Database-managed grid-enabled analysis of neuroimaging data: the CNARI framework.
Functional magnetic resonance imaging (fMRI) has led to an enormous growth in the study of cognitive neuroanatomy, and combined with advances in high-field electrophysiology (and other methods), has led to a fast-growing field of human neuroscience. Technological advances in both hardware and software will lead to an ever more promising future for fMRI. We have developed a new computational framework that facilitates fMRI experimentation and analysis, and which has led to some rethinking of the nature of experimental design and analysis. The Computational Neuroscience Applications Research Infrastructure (CNARI) incorporates novel methods for maintaining, serving, and analyzing massive amounts of fMRI data. By using CNARI, it is possible to perform naturalistic, network-based, statistically valid experiments in systems neuroscience on a very large scale, with ease of data manipulation and analysis, within reasonable computational time scales. In this article, we describe this infrastructure and then illustrate its use on a number of actual examples in both cognitive neuroscience and neurological research. We believe that these advanced computational approaches will fundamentally change the future shape of cognitive brain imaging with fMRI.
Small
Steven L
SL
Department of Neurology, The University of Chicago, United States. small@uchicago.edu
Wilde
Michael
M
Kenny
Sarah
S
Andric
Michael
M
Hasson
Uri
U
D001921:Brain / Q000033:anatomy & histology / Q000098:blood supply / Q000502:physiology; D001931:Brain Mapping; D003628:Database Management Systems; D003952:Diagnostic Imaging / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014584:User-Computer Interface