http://www.springerprotocols.com/cdp/access/showWebFeedListing This feed provides the latest 25 protocols in the given category. http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_1 Processing text from scientific literature has become a necessity due to the burgeoning amounts of information that are fast becoming available, stemming from advances in electronic information technology. We created a program, NeuroText ( http://senselab.med.yale.edu/textmine/neurotext.pl ), designed specifically to extract information relevant to neuroscience-specific databases, NeuronDB and CellPropDB ( http://senselab.med.yale.edu/senselab/ ), housed at the Yale University School of Medicine. NeuroText extracts relevant information from the Neuroscience literature in a two-step process: each step parses text at different levels of granularity. NeuroText uses an expert-mediated knowledgebase and combines the techniques of indexing, contextual parsing, semantic and lexical parsing, and supervised and non-supervised learning to extract information. The constrains, metadata elements, and rules for information extraction are stored in the knowledgebase. NeuroText was created as a pilot project to process 3 years of publications in Journal of Neuroscience and was subsequently tested for 40,000 PubMed abstracts. We also present here a template to create domain non-specific knowledgebase that when linked to a text-processing tool like NeuroText can be used to extract knowledge in other fields of research. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_10 The electrical activity of individual neurons and neuronal networks is shaped by the complex interplay of a large number of non-linear processes, including the voltage-dependent gating of ion channels and the activation of synaptic receptors. These complex dynamics make it difficult to understand how individual neuron or network parameters—such as the number of ion channels of a given type in a neuron’s membrane or the strength of a particular synapse—influence neural system function. Systematic exploration of cellular or network model parameter spaces by computational brute force can overcome this difficulty and generate comprehensive data sets that contain information about neuron or network behavior for many different combinations of parameters. Searching such data sets for parameter combinations that produce functional neuron or network output provides insights into how narrowly different neural system parameters have to be tuned to produce a desired behavior. This chapter describes the construction and analysis of databases of neuron or neuronal network models and describes some of the advantages and downsides of such exploration methods. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_11 Quantifying the effect of a genetic manipulation or disease is a complicated process in a population of animals. Probabilistic brain atlases can capture population variability and be used to quantify those variations in anatomy as measured by structural imaging. Minimum deformation atlases (MDAs), a subclass of probabilistic atlases, are intensity-based averages of a collection of scans in a common space unbiased by selection of a single target image. Here, we describe a method for generating an MDA from a set of magnetic resonance microscopy images. First, the images are segmented to remove any non-brain tissue and bias field corrected to remove field inhomogeneities. The corrected images are then linearly aligned to a representative scan, the geometric mean of all the transformations is calculated, and a minimum deformation target (MDT) is produced by averaging the volumes in this new space. The brains are then non-linearly aligned to the MDT to produce the MDA. Finally, the images are linearly aligned to the MDA using a full-affine transformation to spatially and intensity normalize them, removing global differences in size, shape, and position but retaining anatomically significant differences. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_12 This chapter describes the use of high-resolution functional magnetic resonance imaging (fMRI) technology in brain odor mapping and a suite of informatics tools for building, databasing, and analyzing fMRI odor maps. OdorMapBuilder is a software program that extracts the olfactory signals that occurred in a particular layer of the olfactory bulb (OB), that is, the glomerular layer, from the 3D imaging data and generates 2D flat odor maps. Odor maps describe the odor-induced spatial activity patterns in the entire glomerular layer in the OB. OdorMapDB is a Web-based database system that serves as a centralized repository for the fMRI odor maps. OdorMapComparer is a software program that allows users to visually evaluate and statistically determine the similarity or difference between two fMRI odor maps being compared. Taken together, the fMRI technique and the related informatics tools play an important role in the study of the signal-encoding mechanisms in the olfactory system, which is essential to our understanding of the perception of smell. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_13 Neuroanatomical informatics, a subspecialty of neuroinformatics, focuses on technological solutions to neuroimage database access. Its current main goal is an image-based query system that is able to retrieve imagery based on anatomical location. Here, we describe a set of tools that collectively form such a solution for sectional material and that are available to investigators to use on their own data sets. The system accepts slide images as input and yields a matrix of transformation parameters that map each point on the input image to a standardized 3D brain atlas. In essence, this spatial normalization makes the atlas a spatial indexer from which queries can be issued simply by specifying a location on the reference atlas. Our objective here is to familiarize potential users of the system with the steps required of them as well as steps that take place behind the scene. We detail the capabilities and the limitations of the current implementation and briefly describe the enhancements planned for the near future. