Biomedical Informatics Research Network Research Articles

DNA Sequence Analysis for Brain Disorder Using Deep Learning and Secure Storage

Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the image using a Deoxyribonucleic Acid (DNA) Sequence-based Joint Photographic Experts Group (JPEG) Zig Zag Encryption Algorithm (DBNJZZ). In this work, DBNJZZ implements an efficient and effective prediction model for disorders using the open-access datasets of Alzheimer's Disease Neuroimaging Initiative (Adni), the Center for Biomedical Research Excellence (Cobre), the Open Access Series of Imaging Studies (Oasis), the Function Biomedical Informatics Research Network (Fbirn), a Parkinson's dataset of 55 patients and 23 subjects with Parkinson's syndromes (Ntua), and the Parkinson's Progression Markers Initiative (Ppmi). This algorithm is implemented and tested using performance metric measures of accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). DBNJZZ gives better performance with an accuracy of 99.21% and also surpasses previous methods on other measures.

A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI

Background and objectivesSchizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the distortions caused by schizophrenia in the brain activation. Hence, it can be useful for developing a decision model that performs computer-aided diagnosis of schizophrenia. But, fMRI data is huge in dimension. Therefore dimension reduction is indispensable. It is additionally required to identify the discriminative brain regions. Hence, we aim to build an effective decision model that incorporates suitable dimension reduction and also identifies discriminative brain regions. MethodsWe propose a three-phase dimension reduction. First phase involves spatially-constrained fuzzy clustering of 3-dimensional spatial maps (obtained from general linear model and independent component analysis). In the second phase, non-linear features are extracted from each cluster using a generalized discriminant analysis. In the third phase, a novel fuzzy rough feature selection is proposed. The features obtained after the third phase are used for learning a decision model by the help of support vector machine classifier. This complete method is implemented within leave-one-out cross-validation on two balanced datasets (respectively acquired on 1.5Tesla and 3Tesla scanners). Both these datasets are created using Function Biomedical Informatics Research Network multisite data and contain fMRI data acquired during auditory oddball task performed by age-matched schizophrenia patients and healthy subjects. A permutation test is also carried out to ensure that no bias is involved in the learning. ResultsThe results indicate that the proposed method achieves maximum classification accuracy of 97.1% and 98.0% for the two datasets respectively. The proposed method outperforms the state-of-the-art methods. The results of the permutation test show that p-values are lesser than the significance level i.e. 0.05. Therefore, the classifier has found a significant class structure and does not involve any bias. Further, discriminative brain regions are identified and are in agreement with the findings in related literature. ConclusionThe proposed method is able to derive suitable non-linear features and the related brain regions for effective computer-aided diagnosis. The fuzzy and rough set based approaches help in handling uncertainty and ambiguity in real data.

