PLD3 affects axonal spheroids and network defects in Alzheimer's disease.

The precise mechanisms that lead to cognitive decline in Alzheimer's disease are unknown. Here we identify amyloid-plaque-associated axonal spheroids as prominent contributors to neural network dysfunction. Using intravital calcium and voltage imaging, we show that a mouse model of Alzheimer's disease demonstrates severe disruption in long-range axonal connectivity. This disruption is caused by action-potential conduction blockades due to enlarging spheroids acting as electric current sinks in a size-dependent manner. Spheroid growth was associated with an age-dependent accumulation of large endolysosomal vesicles and was mechanistically linked with Pld3-a potential Alzheimer's-disease-associated risk gene1 that encodes a lysosomal protein2,3 that is highly enriched in axonal spheroids. Neuronal overexpression of Pld3 led to endolysosomal vesicle accumulation and spheroid enlargement, which worsened axonal conduction blockades. By contrast, Pld3 deletion reduced endolysosomal vesicle and spheroid size, leading to improved electrical conduction and neural network function. Thus, targeted modulation of endolysosomal biogenesis in neurons could potentially reverse axonal spheroid-induced neural circuit abnormalities in Alzheimer's disease, independent of amyloid removal.

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

Approaches to neuroscience data integration.

As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.

NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.

Automated Metadata Suggestion During Repository Submission.

Knowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these categories risks reducing completeness in the other because manually curating metadata is time consuming and is restricted by familiarity with both the data and the metadata annotation scheme. The diverse interests of a research community may drive a resource to have hundreds of metadata tags with few examples for each making it challenging for humans or machine learning algorithms to learn how to assign metadata tags properly. We demonstrate with ModelDB, a computational neuroscience model discovery resource, that using manually-curated regular-expression based rules can overcome this challenge by parsing existing texts from data providers during user data entry to suggest metadata annotations and prompt them to suggest other related metadata annotations rather than leaving the task to a curator. In the ModelDB implementation, analyzing the abstract identified 6.4 metadata tags per abstract at 79% precision. Using the full-text produced higher recall with low precision (41%), and the title alone produced few (1.3) metadata annotations per entry; we thus recommend data providers use their abstract during upload. Grouping the possible metadata annotations into categories (e.g. cell type, biological topic) revealed that precision and recall for the different text sources varies by category. Given this proof-of-concept, other bioinformatics resources can likewise improve the quality of their metadata by adopting our approach of prompting data uploaders with relevant metadata at the minimal cost of formalizing rules for each potential metadata annotation.

Respiration Gates Sensory Input Responses in the Mitral Cell Layer of the Olfactory Bulb.

Respiration plays an essential role in odor processing. Even in the absence of odors, oscillating excitatory and inhibitory activity in the olfactory bulb synchronizes with respiration, commonly resulting in a burst of action potentials in mammalian mitral/tufted cells (MTCs) during the transition from inhalation to exhalation. This excitation is followed by inhibition that quiets MTC activity in both the glomerular and granule cell layers. Odor processing is hypothesized to be modulated by and may even rely on respiration-mediated activity, yet exactly how respiration influences sensory processing by MTCs is still not well understood. By using optogenetics to stimulate discrete sensory inputs in vivo, it was possible to temporally vary the stimulus to occur at unique phases of each respiration. Single unit recordings obtained from the mitral cell layer were used to map spatiotemporal patterns of glomerular evoked responses that were unique to stimulations occurring during periods of inhalation or exhalation. Sensory evoked activity in MTCs was gated to periods outside phasic respiratory mediated firing, causing net shifts in MTC activity across the cycle. In contrast, odor evoked inhibitory responses appear to be permitted throughout the respiratory cycle. Computational models were used to further explore mechanisms of inhibition that can be activated by respiratory activity and influence MTC responses. In silico results indicate that both periglomerular and granule cell inhibition can be activated by respiration to internally gate sensory responses in the olfactory bulb. Both the respiration rate and strength of lateral connectivity influenced inhibitory mechanisms that gate sensory evoked responses.

ModelView for ModelDB: Online Presentation of Model Structure.

