Citation

Gee T, Kenny S, Price CJ, Seghier ML, Small SL, Leff AP, Pacurar A, Strother SC. Arch. Ital. Biol. 2010; 148(3): 207-217.

Affiliation

Rotman Research Institute and Centre for Stroke Recovery, Baycrest, Toronto, Canada. tgee@rotman-baycrest.on.ca

Copyright

(Copyright © 2010, Universita Degli Studi Di Pisa)

DOI

unavailable

PMID

21175009

Abstract

In order to accelerate translational neuroscience with the goal of improving clinical care it has become important to support rapid accumulation and analysis of large, heterogeneous neuroimaging samples and their metadata from both normal control and patient groups. We propose a multi-centre, multinational approach to accelerate the data mining of large samples and facilitate data-led clinical translation of neuroimaging results in stroke. Such data-driven approaches are likely to have an early impact on clinically relevant brain recovery while we simultaneously pursue the much more challenging model-based approaches that depend on a deep understanding of the complex neural circuitry and physiological processes that support brain function and recovery. We present a brief overview of three (potentially converging) approaches to neuroimaging data warehousing and processing that aim to support these diverse methods for facilitating prediction of cognitive and behavioral recovery after stroke, or other types of brain injury or disease.

Language: en