title: Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses.
abstract: Traditional analyses of Functional Magnetic Resonance Imaging (fMRI) use little anatomical information. The registration of the images to a template is based on the individual anatomy and ignores functional information; subsequently detected activations are not confined to gray matter (GM). In this paper, we propose a statistical model to estimate a probabilistic atlas from functional and T1 MRIs that summarizes both anatomical and functional information and the geometric variability of the population. Registration and Segmentation are performed jointly along the atlas estimation and the functional activity is constrained to the GM, increasing the accuracy of the atlas.
authors: CMAP Ecole Polytechnique, Route de Saclay, 91128 Palaiseau, France.
authors: Hao
authors: H
authors: Xu
authors: Parietal Team, INRIA Saclay-Ile-de-France.
authors: Bertrand
authors: B
authors: Thirion
authors: CMAP Ecole Polytechnique, Route de Saclay, 91128 Palaiseau, France.
authors: Stéphanie
authors: S
authors: Allassonnière
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D064847:Multimodal Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity