Giorgio A. Ascoli - US grants

DESCRIPTION: (Applicant's Abstract) Despite a general agreement among neuroscientists that dendritic morphology plays an important role in shaping cellular physiology and network connectivity, computational tools for detailed neuromorphological modeling are so far lacking. Such a gap is particularly surprising considering the vast amount of experimental data on the three-dimensional shape of many neuronal classes available in the literature, and the increasingly powerful sophistication of computer graphics and virtual reality. This research project aims at filling this gap. Cajal envisioned neuronal shape as determined by a finite number of intrinsic phenomena, modulated by the extrinsic effect of the environment. Based on this notion, several local rules correlating morphological parameters (e.g. branch diameter and length) have proved to be powerful and parsimonious descriptors of specific aspects of dendritic topology. We are using these successful correlations, together with global geometrical constraints, to implement descriptive algorithms for dendritic morphology. These algorithms will be assembled into a software package, named L-Neuron, for the generation and study of anatomically plausible neuronal analogs. Our implementation is based on L-system, a well-known mathematical formalism particularly suitable to describe branching and recursive structures, and extensively developed in computer graphics. L-Neuron will use experimental distributions of parameters from real-cell anatomical data to generate virtual neurons of various morphological classes. Within each class, the statistically constrained stochastic implementation of the algorithm will produce multiple, non-identical neurons. The generation of sets of virtual neurons is biologically relevant because it discriminates between important morphological parameters and emergent byproducts, which represent redundancies. If the algorithm actually produces accurate and realistic structures, it must contain all the required information and thus completely describes the original morphological family. If there are residual discrepancies between virtual and real neurons, their analysis may lead to the discovery of new geometric constraints and quantitative correlations between dendritic parameters. Generating complete models of dendritic geometry in virtual reality thus stimulates the development of analytical strategies to test whether the virtual neurons are morphologically equivalent to the real ones. L-Neuron will output neuronal structures into various formats, including virtual reality, standard graphic, and anatomical files, also used by compartmental modeling programs such as GENESIS. This variety of options will allow the display, dynamical rendering and quantitative analysis of data as well as their efficient exchange among research groups. The limitation of L-Neuron consists in being oriented toward single-cell analysis, thus making it less suitable for studying the effect of neuronal morphology on network connectivity. However, the simplicity of this system also represents an important advantage because it allows the analysis of the influence of specific intrinsic and extrinsic determinants on neuronal shape, and consequently on neuronal electrophysiology. We believe that this package, portable to all major platforms and freely distributed, will further neuroanatomy, computational modeling, and scientific education.

brain morphology; hippocampus; biomedical resource; clinical research;

DESCRIPTION (provided by applicant): CA3 pyramidal cells (CA3pcs) constitute a central crossroad of synaptic integration in the hippocampus, and play a key role in spatial mapping and memory storage. CA3pcs are monosynaptically excited by the entorhinal cortex, dentate granule cells, and other CA3pcs. The electrophysiological repertoire of CA3pcs includes single spiking and bursting, spanning a broad range of frequencies. Despite a general understanding of the anatomy and physiology of CA3pcs, little is known about the correspondence between a given pattern of synaptic inputs and the resulting firing output. This information, which is essential to relate hippocampal activity and function, constitutes the main goal of this project. First, we will investigate CA3pc dendrite biophysics (passive properties, channel distributions and kinetics), and the unitary synaptic inputs from each pathway. This will be achieved with voltage- and current-clamp recordings, calcium imaging, and the creation of a detailed, data-driven computational model. Next, the firing patterns of CA3pcs will be examined in response to systematic combinations of excitatory inputs. Surgically and pharmacologically isolated pathways will be stimulated extracellularly at various intensities and frequencies, while recording from individual CA3pcs. Corresponding compartmental simulations, implemented and validated against the experiments, will extensively characterize the computational properties of CA3pcs with respect to non-linear summation, pathway specificity, and coincidence detection of synaptic input. The public health relevance of this project directly relates to the mission of the NIA. Malfunction of the hippocampus is linked to devastating age-related conditions such as Alzheimer's disease. The combination of state-of-the-art experimental techniques with the ever-increasing computational power of biophysical modeling will accelerate research progress and help develop highly trained neuroscientists. In addition to the dissemination of results in conferences and peer-reviewed publications, all models will be publicly distributed through internet archives.