Predictive Imaging Approach May Identify Severity of Schizophrenia Symptoms

Back to table of contents Previous article Next article Clinical and Research NewsFull AccessPredictive Imaging Approach May Identify Severity of Schizophrenia SymptomsNick ZagorskiNick ZagorskiSearch for more papers by this authorPublished Online:14 Sep 2017https://doi.org/10.1176/appi.pn.2017.8b16AbstractNew analysis of MRI scans suggests that differences in brain connectivity can be used to predict how strongly certain symptoms including apathy and thought disorder will manifest.The ability to accurately predict at-risk individuals who will transition to psychosis from those who will not could help clinicians connect patients to care faster. In recent years, researchers have increasingly turned to neuroimaging technology in search of biomarkers for psychosis. iStock/baranozdemirA study by researchers at the University of Alberta in Edmonton and IBM’s TJ Watson Research Center in Yorktown, N.Y., now suggests that differences in brain connectivity may predict not only which patients have schizophrenia, but also the severity of specific symptoms.These findings, which were published in NPJ Schizophrenia, came from an analysis of functional MRI (fMRI) scans from 95 people, including 46 patients with schizophrenia or schizoaffective disorder and 49 matched controls.The participants underwent the fMRI scans while performing a task known as the Auditory Oddball. This test involves listening to a series of repetitive tones and pressing a button whenever a different-pitched, “oddball” tone is heard. This test was chosen because auditory problems are a common schizophrenia symptom.Using a computational approach known as “link-weighting,” the researchers compared the relative strength of brain activity in patients with schizophrenia with that of controls. Link-weighting involves measuring neural signals (or links) between different clusters in the brain (or nodes).On average, patients with schizophrenia had greater activity in several brain regions than controls, particularly in links connecting the thalamus and motor cortex. Based on the differences in link-weights, the researchers could differentiate patients with schizophrenia from controls with 74 percent accuracy. Other predictive studies using brain scans have reached higher accuracies, but study investigator Guillermo Cecchi, Ph.D., director of the computational psychiatry group at IBM Research, noted previous studies employed a single MRI machine for more consistency. The fMRI images used in this study came from the national Function Biomedical Informatics Research Network Data Repository (fBIRN), which collects scans from a wide range of machines employing different types of imaging software. According to Cecchi, this made comparing images much more challenging.“That we could reach 74 percent accuracy when working across different sites is actually encouraging,” he said. “There is room to improve, but this is a first step to developing a model for prediction that could really be universal.”Cecchi and his colleagues also found they could predict the severity of specific symptoms in the schizophrenia patients using the imaging data. They assessed connectivity patterns in relation to patient scores on nine global assessment scales and found five where link-weighting scores were tied to severity: alogia, apathy, attention, bizarre behavior, and formal thought disorder. In contrast, the severity of affective flattening, anhedonia, hallucinations, and delusions did not appear to be associated with these connectivity patterns.What was interesting, noted Cecchi, is that the neural links that predicted symptom severity were not the same ones that showed the strongest difference between the schizophrenia patients and controls. The areas associated with symptom differences included the sensory cortex and precuneus, a brain region involved in visuospatial processing and self-reflection. Cecchi thinks this speaks to the complexity of schizophrenia, as neural connections that do not appear predictive of a disordered state greatly affect how symptoms manifest.This study was supported by IBM, Alberta Innovates Health Solutions, and the Natural Sciences and Engineering Research Council of Canada. The fBIRN is supported by grants from the National Institutes of Health. ■“Learning Stable and Predictive Network-Based Patterns of Schizophrenia and Its Clinical Symptoms” can be accessed here. ISSUES NewArchived

MR Performance Comparison of a PET/MR System Before and After SiPM-Based Time-of-Flight PET Detector Insertion

A silicon photomultiplier (SiPM)-based time-of-flight capable PET detector has been integrated with a 70 cm wide-bore 3T MR scanner for simultaneous whole-body imaging (MR750w, GE Healthcare, Waukesha, WI). After insertion of the PET detector, the final PET/MR bore is 60 cm wide (SIGNA PET/MR, GE Healthcare, Waukesha, WI). The MR performance was compared before and after the PET ring insertion. ${\rm B}_0$ homogeneity, ${\rm B}_1^ + $ uniformity of the body coil along with peak ${\rm B}_1^ + $ , coherent noise, and FBIRN (Function Biomedical Informatics Research Network) tests are used to compare the MR performance. It is shown that ${\rm B}_0$ homogeneity and coherent noise have not changed according to the system specifications. Peak ${\rm B}_1^ + $ is increased by 33% and ${\rm B}_1^ + $ inhomogeneity is increased by 4% after PET ring insertion due to a smaller diameter body coil design. The FBIRN test shows similar temporal stability before and after PET ring insertion. Due to a smaller body coil on the PET/MR system, the signal fluctuation to noise ratio (SFNR) and SNR for body receive coil, are improved by ${\sim} 40\%$ and ${\sim} 160\%$ for Echo Planar Imaging (EPI) and spiral sequences respectively. Comparison using RF- and gradient-intensive clinical sequences shows inserting the PET detectors into the wide-bore MRI has not compromised the MR image quality according to these tests.

