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J.-F. Mangin, D. Rivière, O. Coulon, C. Poupon,
A. Cachia, Y. Cointepas, J.-B. Poline,
D. Le Bihan, J. Régis, and D. Papadopoulos-Orfanos
Coordinate-based versus
structural approaches
to brain image analysis
Artificial Intelligence in Medicine,
in press,
2003
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A basic issue in neurosciences is to look for
possible relationships between brain architecture and
cognitive models. The lack of architectural information in magnetic resonance images, however, has led the neuroimaging community to develop brain mapping strategies based on various coordinate systems without accurate architectural content. Therefore,
the relationships between architectural and functional brain organizations are
difficult to study when analyzing neuroimaging experiments.
This paper advocates that the design of new brain image analysis methods inspired
by the structural strategies often used in computer vision may provide better ways
to address these relationships. The key point underlying this new framework
is the conversion of the raw images into structural representations before analysis.
These representations are made up of data-driven elementary features like
activated clusters, cortical folds or fiber bundles.
Two classes of methods are introduced.
Inference of structural models via matching across a set of individuals is
described first. This inference problem is illustrated by the group analysis of
functional statistical parametric maps. Then, the matching of new individual data with a priori known
structural models is described, using the recognition of the cortical sulci
as a prototypical example.
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