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Updated: Feb 8, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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

Christos Davatzikos¹

¹Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, United States .

Medical Image Analysis

|August 13, 2016

Summary

This summary is machine-generated.

Computational neuroanatomy has rapidly advanced over 20 years, driven by image warping. This work details the evolution of brain parcellation, morphometric, and machine learning analyses in neuroimaging.

Keywords:

Brain image analysis Computational neuroanatomy Machine learning Pattern analysis

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Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Computational neuroanatomy
Neuroimaging analysis
Brain mapping

Background:

High-dimensional image warping methods have significantly advanced computational neuroanatomy.
Key analytical approaches include brain parcellation, voxel-based morphometry, and machine learning.

Discussion:

The field has progressed from single-atlas parcellation to ensemble methods.
Morphometric analyses evolved from basic t-tests to optimized regional pattern analyses.
Machine learning applications advanced from support vector machines to generative-discriminative models.

Key Insights:

Refined image warping techniques are central to modern neuroanatomical studies.
Multivariate pattern analysis methods now address neuroanatomical pattern heterogeneity.
The evolution reflects overcoming significant analytical challenges in the field.

Outlook:

Future directions include further advancements in analytical methods.
Addressing heterogeneity in neuroanatomical patterns remains a key challenge.
Continued development in computational neuroanatomy promises deeper insights into brain structure.