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_14 Analysis of functional and structural magnetic resonance imaging (MRI) brain images requires a complex sequence of data processing steps to proceed from raw image data to the final statistical tests. Neuroimaging researchers have begun to apply workflow-based computing techniques to automate data analysis tasks. This chapter discusses eight major components of workflow management systems (WFMSs): the workflow description language, editor, task modules, data access, verification, client, engine, and provenance, and their implementation in the Fiswidgets neuroimaging workflow system. Neuroinformatics challenges involved in applying workflow techniques in the domain of neuroimaging are discussed. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_15 Receptor distributions in the brain are studied by autoradiographic mapping in brain slices, which is a labor-intensive and expensive procedure. To keep track of the results of such studies, we have designed CoReDat, a multi-user relational database system that is available for download from www.cocomac.org/coredat. Here, we describe the data model and provide an architectural overview of CoReDat for the neuroscientist who wants to use this database, adapt it for related purposes, or build a new one. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_16 We outline the theory behind complex trait analysis and systems genetics and describe web-accessible resources including GeneNetwork (GN) that can be used for rapid exploratory analysis and hypothesis testing. GN, in particular, is a tightly integrated suite of bioinformatics tools and data sets, which supports the investigation of complex networks of gene variants, molecules, and cellular processes that modulate complex traits, including behavior and disease susceptibility. Using various statistical tools, users are able to analyze gene expression in various brain regions and tissues, map loci that modulate these traits, and explore genetic covariance among traits. Taken together, these tools enable the user to begin to assess complex interactions of gene networks, and facilitate analysis of traits using a systems approach. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_17 A critical goal of neuroscience is to fully understand neural processes and their relations to mental processes, and cognitive, affective, and behavioral disorders. Computational modeling, although still in its infancy, continues to play a central role in this endeavor. Presented here is a review of different aspects of computational modeling that help to explain many features of neuropsychological syndromes and psychiatric disease. Recent advances in computational modeling of epilepsy, cortical reorganization after lesions, Parkinson’s and Alzheimer diseases are also reviewed. Additionally, this chapter will also identify some trends in the computational modeling of brain functions. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_18 The search for DNA alterations that cause human disease has been an area of active research for more than 50 years, since the time that the genetic code was first solved. In the absence of data implicating chromosomal aberrations, researchers historically have performed whole genome linkage analysis or candidate gene association analysis to develop hypotheses about the genes that most likely cause a specific phenotype or disease. Whereas whole genome linkage analysis examines all chromosomal locations without a priori predictions regarding what genes underlie susceptibility, candidate gene association studies require a researcher to know in advance the genes that he or she wishes to test (based on their knowledge of a disease). To date, very few whole genome linkage studies and candidate gene studies have produced results that lead to generalizable findings about common diseases. One factor contributing to this lack of results has certainly been the previously limited resolution of the techniques. Recent technological advances, however, have made it possible to perform highly informative whole genome linkage and association analyses, as well as whole genome transcription (transcriptome) analysis. In addition, for the first time we can detect structural DNA aberrations throughout the genome on a fine scale. Each of these four approaches has its own strengths and weaknesses, but taken together, the results from an integrated analysis can implicate highly promising novel candidate genes. Here, we provide an overview of the integrated methodology that we have used to combine high-throughput genetic and functional genomic data with bioinformatics data that have produced new insights into the potential biological basis for schizophrenia. We believe that the potential of this combined approach is greater than that of a single mode of discovery, particularly for complex genetic diseases. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_19 The Alzheimer Research Forum Web site ( http://www.alzforum.org ) is an independent research project to develop an online community resource to manage scientific knowledge, information, and data about Alzheimer disease (AD). Its goals are to promote rapid communication, research efficiency, and collaborative, multidisciplinary interactions. Introducing new knowledge management approaches to AD research has a potentially large societal value. AD is among the leading causes of disability and death in older people. According to the Alzheimer’s Association, four million Americans currently suffer from AD. That number is expected to escalate to over 10 million in coming decades. Patients progress from memory loss to a bedridden state over many years and require near-constant care. In addition to imposing a heavy burden on family caregivers and society at large, AD and related neurodegenerative disorders are among the most complex and challenging in biomedicine. Researchers have produced an abundance of data implicating diverse biological mechanisms. Important factors include genes, environmental risks, changes in cell functions, DNA damage, accumulation of misfolded proteins, cell death, immune responses, changes related to aging, and reduced regenerative capacity. Yet there is no agreement on the fundamental causes of AD. The situations regarding Parkinson, Huntington, and amyotrophic lateral sclerosis (ALS) are similar. The challenge of integrating so much data into testable hypotheses and unified concepts is formidable. What is more, basic understanding of these diseases needs to intersect with an equally complex universe of pharmacology, medicinal chemistry, animal studies, and clinical trials. In this chapter, we will describe the approaches developed by Alzforum to achieve knowledge integration through information technology and virtual community-building. We will also propose some future directions in the application of Web-based knowledge management systems in neuromedicine. