title: Pattern based morphometry.
abstract: Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI. In both cases PBM is able to uncover complex global patterns effectively.
authors: Section for Biomedical Image Analysis, University of Pennsylvania, USA.
authors: Bilwaj
authors: B
authors: Gaonkar
authors: Kilian
authors: K
authors: Pohl
authors: Christos
authors: C
authors: Davatzikos
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*