Impairment in subcortical suppression in schizophrenia: Evidence from the fBIRN Oddball Task

Schizophrenia patients show widespread impairments in brain activity during oddball tasks, which involve responding to infrequent target stimuli while refraining from responding during continuous non-target stimuli. In a network-based investigation comparing schizophrenia or schizoaffective patients to healthy controls, we sought to clarify which networks were specifically associated with target detection using a multivariate analysis technique that identifies task-specific functional brain networks. We acquired data from the publicly available function biomedical informatics research network collaboration, including 58 patients and 50 controls. Two task-based functional brain networks were identified: (1) a response modulation network including bilateral temporal pole, supramarginal gyrus, striatum, and thalamus, on which patients showed decreased activity relative to controls; and (2) an auditory-motor response activation network, on which patients showed a slower return to baseline than controls, but no difference in peak activation. For both groups, baseline to peak activation of the response modulation network correlated negatively with peak to baseline activity in the response activation network, suggesting a role in suppressing the motor response following targets. Patients' impaired activity in the response modulation network, and subsequent longer return to baseline in the response activation network, correspond with their later and less accurate behavioral performance, suggesting that impairment in suppression of the auditory-motor response activation network could underlie oddball task deficits in schizophrenia. In addition, the magnitude of the activity in the response modulation network was correlated with intensity of delusions of reference, supporting the notion that increased referential ideation is associated with hyperactivity within the subcortical striatal-limbic network. Hum Brain Mapp 37:4640-4653, 2016. © 2016 Wiley Periodicals, Inc.

Modality-Dependent Impact of Hallucinations on Low-Frequency Fluctuations in Schizophrenia.

Prior resting-state functional magnetic resonance imaging (fMRI) analyses have identified patterns of functional connectivity associated with hallucinations in schizophrenia (Sz). In this study, we performed an analysis of the mean amplitude of low-frequency fluctuations (ALFF) to compare resting state spontaneous low-frequency fluctuations in patients with Sz who report experiencing hallucinations impacting different sensory modalities. By exploring dynamics across 2 low-frequency passbands (slow-4 and slow-5), we assessed the impact of hallucination modality and frequency range on spatial ALFF variation. Drawing from a sample of Sz and healthy controls studied as part of the Functional Imaging Biomedical Informatics Research Network (FBIRN), we replicated prior findings showing that patients with Sz have decreased ALFF in the posterior brain in comparison to controls. Remarkably, we found that patients that endorsed visual hallucinations did not show this pattern of reduced ALFF in the back of the brain. These patients also had elevated ALFF in the left hippocampus in comparison to patients that endorsed auditory (but not visual) hallucinations. Moreover, left hippocampal ALFF across all the cases was related to reported hallucination severity in both the auditory and visual domains, and not overall positive symptoms. This supports the hypothesis that dynamic changes in the ALFF in the hippocampus underlie severity of hallucinations that impact different sensory modalities.

The Function Biomedical Informatics Research Network Data Repository

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.

Mapping pathological changes in brain structure by combining T1- and T2-weighted MR imaging data.

IntroductionA workflow based on the ratio between standardized T1-weighted (T1-w) and T2-weighted (T2-w) MR images has been proposed as a new tool to study brain structure. This approach was previously used to map structural properties in the healthy brain. Here, we evaluate whether the T1-w/T2-w approach can support the assessment of structural impairments in the diseased brain. We use schizophrenia data to demonstrate the potential clinical utility of the technique.MethodsWe analyzed T1-w and T2-w images of 36 schizophrenic patients and 35 age-matched controls. These were collected for the Function Biomedical Informatics Research Network (fBIRN) collaborative project, which had an IRB approval and followed the HIPAA guidelines. We computed T1-w/T2-w images for each individual and compared intensities in schizophrenic and control groups on a voxel-wise basis, as well as in regions of interest (ROIs).ResultsOur results revealed that the T1-w/T2-w image permits to discriminate brain regions showing group-level differences between patients and controls with greater accuracy than conventional T1-w and T2-w images. Both the ROIs and the voxel-wise analysis showed globally reduced gray and white matter values in patients compared to controls. Significantly reduced values were found in regions such as insula, primary auditory cortex, hippocampus, inferior longitudinal fasciculus, and inferior fronto-occipital fasciculus.ConclusionOur findings were consistent with previous meta-analyses in schizophrenia corroborating the hypothesis of a potential “disconnection” syndrome in conjunction with structural alterations in local gray matter regions. Overall, our study suggested that the T1-w/T2-w technique permits to reliably map structural differences between the brains of patients and healthy individuals.

The Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN) data repository

Arterial spin labeling (ASL) MRI provides an accurate and reliable measure of cerebral blood flow (CBF). A rapidly growing number of CBF measures are being collected both in clinical and research settings around the world, resulting in a large volume of data across a wide spectrum of study populations and health conditions. Here, we describe a central CBF data repository with integrated processing workflows, referred to as the Cerebral Blood Flow Biomedical Informatics Research Network (CBFBIRN). The CBFBIRN provides an integrated framework for the analysis and comparison of CBF measures across studies and sites. In this work, we introduce the main capabilities of the CBFBIRN (data storage, processing, and sharing), describe what types of data are available, explain how users can contribute to the data repository and access existing data from it, and discuss our long-term plans for the CBFBIRN.

Visual Hallucinations Are Associated With Hyperconnectivity Between the Amygdala and Visual Cortex in People With a Diagnosis of Schizophrenia

While auditory verbal hallucinations (AH) are a cardinal symptom of schizophrenia, people with a diagnosis of schizophrenia (SZ) may also experience visual hallucinations (VH). In a retrospective analysis of a large sample of SZ and healthy controls (HC) studied as part of the functional magnetic resonance imaging (fMRI) Biomedical Informatics Research Network (FBIRN), we asked if SZ who endorsed experiencing VH during clinical interviews had greater connectivity between visual cortex and limbic structures than SZ who did not endorse experiencing VH. We analyzed resting state fMRI data from 162 SZ and 178 age- and gender-matched HC. SZ were sorted into groups according to clinical ratings on AH and VH: SZ with VH (VH-SZ; n = 45), SZ with AH but no VH (AH-SZ; n = 50), and SZ with neither AH nor VH (NoH-SZ; n = 67). Our primary analysis was seed based, extracting connectivity between visual cortex and the amygdala (because of its role in fear and negative emotion) and visual cortex and the hippocampus (because of its role in memory). Compared with the other groups, VH-SZ showed hyperconnectivity between the amygdala and visual cortex, specifically BA18, with no differences in connectivity among the other groups. In a voxel-wise, whole brain analysis comparing VH-SZ with AH-SZ, the amygdala was hyperconnected to left temporal pole and inferior frontal gyrus in VH-SZ, likely due to their more severe thought broadcasting. VH-SZ have hyperconnectivity between subcortical areas subserving emotion and cortical areas subserving higher order visual processing, providing biological support for distressing VH in schizophrenia.

Modeling fMRI data: challenges and opportunities.

We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data--the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.

The XNAT Ecosystem

Event Abstract Back to Event The XNAT Ecosystem Daniel Marcus1*, Rick Herrick1, Timothy Olsen2, William Horton1 and Alan Sheu3 1 Washington University in St. Louis, United States 2 Deck5 Consulting, United States 3 Integrity, United States The XNAT imaging informatics platform is widely used to manage a variety of neuroimaging research, from internal research programs to large-scale data sharing initiatives. While XNAT has been open source since its inception and developers from around the world have contributed to its features, the number of contributions has been limited due to the scale and complexity of the XNAT code base. However, with the inclusion in recent releases of XNAT of an open application programming interface (API) based on the REST web services model, many more developers are developing tools that interact with XNAT through the API without needing to learn XNAT's internal code. Examples include pyXNAT, a python library for exchanging data with XNAT; Connectome Workbench, a visualization and analysis platform for structural and functional MRI data; and 3D Slicer, an advanced medical image visualization suite; and LONI Pipeline, a GUI-based application for executing and managing automated processing streams. A variety of additional tools are currently in development. The most recent version of XNAT includes a plugin architecture that enables developers to build new capabilities directly in XNAT with minimal interfacing in the core code. Modules can include visual components as well java code and extensions to the XNAT REST API. As an example, our lab has produced a module that enables users to pull XNAT metadata into iCal calendar feeds. These feeds can be used to assist study monitoring by integrating views of data in scheduling systems (e.g. Google calendar) by the date and time of acquisition in an integrated view with scheduling. To assist in dissemination of XNAT related software, we have developed a sharing resource called XNAT Marketplace (https://marketplace.xnat.org) where developers can contribute and share modules, scripts, and other tools along with documentation. XNAT Marketplace interface and features were designed following patterns established by similar software dissemination sites such as Firefox Add-ons (https://addons.mozilla.org/en-US/firefox/extensions/). Marketplace includes categories, featured plug-ins, a search interface, a tool upload interface, and tool versioning. Together, the API, XNAT plugins, external tools that interact with XNAT, and the XNAT marketplace make up an ecosystem of informatics tools and applications that enable new capabilities in neuroimaging research. Acknowledgements This work was supported by the Biomedical Informatics Research Network (BIRN) (U24 GM104203), the National Alliance for Medical Image Computing (NAMIC) (U54 EB005149), and XNAT (R01 EB009352). Keywords: XNAT, data sharing, open source, plugins, database Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: Neuroimaging Citation: Marcus D, Herrick R, Olsen T, Horton W and Sheu A (2013). The XNAT Ecosystem. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00102 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Dr. Daniel Marcus, Washington University in St. Louis, St. Louis, United States, dmarcus@wustl.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Daniel Marcus Rick Herrick Timothy Olsen William Horton Alan Sheu Google Daniel Marcus Rick Herrick Timothy Olsen William Horton Alan Sheu Google Scholar Daniel Marcus Rick Herrick Timothy Olsen William Horton Alan Sheu PubMed Daniel Marcus Rick Herrick Timothy Olsen William Horton Alan Sheu Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

