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      Federated Web-accessible Clinical Data Management within an Extensible NeuroImaging Database

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          Abstract

          Managing vast datasets collected throughout multiple clinical imaging communities has become critical with the ever increasing and diverse nature of datasets. Development of data management infrastructure is further complicated by technical and experimental advances that drive modifications to existing protocols and acquisition of new types of research data to be incorporated into existing data management systems. In this paper, an extensible data management system for clinical neuroimaging studies is introduced: The Human Clinical Imaging Database (HID) and Toolkit. The database schema is constructed to support the storage of new data types without changes to the underlying schema. The complex infrastructure allows management of experiment data, such as image protocol and behavioral task parameters, as well as subject-specific data, including demographics, clinical assessments, and behavioral task performance metrics. Of significant interest, embedded clinical data entry and management tools enhance both consistency of data reporting and automatic entry of data into the database. The Clinical Assessment Layout Manager (CALM) allows users to create on-line data entry forms for use within and across sites, through which data is pulled into the underlying database via the generic clinical assessment management engine (GAME). Importantly, the system is designed to operate in a distributed environment, serving both human users and client applications in a service-oriented manner. Querying capabilities use a built-in multi-database parallel query builder/result combiner, allowing web-accessible queries within and across multiple federated databases. The system along with its documentation is open-source and available from the Neuroimaging Informatics Tools and Resource Clearinghouse (NITRC) site.

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          Most cited references16

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          Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study.

          The Functional Imaging Biomedical Informatics Network is a consortium developing methods for multisite functional imaging studies. Both prefrontal hyper- or hypoactivity in chronic schizophrenia have been found in previous studies of working memory. In this functional magnetic resonance imaging (fMRI) study of working memory, 128 subjects with chronic schizophrenia and 128 age- and gender-matched controls were recruited from 10 universities around the United States. Subjects performed the Sternberg Item Recognition Paradigm1,2 with memory loads of 1, 3, or 5 items. A region of interest analysis examined the mean BOLD signal change in an atlas-based demarcation of the dorsolateral prefrontal cortex (DLPFC), in both groups, during both the encoding and retrieval phases of the experiment over the various memory loads. Subjects with schizophrenia performed slightly but significantly worse than the healthy volunteers and showed a greater decrease in accuracy and increase in reaction time with increasing memory load. The mean BOLD signal in the DLPFC was significantly greater in the schizophrenic group than the healthy group, particularly in the intermediate load condition. A secondary analysis matched subjects for mean accuracy and found the same BOLD signal hyperresponse in schizophrenics. The increase in BOLD signal change from minimal to moderate memory loads was greater in the schizophrenic subjects than in controls. This effect remained when age, gender, run, hemisphere, and performance were considered, consistent with inefficient DLPFC function during working memory. These findings from a large multisite sample support the concept not of hyper- or hypofrontality in schizophrenia, but rather DLPFC inefficiency that may be manifested in either direction depending on task demands. This redirects the focus of research from direction of difference to neural mechanisms of inefficiency.
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            Test-retest and between-site reliability in a multicenter fMRI study.

            In the present report, estimates of test-retest and between-site reliability of fMRI assessments were produced in the context of a multicenter fMRI reliability study (FBIRN Phase 1, www.nbirn.net). Five subjects were scanned on 10 MRI scanners on two occasions. The fMRI task was a simple block design sensorimotor task. The impulse response functions to the stimulation block were derived using an FIR-deconvolution analysis with FMRISTAT. Six functionally-derived ROIs covering the visual, auditory and motor cortices, created from a prior analysis, were used. Two dependent variables were compared: percent signal change and contrast-to-noise-ratio. Reliability was assessed with intraclass correlation coefficients derived from a variance components analysis. Test-retest reliability was high, but initially, between-site reliability was low, indicating a strong contribution from site and site-by-subject variance. However, a number of factors that can markedly improve between-site reliability were uncovered, including increasing the size of the ROIs, adjusting for smoothness differences, and inclusion of additional runs. By employing multiple steps, between-site reliability for 3T scanners was increased by 123%. Dropping one site at a time and assessing reliability can be a useful method of assessing the sensitivity of the results to particular sites. These findings should provide guidance toothers on the best practices for future multicenter studies.
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              Reducing interscanner variability of activation in a multicenter fMRI study: controlling for signal-to-fluctuation-noise-ratio (SFNR) differences.

              Variation in scanner performance will lead to variation in activation patterns in multicenter fMRI studies. The purpose of this investigation was to evaluate the effect of statistically covarying for scanner differences in signal-to-fluctuation-noise-ratio (SFNR) on reducing scanner differences in activation effect size as part of a multicenter fMRI project (FIRST BIRN). For SFNR, "signal" is typically the mean intensity over time and "fluctuation noise" is the temporal standard deviation. Five subjects were sent to 9 centers (10 scanners) and scanned on two consecutive days using a sensorimotor fMRI protocol. High-field (4 T and 3 T) and low-field (1.5 T) scanners from three vendors (GE, Siemens and Picker) were included. The effect size for the detection of neural activation during a sensorimotor task was evaluated as the percent of temporal variance accounted for by our model (percent of variance accounted for, or PVAF). Marked scanner effects were noted for both PVAF as well as SFNR. After covariate adjustment with one of several measures of SFNR, there were dramatic reductions in scanner-to-scanner variations in activation effect size. Variance components analyses revealed 75%-81% reductions in variance due to scanner with this method. Thus, controlling for scanner variation in SFNR may be an effective method to homogenize activation effect sizes in multicenter studies.
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                Author and article information

                Contributors
                +1-858-2460606 , iozyurt@ucsd.edu
                Journal
                Neuroinformatics
                Neuroinformatics
                Humana Press Inc (New York )
                1539-2791
                1559-0089
                22 June 2010
                22 June 2010
                December 2010
                : 8
                : 4
                : 231-249
                Affiliations
                [1 ]Department of Psychiatry, University of California at San Diego, San Diego, CA USA
                [2 ]Brain Imaging Center, University of California at Irvine, Irvine, CA USA
                [3 ]Department of Radiology, University of California at San Diego, San Diego, CA USA
                [4 ]University of Iowa, Iowa City, IA USA
                [5 ]MIND Institute, Albuquerque, NM USA
                [6 ]University of California at San Diego, San Diego, CA USA
                Article
                9078
                10.1007/s12021-010-9078-6
                2974931
                20567938
                37083334-b3d9-4287-8e8c-06ba76a3d7eb
                © The Author(s) 2010
                History
                Categories
                Article
                Custom metadata
                © Springer Science+Business Media, LLC 2010

                Neurosciences
                data sharing,neuroinformatics,federated databases,open source,neuroimaging data management

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