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_2 Data interoperability between well-defined domains is currently performed by leveraging Web services. In the biosciences, more specifically in neuroscience, robust data interoperability is more difficult to achieve due to data heterogeneity, continuous domain changes, and the constant creation of new semantic data models (Nadkarni et al., J Am Med Inform Assoc 6, 478–93, 1999; Miller et al., J Am Med Inform Assoc 8, 34–48, 2001; Gardner et al., J Am Med Inform Assoc 8, 17–33, 2001). Data heterogeneity in neurosciences is primarily due to its multidisciplinary nature. This results in a compelling need to integrate all available neuroscience information to improve our understanding of the brain. Researchers associated with neuroscience initiatives such as the human brain project (HBP) (Koslow and Huerta, Neuroinformatics: An Overview of the Human Brain Project, 1997), the Bioinformatics Research Network (BIRN), and the Neuroinformatics Information Framework (NIF) are exploring mechanisms to allow robust interoperability between these continuously evolving neuroscience databases. To accomplish this goal, it is crucial to orchestrate technologies such as database mediators, metadata repositories, semantic metadata annotations, and ontological services. This chapter introduces the importance of database interoperability in neurosciences. We also describe current data sharing and integration mechanisms in genera. We conclude with data integration in bioscience and present approaches on neuroscience data sharing. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_3 To determine effective database architecture for a specific neuroscience application, one must consider the distinguishing features of research databases and the requirements that the particular application must meet. Research databases manage diverse types of data, and their schemas evolve fairly steadily as domain knowledge advances. Database search and controlled-vocabulary access across the breadth of the data must be supported. We provide examples of design principles employed by our group as well as others that have proven successful and also introduce the appropriate use of entity–attribute–value (EAV) modeling. Most important, a robust architecture requires a significant metadata component, which serves to describe the individual types of data in terms of function and purpose. Recording validation constraints on individual items, as well as information on how they are to be presented, facilitates automatic or semi-automatic generation of robust user interfaces. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_4 EXtensible Markup Language (XML) technology provides an ideal representation for the complex structure of models and neuroscience data, as it is an open file format and provides a language-independent method for storing arbitrarily complex structured information. XML is composed of text and tags that explicitly describe the structure and semantics of the content of the document. In this chapter, we describe some of the common uses of XML in neuroscience, with case studies in representing neuroscience data and defining model descriptions based on examples from NeuroML. The specific methods that we discuss include (1) reading and writing XML from applications, (2) exporting XML from databases, (3) using XML standards to represent neuronal morphology data, (4) using XML to represent experimental metadata, and (5) creating new XML specifications for models. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_5 The insufficiency of terminological standards in neuroscience is increasingly recognized as a serious obstacle to interoperability. Adoption of a controlled vocabulary is a successful solution for small numbers of groups that work closely together but is impractical for large numbers of groups who represent diverse areas of research, index information by various legitimate nomenclatures, or publish in different languages. Interoperability among such disparate databases requires a translation mechanism, or “mediator,” to enable communication and data sharing among databases. Shared ontologies are essential components of a mediator. An ontology codifies the relations between terms of multiple nomenclatures and the concepts they represent. Neuroanatomy is central to neuroscience, and neuroanatomical terminology represents a core portion of the vocabulary of neuroscience. We have created in NeuroNames an ontology of 2500 neuroanatomical concepts referenced by 15,000 terms in seven languages. NeuroNames is the mediator for BrainInfo, a portal to neuroanatomy on the Web. We hope that a description of our experience in establishing interoperability between BrainInfo and other neuroscience Web sites may be useful to others engaged in the development of ontologies for neuroscience. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_6 One of the more important recent additions to the NEURON simulation environment is a tool called ModelView, which simplifies the task of understanding exactly what biological attributes are represented in a computational model. Here, we illustrate how ModelView contributes to the understanding of models and discuss its utility as a neuroinformatics tool for analyzing models in online databases and as a means for facilitating interoperability among simulators in computational neuroscience. