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Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an ever increasing rate. The need to store and exchange data in meaningful ways in support of data analysis, hypothesis testing and future collaborative use is pervasive. Because trans-disciplinary projects rely on effective use of data from many domains, there is a genuine interest in informatics community on how best to store and combine this data while maintaining a high level of data quality and documentation. The difficulties in sharing and combining raw data become amplified after post-processing and/or data analysis in which the new dataset of interest is a function of the original data and may have been collected by multiple collaborating sites. Simple meta-data, documenting which subject and version of data were used for a particular analysis, becomes complicated by the heterogeneity of the collecting sites yet is critically important to the interpretation and reuse of derived results. This manuscript will present a case study of using the XML-Based Clinical Experiment Data Exchange (XCEDE) schema and the Human Imaging Database (HID) in the Biomedical Informatics Research Network's (BIRN) distributed environment to document and exchange derived data. The discussion includes an overview of the data structures used in both the XML and the database representations, insight into the design considerations, and the extensibility of the design to support additional analysis streams.

We have discovered two genes, RSRC1 and ARHGAP18, associated with schizophrenia and in an independent study provided additional support for this association. We have both discovered and verified the association of two genes, RSRC1 and ARHGAP18, with schizophrenia. We combined a genome-wide screening strategy with neuroimaging measures as the quantitative phenotype and identified the single nucleotide polymorphisms (SNPs) related to these genes as consistently associated with the phenotypic variation. To control for the risk of false positives, the empirical P-value for association significance was calculated using permutation testing. The quantitative phenotype was Blood-Oxygen-Level Dependent (BOLD) Contrast activation in the left dorsal lateral prefrontal cortex measured during a working memory task. The differential distribution of SNPs associated with these two genes in cases and controls was then corroborated in a larger, independent sample of patients with schizophrenia (n=82) and healthy controls (n=91), thus suggesting a putative etiological function for both genes in schizophrenia. Up until now these genes have not been linked to any neuropsychiatric illness, although both genes have a function in prenatal brain development. We introduce the use of functional magnetic resonance imaging activation as a quantitative phenotype in conjunction with genome-wide association as a gene discovery tool.

Introduction: Genes play a well-documented role in determining normal cognitive function. This paper focuses on reviewing strategies for the identification of common genetic variation in genes that modulate normal and abnormal cognition with a genome-wide association scan (GWAS). GWASs make it possible to survey the entire genome to discover important but unanticipated genetic influences., Methods: The use of a quantitative phenotype in combination with a GWAS provides many advantages over a case-control design, both in power and in physiological understanding of the underlying cognitive processes. We review the major features of this approach, and show how, using a General Linear Model method, the contribution of each Single Nucleotide Polymorphism (SNP) to the phenotype is determined, and adjustments then made for multiple tests. An example of the strategy is presented, in which fMRI measures of cortical inefficiency while performing a working memory task are used as the quantitative phenotype. We estimate power under different effect sizes (10-30%) and variations in allelic frequency for a Quantitative Trait (QT) (10-20%), and compare them to a case-control design with an Odds Ratio (OR) of 1.5, showing how a QT approach is superior to a traditional case-control. In the presented example, this method identifies putative susceptibility genes for schizophrenia which affect prefrontal efficiency and have functions related to cell migration, forebrain development and stress response., Conclusion: The use of QT as phenotypes provide increased statistical power over categorical association approaches and when combined with a GWAS creates a strategy for identification of unanticipated genes that modulate cognitive processes and cognitive disorders.

Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an alarming rate. The specialization of fields in biology and medicine demonstrates the need for somewhat different structures for storage and retrieval of data. For biologists, the need for structured information and integration across a number of domains drives development. For clinical researchers and hospitals, the need for a structured medical record accessible to, ideally, any medical practitioner who might require it during the course of research or patient treatment, patient confidentiality, and security are the driving developmental factors. Scientific data management systems generally consist of a few core services: a backend database system, a front-end graphical user interface, and an export/import mechanism or data interchange format to both get data into and out of the database and share data with collaborators. The chapter introduces some existing databases, distributed file systems, and interchange languages used within the biomedical research and clinical communities for scientific data management and exchange.