Proposal No: 0747864
PI: Giorgio A. Ascoli

Abstract:

This award supports the preparation and sharing of computational neuroscience data as part of an exploratory activity aimed at catalyzing rapid and innovative advances in computational neuroscience and related fields. The data to be shared in this project are physiological and anatomical data from the rat hippocampus, including (1) recordings from hippocampal CA1 neurons during open field foraging, (2) simultaneous intracellular and extracellular in vivo recordings from CA1 pyramidal cells and histological identities of those neurons, (3) quantitative information on the cellular connectivity of the hippocampal formation, and (4) axonal reconstruction data from in vivo preparations. Anatomical and physiological data will be cross-annotated to facilitate browsing and integration, and provided in a form that is compatible with widely used simulators. It is anticipated that these data will be useful for developing anatomically and physiologically realistic neural networks and understanding emergent behavior of neuronal populations, in particular, the mechanisms of memory.

[unreadable] DESCRIPTION (provided by applicant): Quantitative neuroanatomy is benefiting greatly from the integration of microscopy with increasingly powerful informatics. High resolution images of wide field histological preparations are captured and stored in the gigapixel range, while, at the cellular level, neuronal arborizations can be digitally reconstructed for morphometric analysis and computational modeling. The investigation of the cross-scale relation between system and cellular level neuroanatomy, however, has been so far largely limited to qualitative considerations. The present neurotechnology project will fill this gap and integrate the three-dimensional representation of brain regions and neuronal morphology focusing on the rat hippocampus as an exemplar structure of wide interest. In particular, a high resolution spatial map of all cytoarchitectural subregions of the hippocampal complex will be constructed from Nissl stained thin sections. Over one hundred digitally reconstructed neurons (including both excitatory and inhibitory cells) will be embedded and replicated throughout the full septotemporal extent of the hippocampus according to their appropriate location and orientation. Axonal and dendritic densities and volume occupancies will be calculated for each layer and along the transverse and longitudinal directions. Axodendritic overlaps will be measured for each pair of major cellular classes to estimate the matrix of potential synaptic connectivity for the complete hippocampal network. The maps for the whole hippocampal complex and full cellular detail will be made publicly available along with the underlying raw data and software source code. The hippocampus is intensely studied for its role in learning and memory and impairment in diseases such as epilepsy and Alzheimer's. A wealth of data is accumulating on its molecular and biophysical properties both in physiological and pathological conditions. This project will provide an anatomical framework to integrate hippocampal knowledge from the cellular to the system level for both experimental and computational neuroscientists. The techniques and research approach developed in this exploratory/developmental study will be extensible to other rat brain regions, and eventually to the whole mammalian central nervous system. PUBLIC HEALTH RELEVANCE The connection between the cellular and system levels of neuroanatomical analysis is a fundamental factor in the structure-function relationship of both the normal and diseased central nervous system. This cross-scale connection can seldom be quantitatively characterized because of the current lack of analytical tools and experimental preparations simultaneously suited for both single cells and entire brain regions. By virtually bridging this gap in a detailed digital montage of the mammalian hippocampus, we will demonstrate the feasibility of synthesizing vast amounts of neuroscience data while providing precise quantitative estimates for essential features of a structure involved in learning, memory, and devastating conditions such as epilepsy and Alzheimer's disease. [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Dendritic and axonal morphologies play fundamental roles in physiological brain function and pathological dysfunction by affecting synaptic integration, spike train transmission, and circuit connectivity. Incorporating existing and forthcoming experimental data into accurate, full-scale, and biologically plausible neural network simulations is important for quantitatively bridging the sub-cellular and systems-levels. We successfully designed, implemented, and freely distributed to the community computer software and databases to reconstruct, analyze, visualize, simulate, and share the 3D tree-like shape of neurons from many labeling and visualization techniques, developmental stages, and experimental conditions. We imaged by light microscopy, digitally traced, and shared new data, and we provided our peers with the electronic means of freely doing the same. Moreover, we combined those data with computational models of membrane biophysics to investigate the neuronal structure-activity relationship. We propose to expand this research approach with two specific aims. The first is to augment the power, scope, and usability of the NeuroMorpho.Org repository of digital tracings. We plan to triple the number of shared reconstructions, adding new species, brain regions, and neuron types. Moreover, we will enhance the search functionality with a semantic engine using state-of-the-art ontologies. We will also extend the domain and format of distributed data to include circuitry, multi-channel information, and temporal sequences. The second aim is to develop a new knowledge base of neuron types in the hippocampus and entorhinal cortex by quantifying their morphological, physiological, and molecular properties from published reports. The hippocampal formation is one of the most studied brain regions, underlies autobiographic memory storage and spatial representation, and is prominently involved in devastating neurological disease, including epilepsy and Alzheimer's. Yet, our conceptual understanding of how the hippocampus works is limited compared to the wealth of available knowledge about its neurons, because it is difficult to find and integrate all relevant data scattered in thousands of papers. We will identify all published information and annotate it with specific pointers to the source documents in the peer- reviewed literature. The resulting open-source portal (Hippocampome.Org) will enable the derivation of potential circuit connectivity and the predictive simulation of network-wide spiking activity. We will make this application especially relevant to neuropathology by linking specific neuron types to diseases involving the hippocampus, and demonstrate its potential with a new model of learning disabilities based on impaired structural plasticity.