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_7 The GEneral NEural SImulation System (GENESIS) is an open source simulation platform for realistic modeling of systems ranging from subcellular components and biochemical reactions to detailed models of single neurons, simulations of large networks of realistic neurons, and systems-level models. The graphical interface XODUS permits the construction of a wide variety of interfaces for the control and visualization of simulations. The object-oriented scripting language allows one to easily construct and modify simulations built from the GENESIS libraries of simulation components. Here, we present procedures for installing GENESIS and its supplementary tutorials, running GENESIS simulations, and creating new neural simulations. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_8 A key challenge for neuroinformatics is to devise methods for representing, accessing, and integrating vast amounts of diverse and complex data. A useful approach to represent and integrate complex data sets is to develop mathematical models [Arbib (The Handbook of Brain Theory and Neural Networks, pp. 741–745, 2003); Arbib and Grethe (Computing the Brain: A Guide to Neuroinformatics, 2001); Ascoli (Computational Neuroanatomy: Principles and Methods, 2002); Bower and Bolouri (Computational Modeling of Genetic and Biochemical Networks, 2001); Hines et al. (J. Comput. Neurosci. 17, 7–11, 2004); Shepherd et al. (Trends Neurosci. 21, 460–468, 1998); Sivakumaran et al. (Bioinformatics 19, 408–415, 2003); Smolen et al. (Neuron 26, 567–580, 2000); Vadigepalli et al. (OMICS 7, 235–252, 2003)]. Models of neural systems provide quantitative and modifiable frameworks for representing data and analyzing neural function. These models can be developed and solved using neurosimulators. One such neurosimulator is simulator for neural networks and action potentials (SNNAP) [Ziv (J. Neurophysiol. 71, 294–308, 1994)]. SNNAP is a versatile and user-friendly tool for developing and simulating models of neurons and neural networks. SNNAP simulates many features of neuronal function, including ionic currents and their modulation by intracellular ions and/or second messengers, and synaptic transmission and synaptic plasticity. SNNAP is written in Java and runs on most computers. Moreover, SNNAP provides a graphical user interface (GUI) and does not require programming skills. This chapter describes several capabilities of SNNAP and illustrates methods for simulating neurons and neural networks. SNNAP is available at http://snnap.uth.tmc.edu . 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-520-6_9 Data integration is particularly difficult in neuroscience; we must organize vast amounts of data around only a few fragmentary functional hypotheses. It has often been noted that computer simulation, by providing explicit hypotheses for a particular system and bridging across different levels of organization, can provide an organizational focus, which can be leveraged to form substantive hypotheses. Simulations lend meaning to data and can be updated and adapted as further data come in. The use of simulation in this context suggests the need for simulator adjuncts to manage and evaluate data. We have developed a neural query system (NQS) within the NEURON simulator, providing a relational database system, a query function, and basic data-mining tools. NQS is used within the simulation context to manage, verify, and evaluate model parameterizations. More importantly, it is used for data mining of simulation data and comparison with neurophysiology. 2007-11-29T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-099-1_1 2007-11-13T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-099-1_10 The need for a simple method of examining the early postnatal development of mouse models of human neurodevelopmental disorders has become evident. The following method for evaluating the developmental milestones of newborn mice allows for fast throughput of large numbers of mice in a battery of tests that examines weight gain and the reflexes and coordinated movements that are expressed at differing periods throughout the first 21 days of life. Sophisticated equipment is not required, and the measures focus on the day of first appearance of a developmental sign, reflex, or coordinated movement. 2007-11-13T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-099-1_2 Activity-dependent neuroprotective protein (ADNP, human calculated molecular mass 123,562.8 Da) is a newly discovered glial protein that it is essential for embryonic development and brain formation. ADNP includes an active neuroprotective site, an 8 amino acid peptide NAPSVIPQ (NAP). The current study was set out to prepare antibodies to ADNP that will recognize different sites on the molecule. Four peptides of 8–20 amino acids that span the ADNP molecule, including NAPVSIPQ, were prepared. Peptides (containing a Cys residue attached to the N-terminal amino acid) were conjugated to keyhole limpet hemocyanin (KLH) and injected to respective rabbits in the presence of Freund’s complete adjuvant. Following five booster injections in incomplete Freund’s adjuvant, the respective antisera were collected and assayed by enzyme-linked immunosorbent assay (ELISA) and purified by affinity chromatography on peptides conjugated to SulfoLink Coupling Gel (Pierce). Mouse brain proteins were prepared (4 months old) and separated into cytoplasmic and nuclear fractions. Proteins were further separated by SDS-PAGE (SDS-PolyAcrylamide Gel Electrophoresis) and transferred to nitrocellulose membranes. Membranes were then probed with the antibodies followed by a secondary antibody horseradish peroxidase conjugated anti-rabbit IgG prepared in goat and developed with ECL. All antibodies recognized intact ADNP in both cytoplasmic and nuclear fractions. This work developed antibodies against ADNP and NAP that will be utilized for further experimentations to elucidate the distribution and mechanisms of ADNP and NAP neuroprotection. 2007-11-13T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-099-1_3 2007-11-13T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-099-1_4 2007-11-13T05:00:00Z http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-60327-099-1_5 2007-11-13T05:00:00Z