Interindividual functional mapping: a nonlinear local approach.
Within the scope of three-dimensional brain imaging, we propose an interindividual fusion scheme to register functional activations according to anatomical cortical structures, the sulci. This paper is based on the assumption that an important part of functional intersubject variability is encoded in anatomical variability. The aim of this paper is therefore to propose a generic framework to register functional activations according to the relevant anatomical landmarks. Compared to "classical" interindividual fusion schemes, this approach is local. It relies on a statistical sulci shape model accounting for the interindividual variability of a population of subjects and providing deformation modes relative to a reference shape (a mean sulcus). The deformation field obtained between a given sulcus and the reference sulcus is extended to a neighborhood of the given sulcus by using the thin-plate spline interpolation. It is then applied to functional activations located in the vicinity of this sulcus. This approach is compared with rigid and nonrigid registration methods. In this paper, we present results on MEG somatosensory data acquired on 18 subjects. We show that the nonlinear local fusion scheme significantly reduces the observed functional variability.
Corouge
I
I
Projet Vista, IRISA/INRIA-CNRS, Campus de Beaulieu, 35042 Rennes Cedex, France. Isabelle.Corouge@irisa.fr
Hellier
P
P
Gibaud
B
B
Barillot
C
C
D000328:Adult; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D004292:Dominance, Cerebral / Q000502:physiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D008432:Mathematical Computing; D008875:Middle Aged; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D012016:Reference Values