The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular to that group. Moreover, the types of data and metadata collected are quite diverse, including image, physiological, and behavioral data, as well as descriptions of experimental design, and preprocessing and analysis methods. Each of these types of data utilizes a variety of software tools for collection, storage, and processing. Furthermore sites are reluctant to release control over the distribution and access to the data and the tools. To address these needs, the Biomedical Informatics Research Network (BIRN) has developed a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The infrastructure consists of distributed data collections hosted on dedicated storage and computational resources located at each participating site, a federated data management system and data integration environment, an Extensible Markup Language (XML) schema for data exchange, and analysis pipelines, designed to leverage both the distributed data management environment and the available grid computing resources.

We studied the effects of total sleep deprivation and recovery sleep in normal subjects using position emission tomography with 18F-deoxyglycose. Sleep deprivation resulted in a significant decrease in relative metabolism of the frontal cortex, thalamus, and striatum. Recovery sleep was found to have only a partial restorative effect on frontal lobe function with minimal reversal of subcortical deficits. Sleep may be especially important for maintenance of frontal lobe activity.

With the increased frequency of multisite, large-scale collaborative neuro-imaging studies, the need for a general, self-documenting framework for the storage and retrieval of activation maps and anatomical labels becomes evident. To address this need, we have developed and extensible markup language (XML) schema and associated tools for the storage of neuro-imaging activation maps and anatomical labels. This schema, as part of the XML-based Clinical Experiment Data Exchange (XCEDE) schema, provides storage capabilities for analysis annotations, activation threshold parameters, and cluster and voxel-level statistics. Activation parameters contain information describing the threshold, degrees of freedom, FWHM smoothness, search volumes, voxel sizes, expected voxels per cluster, and expected number of clusters in the statistical map. Cluster and voxel statistics can be stored along with the coordinates, threshold, and anatomical label information. Multiple threshold types can be documented for a given cluster or voxel along with the uncorrected and corrected probability values. Multiple atlases can be used to generate anatomical labels and stored for each significant voxel or cluter. Additionally, a toolbox for Statistical Parametric Mapping software (http://www. fil. ion.ucl.ac.uk/spm/) was created to capture the results from activation maps using the XML schema that supports both SPM99 and SPM2 versions (http://nbirn.net/Resources/Users/ Applications/xcede/SPM_XMLTools.htm). Support for anatomical labeling is available via the Talairach Daemon (http://ric.uthscsa. edu/projects/talairachdaemon.html) and Automated Anatomical Labeling (http://www. cyceron.fr/freeware/).

Biological factors responsible for nicotine initiation and dependence are largely unknown. Men and women smoke differently, and may smoke for different reasons. Brain metabolic response to nicotine may explain gender differences in nicotine use. We used FDG-PET to measure brain metabolic response on placebo and following nicotine administered by patch in 42 females and 77 males (smokers and non-smokers) while performing a Continuous Performance Task (CPT) or the Bushman Competition and Retaliation Task (CRT). Nicotine administration affected brain metabolism much differently in males and females, and these differences were dependent on task and smoking history. In the placebo condition female smokers performing the CPT and female non-smokers performing the CRT consistently had higher brain metabolism than males, especially in the entire prefrontal system and the mid and anterior temporal lobe, language cortices, and related subcortical systems. The overall effect of nicotine was to decrease these gender differences in brain metabolism.

The brain mechanisms underlying the cause of nicotine dependence are unknown, however, hostility traits are associated with increased susceptibility to nicotine dependence. We used FDG PET to measure brain metabolic response to nicotine administered by patch while the subject performed the Bushman aggression task in 86 high- and low-hostility subjects. Low-hostility trait subjects demonstrated no significant change in brain metabolic response to nicotine. In marked contrast, high-hostility non-smokers subjects demonstrated dramatic metabolic changes to low dose (3.5 mg patch) as did high-hostility smokers to high dose nicotine (21 mg patch) throughout the brain bilaterally (p<0.025). Correlational analyses demonstrated greater metabolic changes in response to nicotine in subjects with greatest hostility trait measures. The observed differences were not a consequence of plasma nicotine or cotinine levels. These metabolic changes were not observed when subjects performed a sustained attentional task (continuous performance task; CPT). Behaviorally, high-hostility subjects had higher ratings of anger, impatience, irritability and nervousness, and lower ratings of happiness, relaxation and curiosity than low-hostility subjects. Smokers had significantly greater scores on impatience and restlessness than non-smokers. This PET study demonstrates a conspicuous lack of the brain metabolic response to nicotine in low-hostility non-smokers in contrast to a dramatic brain response to nicotine in high hostility subjects. This biological difference in brain metabolic response to nicotine between high and low hostility trait subjects may explain differences in susceptibility to nicotine dependence.