The International Neuroinformatics Coordinating Facility: evaluating the first years

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation--translating across data sources--so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information is being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.

MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB

Experiments to monitor human brain activity during active behavior record a variety of modalities (e.g., EEG, eye tracking, motion capture, respiration monitoring) and capture a complex environmental context leading to large, event-rich time series datasets. The considerable variability of responses within and among subjects in more realistic behavioral scenarios requires experiments to assess many more subjects over longer periods of time. This explosion of data requires better computational infrastructure to more systematically explore and process these collections. MOBBED is a lightweight, easy-to-use, extensible toolkit that allows users to incorporate a computational database into their normal MATLAB workflow. Although capable of storing quite general types of annotated data, MOBBED is particularly oriented to multichannel time series such as EEG that have event streams overlaid with sensor data. MOBBED directly supports access to individual events, data frames, and time-stamped feature vectors, allowing users to ask questions such as what types of events or features co-occur under various experimental conditions. A database provides several advantages not available to users who process one dataset at a time from the local file system. In addition to archiving primary data in a central place to save space and avoid inconsistencies, such a database allows users to manage, search, and retrieve events across multiple datasets without reading the entire dataset. The database also provides infrastructure for handling more complex event patterns that include environmental and contextual conditions. The database can also be used as a cache for expensive intermediate results that are reused in such activities as cross-validation of machine learning algorithms. MOBBED is implemented over PostgreSQL, a widely used open source database, and is freely available under the GNU general public license at http://visual.cs.utsa.edu/mobbed. Source and issue reports for MOBBED are maintained at http://vislab.github.com/MobbedMatlab/

The layer-oriented approach to declarative languages for biological modeling

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

The NeuroDevNet Neuroinformatics Core

The field of neuroinformatics has expanded dramatically during the past decade building on the development of new technologies in brain research as well as in computing. The activities are diverse, from data management and standardization that has become essential due to the large amount of data generated and the needs to share it, to the development of sophisticated software necessary for the analyses and visualization of such data. NeuroDevNet is a Canadian initiative, funded by the Networks of Centres of Excellence, devoted to the study of brain development with the goal to translate this knowledge into improved diagnosis, prevention and treatment of neurodevelopmental disorders. The NeuroDevNet Neuroinformatics Core is dedicated to helping researchers across the network with their data management, standardization and sharing, as well as to implement innovative solutions to facilitate their research. It is an essential component to NeuroDevNet, enabling active collaboration across the country and optimizing this unique endeavor.

Technical and organizational considerations for the long-term maintenance and development of digital brain atlases and web-based databases

Digital brain atlas is a kind of image database that specifically provide information about neurons and glial cells in the brain. It has various advantages that are unmatched by conventional paper-based atlases. Such advantages, however, may become disadvantages if appropriate cares are not taken. Because digital atlases can provide unlimited amount of data, they should be designed to minimize redundancy and keep consistency of the records that may be added incrementally by different staffs. The fact that digital atlases can easily be revised necessitates a system to assure that users can access previous versions that might have been cited in papers at a particular period. To inherit our knowledge to our descendants, such databases should be maintained for a very long period, well over 100 years, like printed books and papers. Technical and organizational measures to enable long-term archive should be considered seriously. Compared to the initial development of the database, subsequent efforts to increase the quality and quantity of its contents are not regarded highly, because such tasks do not materialize in the form of publications. This fact strongly discourages continuous expansion of, and external contributions to, the digital atlases after its initial launch. To solve these problems, the role of the biocurators is vital. Appreciation of the scientific achievements of the people who do not write papers, and establishment of the secure academic career path for them, are indispensable for recruiting talents for this very important job.