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Related Concept Videos

Anatomy of the Brain: Major Regions01:20

Anatomy of the Brain: Major Regions

The brain is the most complex organ in the human body. It consists of four main parts: the cerebrum, diencephalon, cerebellum, and brainstem.
The cerebrum is the largest section of the brain and divides into left and right hemispheres, separated by a deep fissure. The cerebral outer layer of grey matter — the cerebral cortex — comprises elevations called gyri and shallow groves called sulci. The inner portion of white matter includes long nerve fibers known as axons, which connect various areas...

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Related Experiment Video

Updated: Jun 23, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Disease-Specific Probabilistic Brain Atlases.

Paul Thompson1, Michael S Mega, Arthur W Toga

  • 1Laboratory of Neuro Imaging, Dept. of Neurology, Division of Brain Mapping, UCLA School of Medicine, Los Angeles, CA.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces disease-specific brain atlases to capture unique patient anatomy. These advanced atlases reveal subtle disease patterns and guide neuroimaging analysis.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Atlasing

Background:

  • Standardized brain atlases face challenges due to high anatomical variability, especially in cortical gyral patterns.
  • Existing atlases struggle to represent the unique neuroanatomy of specific patient subpopulations.
  • Understanding brain structure and function in health and disease requires comprehensive frameworks.

Purpose of the Study:

  • To introduce a novel concept of population-based, disease-specific brain atlases.
  • To develop methods for reflecting the unique anatomy and physiology of clinical subpopulations.
  • To correlate structural, metabolic, molecular, and histologic disease hallmarks.

Main Methods:

  • Utilizing well-characterized patient groups to build disease-specific atlases.
  • Developing new mathematical strategies, including high-dimensional elastic mappings based on covariant partial differential equations.
  • Encoding patterns of cortical variation to resolve group-specific features not apparent in individual scans.

Main Results:

  • Disease-specific atlases incorporate thousands of structure models, composite maps, and average templates.
  • New mathematical strategies reveal group-specific features and regional asymmetries.
  • The resulting probabilistic atlas identifies altered structure and function patterns.

Conclusions:

  • Disease-specific brain atlases offer a powerful tool for understanding neuroanatomy in clinical populations.
  • These atlases can guide advanced neuroimaging analysis, including automated labeling and data mining.
  • The developed methods enable the emergence of disease-specific features and asymmetries not visible in individual scans.