The Clinical Value of Large Neuroimaging Data Sets in Alzheimer's Disease
Section snippets
New era of collaborative, interdisciplinary team science
The ongoing convergence and integration of neuroscientific infrastructures worldwide is heading to the creation of a global virtual imaging laboratory. Through ordinary Web browsers, large-scale image data sets and related clinical data and biospecimens, algorithm pipelines, computational resources, and visualization and statistical toolkits are easily accessible to users regardless of their physical location or disciplinary orientation. The promise of this investigatory
Impact of key areas of e-science on neuroscience and neurology
Multisite neuroimaging studies are dramatically accelerating the pace and volume of discoveries regarding major brain disease and the contrasts between normal and abnormal brain structure and function.14 The large-scale, purpose-driven data sets generated by these consortia can then be used by the broader community to model and predict clinical outcomes as well as guide clinicians in selecting treatment options for various neurologic diseases. Multisite trials are an important element in the
Data integration
A subset of data from the clinical database is also integrated into the IDA to support richer multimodal queries across the combined set (Fig. 3). The selection of the initial set of clinical data elements was based on user surveys in which participants identified the elements they believed would be most useful in supporting their investigations. Because the clinical data originate in an external database, automated methods for obtaining and integrating the external data also validate and
Infrastructure mechanics
A robust and reliable infrastructure is a necessity for supporting a resource intended to serve a global scientific community. The hardware infrastructure of the informatics core provides high performance, security, and reliability at each level. The fault-tolerant network infrastructure has no single points of failure. A firewall appliance protects and segments the network traffic, permitting only authorized ingress and egress. Multiple redundant database, application, and Web servers ensure
Summary
Given the vagaries of disease modification and of what will eventually prove a valid, reliable, and compelling empiric basis for distinguishing between true disease modification and symptomatic treatment effects, ADNI investigators are bringing multimodal neuroimaging techniques to the cutting edge of usefulness as dynamic biomarkers, sensitive to disease state and progression across different stages.18, 33, 63 From here, the next step is neuroimages as reliable treatment trial end points and
Acknowledgments
The original work was primarily funded by the ADNI (Principal Investigator: Michael Weiner; National Institutes of Health (NIH) grant number U01 AG024904). ADNI is funded by the National Institute of Aging, the National Institute of Biomedical Imaging and Bioengineering, and the Foundation for the National Institutes of Health, through generous contributions from the following companies and organizations: Pfizer Inc, Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline,
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Cited by (9)
Open access image repositories: high-quality data to enable machine learning research
2020, Clinical RadiologyCitation Excerpt :All data within TCIA have been fully de-identified using tools and procedures that have been validated to comply with US and international privacy laws.28 Although the number of on-line image repositories is growing rapidly (e.g.,29–35) to the authors' knowledge there is no data management service that can support the range of image data types, rich metadata, robust curation processes, and breadth of both human and computer access methods as TCIA. The rapid growth of quantitative image analysis based on machine learning has extended the mission of TCIA to include providing data for training and testing of new algorithms.36
The Global Alzheimer's Association Interactive Network
2016, Alzheimer's and DementiaCitation Excerpt :As the questions become more complicated, the data that can provide answers becomes more difficult to find and it becomes necessary to aggregate more and more data collections together. As a model of collaborative research, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been successful in standardizing its data acquisition protocols, allowing its results to be compared together across participating sites, and in making new data publicly available shortly after it is archived [3,4]. ADNI is focused on developing clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease [5], and its success has led to the establishment of ADNI-like programs in Australia, Europe, Japan, Argentina, and Korea and to study other diseases such as Parkinson's disease [6].
The Alzheimer's Disease Neuroimaging Initiative informatics core: A decade in review
2015, Alzheimer's and DementiaCitation Excerpt :How easily can data be found and queried by anyone who is provided access further enhanced its usefulness. The ever increasing rate of utilization seen in Alzheimer's Disease Neuroimaging Initiative (ADNI) and other efforts has been driven largely by the creation and adoption of successful informatics solutions along with the demand for multiscale, multimodal, large N data in the investigation of fundamental disease processes [1]; the necessity of applying methodologies and insights from multiple disciplines to adequately integrate, query, analyze, and interpret the data [2]; and the movement of science in general toward freely and openly available information [3]. We have reached a point in biomedical science where the electronic collection, organization, annotation, storage, and distribution of data are essential activities in most translational discovery processes.
National neuroinformatics framework for canadian consortium on neurodegeneration in aging (CCNA)
2018, Frontiers in NeuroinformaticsSharing big biomedical data
2015, Journal of Big Data