title: Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.
abstract: The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges.
authors: Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, United States . Electronic address: christos@rad.upenn.edu.
authors: Christos
authors: C
authors: Davatzikos
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D015999:Multivariate Analysis; D009445:Neuroanatomy / Q000379:methods*; D060388:Support Vector Machine
keywords: Brain image analysis; Computational neuroanatomy; Machine learning; Pattern analysis