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

Christos Davatzikos

Research output: Contribution to journalEditorialpeer-review

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.

Original languageEnglish (US)
Pages (from-to)149-154
Number of pages6
JournalMedical image analysis
Volume33
DOIs
StatePublished - Oct 1 2016

Keywords

  • Brain image analysis
  • Computational neuroanatomy
  • Machine learning
  • Pattern analysis

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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