State-space analysis of working memory in schizophrenia: an fBIRN study.

The neural correlates of working memory (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task conditions, important hypotheses regarding the representation of mental processes in the spatio-temporal patterns of neural recruitment and the differential organization of these mental processes in patients versus controls have not been addressed in this context. This paper uses a multivariate state-space model (SSM) to analyze the differential representation and organization of mental processes of controls and patients performing the Sternberg Item Recognition Paradigm (SIRP) WM task. The SSM is able to not only predict the mental state of the subject from the data, but also yield estimates of the spatial distribution and temporal ordering of neural activity, along with estimates of the hemodynamic response. The dynamical Bayesian modeling approach used in this study was able to find significant differences between the predictability and organization of the working memory processes of SZ patients versus healthy subjects. Prediction of some stimulus types from imaging data in the SZ group was significantly lower than controls, reflecting a greater level of disorganization/heterogeneity of their mental processes. Moreover, the changes in accuracy of predicting the mental state of the subject with respect to parametric modulations, such as memory load and task duration, may have important implications on the neurocognitive models for WM processes in both SZ and healthy adults. Additionally, the SSM was used to compare the spatio-temporal patterns of mental activity across subjects, in a holistic fashion and to derive a low-dimensional representation space for the SIRP task, in which subjects were found to cluster according to their diagnosis.

Technical performance evaluation of a human brain PET/MRI system

Technical performance evaluation of a human brain PET/MRI system. The magnetic field compatible positron emission tomography (PET) insert is based on avalanche photodiode (APD) arrays coupled with lutetium oxyorthosilicate (LSO) crystals and slip-fits into a slightly modified clinical 3-T MRI system. The mutual interference between the two imaging techniques was minimised by the careful design of the hardware to maintain the quality of the B (0) and B (1) field homogeneity. The signal-to-noise ratio (SNR) and the homogeneity of the MR images were minimally influenced by the presence of the PET. Measurements according to the Function Biomedical Informatics Research Network (FBIRN) protocol proved the combined system's ability to perform functional MRI (fMRI). The performance of the PET insert was evaluated according to the National Electrical Manufacturers Association (NEMA) standard. The noise equivalent count rate (NEC) peaked at 30.7 × 10(3) counts/s at 7.3 kBq/mL. The point source sensitivity was greater than 7 %. The spatial resolution in the centre field of view was less than 3 mm. Patient data sets clearly revealed a noticeably good PET and MR image quality. PET and MRI phantom tests and first patient data exhibit the device's potential for simultaneous multiparametric imaging. • Combination of PET and MRI is a new emerging imaging technology. • Evaluated brain PET/MRI enables uncompromised imaging performance. • PET/MRI aims to provide multiparametric imaging allowing acquisition of morphology and metabolism.

Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies

This report provides practical recommendations for the design and execution of multicenter functional MRI (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.

Open Neuroscience Solutions for the Connectome-wide Association Era

The neuroimaging community is at a crossroads. Long characterized by individualism, the data and computational and analytic needs of the connectome-wide association era necessitate cultural reform. Emerging initiatives have demonstrated the feasibility and utility of adopting an open neuroscience model to accelerate the pace and success of scientific discovery.

