Abstract

Neuroimaging experiments can generate impressive volumes of data and many images of the results. This is particularly true of multi-modal imaging studies that use more than one imaging technique, or when imaging is combined with other assessments. A challenge for these studies is appropriate visualisation of results in order to drive insights and guide accurate interpretations. Next-generation visualisation technology therefore has much to offer the neuroimaging community. One example is the Imperial College London Data Observatory; a high-resolution (132 megapixel) arrangement of 64 monitors, arranged in a 313 degree arc, with a 6 metre diameter, powered by 32 rendering nodes. This system has the potential for high-resolution, large-scale display of disparate data types in a space designed to promote collaborative discussion by multiple researchers and/or clinicians. Opportunities for the use of the Data Observatory are discussed, with particular reference to applications in Multiple Sclerosis (MS) research and clinical practice. Technical issues and current work designed to optimise the use of the Data Observatory for neuroimaging are also discussed, as well as possible future research that could be enabled by the use of the system in combination with eye-tracking technology.

Highlights

  • A natural trend in many scientific disciplines is towards greater size and complexity of the empirical data sets that are collected

  • Grant information: The authors declare that no grants were involved in supporting this work

  • This may be driven by the development of entirely new research methodologies or diagnostic tests, further refinements of existing technology, or by the incorporation of multiple measurement methods to examine a single question

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Summary

Joshua Balsters Switzerland

ETH Zurich, Zurich, Any reports and responses or comments on the article can be found at the end of the article. Keywords Neuroimaging , fMRI , PET , visualisation , data observatory , display technology , eye-tracking , multiple sclerosis. This article is included in the INCF gateway. This article is included in the University College London collection. Grant information: The authors declare that no grants were involved in supporting this work. How to cite this article: Wall MB, Birch D and Yong MY.

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