Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

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TitleComputational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.
Publication TypeJournal Article
Year of Publication2016
AuthorsDavatzikos, C
JournalMed Image Anal
Volume33
Pagination149-154
Date Published2016 Oct
ISSN1361-8423
KeywordsAlgorithms, Brain, Humans, Machine Learning, Magnetic Resonance Imaging, Multivariate Analysis, Neuroanatomy, Support Vector Machine
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.

DOI10.1016/j.media.2016.06.026
Alternate JournalMed Image Anal
PubMed ID27514582
PubMed Central IDPMC5642036
Grant ListR01 AG014971 / AG / NIA NIH HHS / United States