ModelDB ( modeldb.yale.edu ), a searchable repository of source code of more than 950 published computational neuroscience models, seeks to promote model reuse and reproducibility. Code sharing is a first step; however, model source code is often large and not easily understood. To aid users, we have developed ModelView, a web application for ModelDB that presents a graphical view of model structure augmented with contextual information for NEURON and NEURON-runnable (e.g. NeuroML, PyNN) models. Web presentation provides a rich, simulator-independent environment for interacting with graphs. The necessary data is generated by combining manual curation, text-mining the source code, querying ModelDB, and simulator introspection. Key features of the user interface along with the data analysis, storage, and visualization algorithms are explained. With this tool, researchers can examine and assess the structure of hundreds of models in ModelDB in a standardized presentation without installing any software, downloading the model, or reading model source code.

Semi-automated population of an online database of neuronal models (ModelDB) with citation information, using PubMed for validation.

Citations play an important role in medical and scientific databases by indicating the authoritative source of the data. Manual citation entry is tedious and prone to errors. We describe a method and make available computer scripts which automate the process of citation entry. We use an open citation project PERL module (PARSER) for parsing citation data that is then used to retrieve PubMed records to supply the (validated) reference. Our PERL scripts are available via a link in the web references section of this article.

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.

Abnormal Excitability of Oblique Dendrites Implicated in Early Alzheimer's: A Computational Study.

The integrative properties of cortical pyramidal dendrites are essential to the neural basis of cognitive function, but the impact of amyloid beta protein (abeta) on these properties in early Alzheimer's is poorly understood. In animal models, electrophysiological studies of proximal dendrites have shown that abeta induces hyperexcitability by blocking A-type K+ currents (I(A)), disrupting signal integration. The present study uses a computational approach to analyze the hyperexcitability induced in distal dendrites beyond the experimental recording sites. The results show that back-propagating action potentials in the dendrites induce hyperexcitability and excessive calcium concentrations not only in the main apical trunk of pyramidal cell dendrites, but also in their oblique dendrites. Evidence is provided that these thin branches are particularly sensitive to local reductions in I(A). The results suggest the hypothesis that the oblique branches may be most vulnerable to disruptions of I(A) by early exposure to abeta, and point the way to further experimental analysis of these actions as factors in the neural basis of the early decline of cognitive function in Alzheimer's.

ModelDB in computational neuroscience education - a research tool as interactive educational media.

ModelDB's mission is to link computational models and publications, supporting the field of computational neuroscience (CNS) by making model source code readily available. It is continually expanding, and currently contains source code for more than 300 models that cover more than 41 topics. Investigators, educators, and students can use it to obtain working models that reproduce published results and can be modified to test for new domains of applicability. Users can browse ModelDB to survey the field of computational neuroscience, or pursue more focused explorations of specific topics. Here we describe tutorials and initial experiences with ModelDB as an interactive educational tool.

Neuroinformatics: from bioinformatics to databasing the brain.

Neuroinformatics seeks to create and maintain web-accessible databases of experimental and computational data, together with innovative software tools, essential for understanding the nervous system in its normal function and in neurological disorders. Neuroinformatics includes traditional bioinformatics of gene and protein sequences in the brain; atlases of brain anatomy and localization of genes and proteins; imaging of brain cells; brain imaging by positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) and other methods; many electrophysiological recording methods; and clinical neurological data, among others. Building neuroinformatics databases and tools presents difficult challenges because they span a wide range of spatial scales and types of data stored and analyzed. Traditional bioinformatics, by comparison, focuses primarily on genomic and proteomic data (which of course also presents difficult challenges). Much of bioinformatics analysis focus on sequences (DNA, RNA, and protein molecules), as the type of data that are stored, compared, and sometimes modeled. Bioinformatics is undergoing explosive growth with the addition, for example, of databases that catalog interactions between proteins, of databases that track the evolution of genes, and of systems biology databases which contain models of all aspects of organisms. This commentary briefly reviews neuroinformatics with clarification of its relationship to traditional and modern bioinformatics.

SenseLab: new developments in disseminating neuroscience information.

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.

Creating knowledgebases to text-mine PUBMED articles using clustering techniques.

Knowledgebase-mediated text-mining approaches work best when processing the natural language of domain-specific text. To enhance the utility of our successfully tested program-NeuroText, and to extend its methodologies to other domains, we have designed clustering algorithms, which is the principal step in automatically creating a knowledgebase. Our algorithms are designed to improve the quality of clustering by parsing the test corpus to include semantic and syntactic parsing