronal connectivity is essential to understanding the architecture of the animal nervous system. Specific labeling and imaging techniques can visualize axons and dendrites of single nerve cells. Two-dimensional manual drawing has long been used to describe the morphology of labeled neuronal elements. However, quantitative morphometry, which is essential to understanding functional significance, cannot be readily extracted unless the detailed neuronal geometry is comprehensively reconstructed in three-dimensional space. We have recently applied an accurate and robust digital reconstruction system to cerebellar climbing fibers, which form highly dense and complex terminal arbors as one of the strongest presynaptic endings in the vertebrate nervous system. Resulting statistical analysis has shown how climbing fibers morphology is special in comparison to other axonal terminals. While thick primary branches may convey excitation quickly and faithfully to the far ends, thin tendril branches, which have a larger bouton density, form the majority of presynaptic outputs. This data set, now publicly available from NeuroMorpho.Org for further modeling and analysis, may constitute the first detailed and comprehensive digital reconstruction of the complete axonal terminal field with identified branch types and full accounting of boutons for any neuronal class in the vertebrate brain.

Spillover-mediated feedforward inhibition functionally segregates interneuron activity

Neurotransmitter spillover represents a form of neural transmission not restricted to morphologically defined synaptic connections. Communication between climbing fibers (CFs) and molecular layer interneurons (MLIs) in the cerebellum is mediated exclusively by glutamate spillover. Here, we show how CF stimulation functionally segregates MLIs based on their location relative to glutamate release. Excitation of MLIs that reside within the domain of spillover diffusion coordinates inhibition of MLIs outside the diffusion limit. CF excitation of MLIs is dependent on extrasynaptic NMDA receptors that enhance the spatial and temporal spread of CF signaling. Activity mediated by functionally segregated MLIs converges onto neighboring Purkinje cells (PCs) to generate a long-lasting biphasic change in inhibition. These data demonstrate how glutamate release from single CFs modulates excitability of neighboring PCs, thus expanding the influence of CFs on cerebellar cortical activity in a manner not predicted by anatomical connectivity.

Structural plasticity of climbing fibers and the growth-associated protein GAP-43

Structural plasticity occurs physiologically or after brain damage to adapt or re-establish proper synaptic connections. This capacity depends on several intrinsic and extrinsic determinants that differ between neuron types. We reviewed the significant endogenous regenerative potential of the neurons of the inferior olive (IO) in the adult rodent brain and the structural remodeling of the terminal arbor of their axons, the climbing fiber (CF), under various experimental conditions, focusing on the growth-associated protein GAP-43. CFs undergo remarkable collateral sprouting in the presence of denervated Purkinje cells (PCs) that are available for new innervation. In addition, severed olivo-cerebellar axons regenerate across the white matter through a graft of embryonic Schwann cells. In contrast, CFs undergo a regressive modification when their target is deleted. In vivo knockdown of GAP-43 in olivary neurons, leads to the atrophy of their CFs and a reduction in the ability to sprout toward surrounding denervated PCs. These findings demonstrate that GAP-43 is essential for promoting denervation-induced sprouting and maintaining normal CF architecture.