Enabling collaborative research using the Biomedical Informatics Research Network (BIRN)

As biomedical technology becomes increasingly sophisticated, researchers can probe ever more subtle effects with the added requirement that the investigation of small effects often requires the acquisition of large amounts of data. In biomedicine, these data are often acquired at, and later shared between, multiple sites. There are both technological and sociological hurdles to be overcome for data to be passed between researchers and later made accessible to the larger scientific community. The goal of the Biomedical Informatics Research Network (BIRN) is to address the challenges inherent in biomedical data sharing. BIRN tools are grouped into 'capabilities' and are available in the areas of data management, data security, information integration, and knowledge engineering. BIRN has a user-driven focus and employs a layered architectural approach that promotes reuse of infrastructure. BIRN tools are designed to be modular and therefore can work with pre-existing tools. BIRN users can choose the capabilities most useful for their application, while not having to ensure that their project conforms to a monolithic architecture. BIRN has implemented a new software-based data-sharing infrastructure that has been put to use in many different domains within biomedicine. BIRN is actively involved in outreach to the broader biomedical community to form working partnerships. BIRN's mission is to provide capabilities and services related to data sharing to the biomedical research community. It does this by forming partnerships and solving specific, user-driven problems whose solutions are then available for use by other groups.

XCEDE: an extensible schema for biomedical data.

The XCEDE (XML-based Clinical and Experimental Data Exchange) XML schema, developed by members of the BIRN (Biomedical Informatics Research Network), provides an extensive metadata hierarchy for storing, describing and documenting the data generated by scientific studies. Currently at version 2.0, the XCEDE schema serves as a specification for the exchange of scientific data between databases, analysis tools, and web services. It provides a structured metadata hierarchy, storing information relevant to various aspects of an experiment (project, subject, protocol, etc.). Each hierarchy level also provides for the storage of data provenance information allowing for a traceable record of processing and/or changes to the underlying data. The schema is extensible to support the needs of various data modalities and to express types of data not originally envisioned by the developers. The latest version of the XCEDE schema and manual are available from http://www.xcede.org/ .

A multi-scale parts list for the brain: community-based ontology curation for neuroinformatics with NeuroLex.org

Event Abstract Back to Event A multi-scale parts list for the brain: community-based ontology curation for neuroinformatics with NeuroLex.org Stephen Larson1*, Fahim Imam1, Rembrandt Bakker2, Luan Pham1 and Maryann Martone1 1 University of California, San Diego, Center for Research of Biological Systems, United States 2 Radboud UMC Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Netherlands What makes up the nervous system? What are the inhibitory neurons? What brain regions are they in? What ion channels do they express? Do their dendrites have spines? What fiber bundles do they participate in? These questions and many more could be answered by a multi-scale parts list for the brain, where each part was uniquely indexed and unambiguously defined. Unfortunately, no such standard list has come into widespread use by the neuroscience community. Our effort to address this challenge has resulted in the instantiation of a semantic wiki, NeuroLex.org. For many years we have been building community ontologies for neuroscience, first through the Biomedical Informatics Research Network and now through the Neuroscience Information Framework (http://neuinfo.org) projects. These projects resulted in the construction of a large modular ontology, constructed by importing existing ontologies where possible, called the NIF Standard Ontology (NIFSTD; Bug et al, 2008). It covers behavioral activity, behavioral paradigms, brain regions, cells, diseases, molecules, nervous system function, subcellular components, information resources, resource types, and qualities. NeuroLex.org was originally populated with the NIFSTD in an effort to improve the process of editing and maintaining it. The initial update processes created one wiki page from each ontological "class", which refers to an entity relevant to neuroscience. Content that is added or updated in NeuroLex.org is contributed back to the NIFSTD. This content is not directly added to NIFSTD, but is incorporated into the NIFSTD OWL file by a knowledge engineer after curation by the NIF ontology group. NeuroLex.org's structuring of knowledge allows classes to be rendered using tables, trees, and lists which combine the asserted content of the class with queried content derived from other classes. For example, the page for "Cerebellum" displays a dynamic tree that lists the brain region classes that have been asserted as "part-of" the Cerebellum. This page also lists the neuron classes that have been asserted as "located-in" the Cerebellum. Neither of these property assertions are made on the Cerebellum page itself; rather they are displayed via dynamic queries that find classes related to the Cerebellum throughout the wiki. NeuroLex.org has served as a test bed for a rallying point for the efforts of the Program of Ontologies of Neural System (PONS), an activity of the International Neuroinformatics Coordinating Facility (INCF). In the last year, content and edits have been contributed by several members of the task forces in this activity. In addition, NeuroLex.org has incorporated a lightweight web application for digital atlasing called the Scalable Brain Atlas (http://scalablebrainatlas.incf.org), which generates atlas plate images that highlight a given brain region, in order to more clearly define it. NeuroLex.org has been online since October 2008 and has evolved into a powerful platform for collaboratively maintaining and extending the NIFSTD ontology. It has received 110,000 edits and has 104 registered users. It regularly receives 225 hits per day on average, 80% of which come directly from search engine hit results. NeuroLex.org is built on top of the open source Semantic Mediawiki platform (http://semantic-mediawiki.org) and incorporates several open source extensions such as Semantic Forms and the Halo Extension. We conclude that NeuroLex.org is a good starting point for the collaborative maintenance of ontologies. Other groups are also using a similar approach, e.g., BioMedGT (http://biomedgt.nci.nih.gov). While we are still working through some issues, e.g., synchronizing the NIFSTD with the content on NeuroLex, exporting and importing OWL, and bulk uploading concepts, we believe that semantic wikis are a good tool for providing community contribution and feedback to projects like NIF.

