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Virtual imaging laboratories for marker discovery in neurodegenerative diseases

Nature Reviews Neurology volume 7, pages 429–438 (2011)Cite this article

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Abstract

The unprecedented growth, availability and accessibility of imaging data from people with neurodegenerative conditions has led to the development of computational infrastructures, which offer scientists access to large image databases and e-Science services such as sophisticated image analysis algorithm pipelines and powerful computational resources, as well as three-dimensional visualization and statistical tools. Scientific e-infrastructures have been and are being developed in Europe and North America that offer a suite of services for computational neuroscientists. The convergence of these initiatives represents a worldwide infrastructure that will constitute a global virtual imaging laboratory. This will provide computational neuroscientists with a virtual space that is accessible through an ordinary web browser, where image data sets and related clinical variables, algorithm pipelines, computational resources, and statistical and visualization tools will be transparently accessible to users irrespective of their physical location. Such an experimental environment will be instrumental to the success of ambitious scientific initiatives with high societal impact, such as the prevention of Alzheimer disease. In this article, we provide an overview of the currently available e-infrastructures and consider how computational neuroscience in neurodegenerative disease might evolve in the future.

Key Points

  • Image data sets of unprecedented size from healthy and pathologically aging individuals are posing new challenges related to availability and accessibility of data, computational resources, and visualization tools

  • Scientific e-infrastructures based on grid computing, such as LONI, neuGRID, and CBRAIN, offer a suite of services to facilitate advanced computational analyses on brain images

  • In the neurodegenerative disease field, such e-infrastructures are critical to foster the development of disease markers for early diagnosis and to track the course of the disease in clinical trials

  • Steps have been taken towards convergence of the individual infrastructures into a worldwide, cloud-based global virtual imaging laboratory

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Figure 1: Image-processing algorithms, suites and tools available in the LONI, neuGRID and CBRAIN infrastructures.
Figure 2: End-to-end data sharing, databasing and data choosing with the IDA database.38
Figure 3: Graphical representation of the CIVET cortical thickness extraction algorithm exposed through the LONI Pipeline Environment.
Figure 4: Maps of mean cortical thickness in the Alzheimer's Disease Neuroimaging Initiative dataset obtained with CIVET, FreeSurfer and RIC-BrainVISA, and displayed with the same visualization tool.

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Acknowledgements

The authors thank all the partners in FP7 outGRID, FP7 neuGRID, LONI–ADNI and CBRAIN for collecting and providing information on main National and International virtual imaging laboratories. Special thanks go to Richard McClatchey, University of the West of England, Bristol, UK. G. B. Frisoni and D. Manset are supported by FP7 neuGRID and FP7 outGRID funded by the European Commission (FP7/2007-2013) under grant agreement no. 211,714 and no. 246,690, DG INFSO, e-Infrastructures. A. Toga was supported by NIH LONI–ADNI projects. A. Evans was supported by CANARIE Inc. and CBRAIN/GBRAIN projects.

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Authors and Affiliations

  1. IRCCS Fatebenefratelli, Via Pilastroni 4, 25125, Brescia, Italy

    Giovanni B. Frisoni & Alberto Redolfi

  2. MAAT France, Immeuble Alliance, Entrée A, 74160, Archamps, France

    David Manset

  3. Montreal Neurological Institute at McGill University, Sherbrooke Street West 845, Montreal, H3A 2T5, QC, Canada

    Marc-Étienne Rousseau & Alan C. Evans

  4. Laboratory Of Neuro Imaging, UCLA School of Medicine, Neuroscience Research Building, Suite 225, 635 Charles E. Young Drive South, Los Angeles, 90095-7334, CA, USA

    Arthur Toga

Authors
  1. Giovanni B. Frisoni
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  2. Alberto Redolfi
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  3. David Manset
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  4. Marc-Étienne Rousseau
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  6. Alan C. Evans
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Contributions

G. B. Frisoni developed the architecture of the manuscript. G. B. Frisoni and A. Redolfi drafted a first version, which was completed, edited, and reviewed for important intellectual content by D. Manset, M.-É. Rousseau, A. Toga and A. C. Evans.

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Correspondence to Giovanni B. Frisoni.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Imaging processing algorithms and suites available in the three infrastructures (PDF 107 kb)

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Frisoni, G., Redolfi, A., Manset, D. et al. Virtual imaging laboratories for marker discovery in neurodegenerative diseases. Nat Rev Neurol 7, 429–438 (2011). https://doi.org/10.1038/nrneurol.2011.99

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