Nian Liu

Gene expression patterns of olfactory receptors (ORs) are an important component of the signal encoding mechanism in the olfactory system since they determine the interactions between odorant ligands and sensory neurons. We have developed the Olfactory Receptor Microarray Database (ORMD) to house OR gene expression data. ORMD is integrated with the Olfactory Receptor Database (ORDB), which is a key repository of OR gene information. Both databases aim to aid experimental research related to olfaction.

Brain odor maps are reconstructed flat images that describe the spatial activity patterns in the glomerular layer of the olfactory bulbs in animals exposed to different odor stimuli. We have developed a software application, OdorMapComparer, to carry out quantitative analyses and comparisons of the fMRI odor maps. This application is an open-source window program that first loads two odor map images being compared. It allows image transformations including scaling, flipping, rotating, and warping so that the two images can be appropriately aligned to each other. It performs simple subtraction, addition, and average of signals in the two images. It also provides comparative statistics including the normalized correlation (NC) and spatial correlation coefficient. Experimental studies showed that the rodent fMRI odor maps for aliphatic aldehydes displayed spatial activity patterns that are similar in gross outlines but somewhat different in specific subregions. Analyses with OdorMapComparer indicate that the similarity between odor maps decreases with increasing difference in the length of carbon chains. For example, the map of butanal is more closely related to that of pentanal (with a NC = 0.617) than to that of octanal (NC = 0.082), which is consistent with animal behavioral studies. The study also indicates that fMRI odor maps are statistically odor-specific and repeatable across both the intra- and intersubject trials. OdorMapComparer thus provides a tool for quantitative, statistical analyses and comparisons of fMRI odor maps in a fashion that is integrated with the overall odor mapping techniques.

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.

The present study described an open source application, ResourceLog, that allows website administrators to record and analyze the usage of online resources. The application includes four components: logging, data mining, administrative interface, and back-end database. The logging component is embedded in the host website. It extracts and streamlines information about the Web visitors, the scripts, and dynamic parameters from each page request. The data mining component runs as a set of scheduled tasks that identify visitors of interest, such as those who have heavily used the resources. The identified visitors will be automatically subjected to a voluntary user survey. The usage of the website content can be monitored through the administrative interface and subjected to statistical analyses. As a pilot project, ResourceLog has been implemented in SenseLab, a Web-based neuroscience database system. ResourceLog provides a robust and useful tool to aid system evaluation of a resource-driven Web application, with a focus on determining the effectiveness of data sharing in the field and with the general public.

The present study applies informatics tools to aid and extend fMRI analysis of the coding mechanism of neural signals in the rodent olfactory system. Odor stimulation evokes unique spatial patterns of activity in the glomerular layer of the mammalian olfactory bulb (OB). An open-source software program, OdorMap-Builder, has been developed to process the high resolution anatomical and functional MRI images of the OB and to generate single two-dimensional flat maps, called odor maps, that describe the spatial activity patterns in the entire glomerular layer. Odor maps help identify the spatial activity patterns from the tremendous amount of fMRI data and they serve as ideal representation of space coding for the olfactory signals in the OB in response to a given odor stimulation. Based on the fMRI technology, OdorMapBuilder provides comparable odor maps on the intra-subject basis, a significant step towards the detailed analyses of the effects of odor types and/or concentrations. In addition, a new database, OdorMapDB, is developed to provide a repository for the generated odor maps. Web interfaces to the database are provided for the data entry, modification and retrieval. OdorMapDB is based on the EAV/CR (entity-attribute-value with classes and relationships) architecture and it is integrated with two other SenseLab olfactory databases: the olfactory receptor and odor databases. Both OdorMapBuilder and OdorMapDB should serve as useful tools and resources for the field and help facilitate experimental research in understanding the olfactory system and the mechanism for smell perception.