NeuroImage Databases: Overcoming the Unknown
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1
UCLA, LONI, United States
The ability to statistically and visually compare and contrast brain image data from multiple subjects is essential to understanding normal variability and differentiating normal from diseased populations. This talk describes the concerns that must be dealt with to maximize the value of data contained in databases. The problem is particularly acute when the ultimate goal is the synthesis of atlases, the creation of models and statistical comparisons across different cohorts. Data coming from multiple sources, subjects, protocols and devices must either be treated or described sufficiently to make it comparable. Legacy data presents unique problems as an accurate history may be unavailable. Newly acquired data affords the opportunity to create detailed metadata but must be extendable, as needed, to accommodate pre-processing and other data manipulations. The ever increasing size of emerging databases also poses challenges for automation and direct linkages between data archives and processing procedures. Examples of these and related issues will be illustrated in applications with several consortia and projects.
Conference:
Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009.
Presentation Type:
Oral Presentation
Topic:
Keynote speakers
Citation:
Toga
AW
(2019). NeuroImage Databases: Overcoming the Unknown.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2009.
doi: 10.3389/conf.neuro.11.2009.08.127
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Received:
10 Jun 2009;
Published Online:
09 May 2019.
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Correspondence:
Arthur W Toga, UCLA, LONI, Los Angeles, United States, toga@loni.usc.edu