NeuroLex.org - A semantic wiki for neuroinformatics based on the NIF Standard Ontology

Event Abstract Back to Event NeuroLex.org - A semantic wiki for neuroinformatics based on the NIF Standard Ontology Stephen Larson1*, Sarah Maynard1, Fahim Imam1 and Maryann Martone1 1 UCSD, Department of Neuroscience, Faculty of Health,Medicine and Life Sciences, United States The neuroscience community needs its basic domain concepts organized into a coherent framework. Ontologies provide an important medium for reconciling knowledge into a portable and machine readable form. For many years we have been building community ontologies for neuroscience, first through the Biomedical Informatics Research Network and now through the Neuroscience Information Framework projects (http://neuinfo.org). These projects resulted in the construction of a large modular ontology, constructed by importing existing ontologies where possible, called the NIF Standard Ontology (NIFSTD; Bug et al, 2008). The list of topics it covers includes behavioral activity, behavioral paradigms, brain regions, cells, diseases, molecules, nervous system function, subcellular components, information resources, resource types, and qualities.One of the largest roadblocks that we encountered was the lack of tools for domain experts to view, edit, and contribute their knowledge to the NIFSTD ontology. Existing editing tools were difficult to use or required expert knowledge to employ. By combining several open source technologies related to semantic wikis and NIFSTD, we have created NeuroLex.org, the first semantic wiki for neuroscience. NeuroLex.org is built on top of the open source Semantic Mediawiki platform (http://semantic-mediawiki.org). Semantic Mediawiki makes querying the concepts in the ontology via properties or class hierarchy very straightforward. In addition, we have incorporated tools such as Semantic Forms, which allow the ontology classes to be edited as a form rather than as a wiki page with special text mark-up. Some of the fields in the form support autocomplete which allows users to populate those fields with other classes from the ontology. NeuroLex.org has evolved into a powerful platform for collaboratively maintaining and extending the NIFSTD ontology. We have been able to incorporate user feedback and create custom views of the ontology content with very rapid turnaround. Table 1 shows some key metrics that we have collected on its usage. The content contributed to the Neurolex is not directly added to NIFSTD, but is incorporated into the NIFSTD OWL file by a knowledge engineer after curation by the NIF ontology group. We conclude that the Semantic Mediawiki is a good starting point for the collaborative maintenance of ontologies. Other groups are also using a similar approach, e.g., BioMedGT (http://biomedgt.nci.nih.gov). While we are still working through some issues, e.g., synchronizing the NIFSTD with the content on NeuroLex, exporting and importing OWL, and bulk uploading concepts, we believe that semantic wikis are a good tool for providing community contribution and feedback to projects like NIF.