DESCRIPTION (provided by applicant): Cerebrovascular diseases are a leading cause of death and long term disability in the United States. Detailed knowledge of the human brain's vascular architecture is important in relating hemodynamics to physiopathology, and can help optimize the diagnosis and treatment of cerebrovascular disease. This exploratory collaboration with the UCLA Center for Computational Biology (CCB) will develop a framework for the quantitative characterization of brain vasculature and its variability among normal subjects from non-invasive magnetic resonance angiography (MRA). The project is organized around three specific aims, broadly corresponding to angiographic reconstruction, analysis, and probabilistic atlasing: Aim1 - Digital reconstruction of a normative set of MRA: a) Build 3D vascular reconstructions from MRA data of normal subjects. b) Evaluate and verify the vascular reconstructions using a battery of reliability tests. Aim2 - Statistical morphometric analysis: a) Characterize the geometry and branching topology of the arterial structures of the brain. b) Conduct statistical analysis to establish inter-subject variability of the vascular structural characteristics and test for differences between males and females, and left/right hemispheres. Aim3 - Probabilistic angiographic atlas: a) Construct a brain vascular atlas using the CCB registration pipeline and integrate with the other modalities of the CCB brain atlas. At the completion of the project, the anonymized raw image stacks, the vascular reconstructions and the morphometric characterizations will be made available to the scientific community through the CCB pipeline. The datasets and information generated and disseminated will be of broad and extreme value. PUBLIC HEALTH RELEVANCE: This project aims at constructing brain vascular reconstructions from magnetic resonance angiography data and constructing a vascular atlas of the brain. Both the algorithms and data generated and disseminated in this project will be of broad and high value for a better understanding of the mechanisms involved in the initiation, progression and outcome of cerebrovascular diseases such as stroke and aneurysms. This knowledge is important for improving current evaluation and treatment of patients with cerebrovascular disease.

This EAGER project will provide all neuroscientists and computer scientists with much needed reliable, repeatable, high-throughput, quantitative data to begin piecing together the complex puzzle of the neural structure-activity-function relationship. Recent breakthroughs in genetic labeling and microscopic imaging have energized the research community with unprecedented optimism in the ability to collect the enormous amount of data that is necessary to quantify statistically representative samples of neurons in multiple species, developmental stages, and conditions, across the overwhelming variety of cell types throughout the nervous system. Due to the sheer extent and branching complexity of axonal and dendritic arbors, however, the bottleneck in the advancement of progress in this endeavor is no longer raw data acquisition, but the digital reconstruction of the corresponding morphology. The BigNeuron initiative (bigneuron.org) promises to consolidate and further advance the gains in automated tracing, and the ongoing development of multiple algorithms provides a strong insurance of robustness. Now, formulating a consensus from these alternative results is critical to prevent dispersive fragmentation and thrust the field into a new era of discovery.

BigNeuron is porting all available algorithms for automated reconstruction of neuronal morphology under a unified open source framework. Each of the multiple BigNeuron algorithms will create non-identical digital tracings from every neuronal image stack. A remaining unsolved step is to morph these multiple variants into a single optimal consensus reconstruction that would de facto become a community standard. While human expertise is currently the gold standard (and the ground truth may not be known), even the reconstructions of the exact same neuron by two trained human operators will not be identical and need to be reconciled. Thus, to ensure scalable to whole-brain throughput, an automated method is needed to transform a collection of non-identical tracing versions into a consensus reconstruction, ideally with a confidence (or variance) associated with each branch. The specific aims of this project are to design, implement, test, refine, and deploy a method to generate a consensus neuronal reconstruction from the multiple digital tracings produced by each of the available algorithms. Specifically, the team will first create a draft working algorithm by synergistically combining two recently introduced complementary approaches. The resulting initial procedure for morphological consensus production will serve as straw man for community discussion in several meetings and workshops. After expert feedback and new ideas have been incorporated, the consensus generation process will be finalized for incorporation into the BigNeuron pipeline. Results from this project will be available to researchers and science educational users through the NeuroMorpho.Org website.

