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Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the... more
Stroke is one of the leading causes of death and disability worldwide. Reducing this disease burden through drug discovery and evaluation of stroke patient outcomes requires broader characterization of stroke pathophysiology, yet the underlying biologic and genetic factors contributing to outcomes are largely unknown. Remedying this critical knowledge gap requires deeper phenotyping, including large-scale integration of demographic, clinical, genomic, and imaging features. Such big data approaches will be facilitated by developing and running processing pipelines to extract stroke-related phenotypes at large scale. Millions of stroke patients undergo routine brain imaging each year, capturing a rich set of data on stroke-related injury and outcomes. The Stroke Neuroimaging Phenotype Repository (SNIPR) was developed as a multi-center centralized imaging repository of clinical computed tomography (CT) and magnetic resonance imaging (MRI) scans from stroke patients worldwide, based on ...
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Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import,... more
Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer ...
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Research Interests:
Page 1. THE 9/11 COMMISSION REPORT Page 2. Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including ...
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Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course... more
Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining 'normality'); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range ...
Research Interests: Psychology, Computer Science, Neuroimaging, Biology, Medicine, and 10 moreDatabase Management Systems, Humans, Neuroimage, Data Sharing, Information Storage and Retrieval, Information Dissemination, Biobank, Multidisciplinary Approach, Psychology and Cognitive Sciences, and Medical and Health Sciences
Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition,... more
Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research...
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Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of... more
Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma ...
Research Interests: Computer Science, Algorithms, Artificial Intelligence, Image Processing, Neuroinformatics, and 11 moreMagnetic Resonance Imaging, Workflow, Medicine, Diffusion MRI, Brain Mapping, Humans, Multimodal imaging, Reproducibility of Results, Neurosciences, Brain Neoplasms, and Biochemistry and cell biology
Research Interests: Computer Science, Neuroimaging, Magnetic Resonance Imaging, Aging, Medical Physics, and 15 moreAdolescent, Positron Emission Tomography, Medicine, Brain, Humans, Female, Neuroimage, Male, Aged, Middle Aged, Information Dissemination, Adult, Alzheimer Disease, Medical and Health Sciences, and HIV infections
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In this paper, we describe an instance of the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), a schizophrenia-related dataset hosted at XNAT Central, and the SchizConnect data portal used for accessing and sharing... more
In this paper, we describe an instance of the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), a schizophrenia-related dataset hosted at XNAT Central, and the SchizConnect data portal used for accessing and sharing the dataset. NUSDAST was built and extended upon existing, standard schemas available for data sharing on XNAT Central (http://central.xnat.org/). With the creation of SchizConnect, we were able to link NUSDAST to other neuroimaging data sources and create a powerful, federated neuroimaging resource.
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Research Interests: Computer Science, Neuroimaging, Behavior, Magnetoencephalography, Magnetic Resonance Imaging, and 15 moreAccess To Information, Medicine, Brain Mapping, Modalities, Brain, Humans, Neuroimage, Data Sharing, Information Dissemination, Dissemination, Connectome, Internet, Data Set, Human Connectome Project, and Medical and Health Sciences
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Research Interests: Computer Science, Algorithms, Chemistry, Individuality, Positron Emission Tomography, and 15 moreMedicine, PET, Amyloid, Humans, Computer Simulation, Cerebral Cortex, PMC, Neuroimage, Longitudinal Studies, PIB, Cross Sectional Studies, Alzheimer Disease, Cohort Studies, Psychology and Cognitive Sciences, and Medical and Health Sciences
Research Interests: Computer Science, Algorithms, Artificial Intelligence, Computer Vision, Neuroimaging, and 15 moreNeuroinformatics, Magnetic Resonance Imaging, Face, Adolescent, Medicine, Brain, Humans, Child, Confidentiality, Aged, Middle Aged, Adult, Biometric Identification, Neurosciences, and Biochemistry and cell biology
Research Interests: Computer Science, Visualization, Data Mining, Computational Biology, Medicine, and 11 moreQuality Control, Database Management Systems, Brain, Humans, Neuroimage, Data Quality, Computer User Interface Design, Information Storage and Retrieval, Connectome, Psychology and Cognitive Sciences, and Medical and Health Sciences
Research Interests: Computer Science, Metadata, Workflow, Digital imaging, Medicine, and 13 moreDatabase Management Systems, Software, Humans, Data Display, Search Engine, DICOM, Diagnostic Imaging, Clinical Sciences, Computer User Interface Design, Computer communication networks, Radiology Information Systems, Sensitivity and Specificity, and Software Validation
Research Interests: Computer Science, Information Management, Neuroimaging, Computer Security, Digital imaging, and 15 moreMedicine, Database Management Systems, Digital, Brain, Humans, Data Science, Article, DICOM, Diagnostic Imaging, Knowledge Bases, Clinical Sciences, Computer User Interface Design, Information Storage and Retrieval, Neurosciences, and Databases as topic
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The Open Access Series of Imaging Studies is a series of neuroimaging data sets that are publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 years all... more
The Open Access Series of Imaging Studies is a series of neuroimaging data sets that are publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 years all acquired on the same scanner using identical sequences. Each subject was scanned on two or more visits, separated by at least 1 year for a total of 373 imaging sessions. Subjects were characterized using the Clinical Dementia Rating (CDR) as either nondemented or with very mild to mild Alzheimer's disease. Seventy-two of the subjects were characterized as nondemented throughout the study. Sixty-four of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with CDR 0.5 similar level of impairment to individuals elsewhere considered to have “mild cognitive impairment.” Another 14 subjects were characterized as nondemented at the time of their initial vis...
Research Interests: Cognitive Science, Computer Science, Neuroimaging, Dementia, Magnetic Resonance Imaging, and 15 moreMild Cognitive Impairment, Cognitive Neuroscience, Medicine, Cognitive, Brain Mapping, Brain, Humans, Female, Male, Aged, Middle Aged, Longitudinal Studies, Older Adult, Neurosciences, and Normal Aging
The Open Access Series of Imaging Studies is a series of magnetic resonance imaging data sets that is publicly available for study and analysis. The initial data set consists of a cross-sectional collection of 416 subjects aged 18 to 96... more
The Open Access Series of Imaging Studies is a series of magnetic resonance imaging data sets that is publicly available for study and analysis. The initial data set consists of a cross-sectional collection of 416 subjects aged 18 to 96 years. One hundred of the included subjects older than 60 years have been clinically diagnosed with very mild to moderate Alzheimer's disease. The subjects are all right-handed and include both men and women. For each subject, three or four individual T1-weighted magnetic resonance imaging scans obtained in single imaging sessions are included. Multiple within-session acquisitions provide extremely high contrast-to-noise ratio, making the data amenable to a wide range of analytic approaches including automated computational analysis. Additionally, a reliability data set is included containing 20 subjects without dementia imaged on a subsequent visit within 90 days of their initial session. Automated calculation of whole-brain volume and estimated...
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Research Interests: Engineering, Computer Science, Grid Computing, Computational Biology, Data Management, and 15 moreExperimental Design, Database Management Systems, Biomedical Imaging, Humans, Data Science, Data Collection, DICOM, Information Storage and Retrieval, Data Exchange, Data Integrity, Information Dissemination, Cooperative Behavior, Extensible Markup Language, Data Grid, and Data management system
The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience... more
The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: (1) storage of primary and processed data, (2) systematic processing and analysis of the data, (3) open-access data-sharing, and (4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (A...