PROJECT SUMMARY AND ABSTRACT A comprehensive understanding neuronal cell type diversity is an essential guide to selective manipulation and illuminating cell type specific functional contributions toward health and disease. Accordingly, the Brain Initiative Cell Census Network (BICCN) is unifying the efforts of laboratories with unique expertise in anatomy, genetics, electrophysiology, and function to classify neurons and create a common 3D atlas with integrated cell type data. To this end, our proposed collaboratory aims to anatomically characterize neuronal cell types of the mouse limbic system. Using mesoscale quadruple retrograde tracing, we will initially characterize cell types based on the anatomical location of their connectional start and end points [e.g., ACB(contralateral)?BLAa?ACB(ipsilateral)]. A two-step cre-dependent AAV tracing strategy using advanced viral tools will subsequently validate and refine specific axonal projections, collaterals, and projection fields [e.g., ACB/X/Y?BLAa?ACB/X/Y]. Injections of G-deleted rabies in CLARITY-processed tissue will label morphological features of cell types. Cre-dependent TRIO viral tracing will determine discrete inputs to each cell type, providing deeper characterization of connectivity. Novel TRIO using flp recombinase in cre- dependent mice will define projection patterns of genetically-defined cell types. Newly constructed AAV and rabies viruses tagged to spaghetti monster fluorescent proteins, applied in combination with Expansion Microscopy and multiphoton imaging, will determine the spatial organization of different synaptic inputs to the cell types. Collectively, experiments will reveal cell type anatomic location, morphology, and comprehensive connectivity. Initial efforts will focus on the limbic system, with the design extensible to neuronal characterization of the entire brain. A web-based visualization platform will be developed to enable viewing and analysis of cell type anatomy data in 2D and 3D. An online visualization tool similar in function to our iConnectome viewer will present quadruple retrograde and TRIO tracing images. Digitized, reconstructed quadruple retrograde, cre-AAV, and TRIO labeling will be placed atop the Allen Reference Atlas (ARA) to create an online 2D connectivity map, allowing easy comparison of cell type specific inputs and outputs. Common Coordinate Framework (CCF) registration and reconstruction of cre-AAV labeling experiments will provide the cell type specific 3D context of projections, with input and morphological information integrated into the viewer. An interactive, weighted and directed matrix will present an intuitive visualization of all connectivity data. 3D reconstructed neurons will also be hosted on Neuromorpho.org for interspecies comparison. Our current informatics pipelines will be extended and optimized to support the proposed viewer features. We expect our technologies to elucidate diverse cell type specific networks and provide foundations for the overarching goal of the BICCN of creating a comprehensive 3D cell type atlas.

Abstract The specification and dynamic modification of subtype specific dendritic architecture not only dictates how distinct classes of neurons form functional connections with other neurons, but also directly influences subtype- specific computational properties. Dendritic form, and by extension function, is chiefly mediated by subcellular organization and dynamics of cytoskeletal components. Thus, identifying molecular factors and cellular processes that regulate subtype specific dendritogenesis is essential to our understanding of the mechanistic links between cytoskeletal organization and neuronal form and function in both health and neuropathologies. Protein homeostasis, or proteostasis, is essential to cellular health and as a surveillance system against neurotoxic aggregates implicated in numerous neurodegenerative disease states. Despite this importance, relatively little is known regarding the normal developmental roles of proteostasis regulatory pathways in driving dendritic diversity or subtype-specific cytoskeletal organization. Our work in the previous funding cycle provided the foundations for combining neurogenetic manipulations, in vivo spatio-temporal multichannel imaging and computational techniques for multichannel and time-varying neuronal reconstructions of subtype specific dendritic cytoskeletal architectures. This strategy yielded novel insights into local cytoskeletal control mechanisms regulating dendritic arbor diversity that could not have been solely predicted or quantitatively characterized without the synergy of these approaches. For this next funding cycle, we hypothesize that the evolutionarily conserved PP2A phosphatase and TRiC/CCT chaperonin complexes function as essential proteostasis regulators that exert control over the spatiotemporal organization and dynamics of cytoskeletal components underlying subtype-specific dendritic arbor diversity. To investigate this core hypothesis, we propose the following tightly linked aims. First, we will elucidate the mechanistic role(s) of the PP2A phosphatase and TRiC/CCT chaperonin complex in directing subtype specific dendritic arborization. Second, we will identify the functional requirements and putative molecular targets of PP2A and TRiC/CCT in regulating subtype specific dendritic cytoskeletal architecture and dynamics. Third, we will conduct computational studies of dendritic morphology and spatio-temporal cytoskeletal distributions that directly integrates and synergizes with the first two aims thereby generating a closed-loop investigational system. These studies will not only reveal novel molecular mechanisms driving cytoskeletal organization and dynamics that functionally contribute to the emergence of diverse dendritic arbors, but also develop and disseminate neuroinformatic tools and data of broad impact to the neuroscience community.