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Disease-Specific Brain Atlases
Source: 2000 May;:131-177.
Author: Thompson PM, Mega MS, Toga AW.
Abstract:
ABSTRACT: Atlases of the human brain, in health and disease, provide a comprehensive framework for understanding brain structure and function. The complexity and variability of brain structure, especially in the gyral patterns of the human cortex, present challenges in creating standardized brain atlases that reflect the anatomy of a population. This paper introduces the concept of a population-based, disease-specific brain atlas that can reflect the unique anatomy and physiology of a particular clinical subpopulation. Based on well-characterized patient groups, disease-specific atlases contain thousands of structure models, composite maps, average templates, and visualizations of structural variability, asymmetry and group-specific differences. They correlate the structural, metabolic, molecular and histologic hallmarks of the disease. Rather than simply fusing information from multiple subjects and sources, new mathematical strategies are introduced to resolve group-specific features not apparent in individual scans. High-dimensional elastic mappings, based on covariant partial differential equations, are developed to encode patterns of cortical variation. In the resulting brain atlas, disease-specific features and regional asymmetries emerge that are not apparent in individual anatomies. The resulting probabilistic atlas can identify patterns of altered structure and function, and can guide algorithms for knowledge-based image analysis, automated image labeling, tissue classifications, data mining and functional image analysis. INTRODUCTION: Advanced brain imaging technologies now provide a means to investigate disease and therapeutic response in their full spatial and temporal complexity. Imaging studies of clinical populations continue to uncover new patterns of altered stucture and function, and novel algorithims are being applied to relate these patterns to cognitive and genetic parameters. As imaging studes expand into ever-larger patient populations, population-based brain atlases will offer a powerful framework to synthesize the results of disparate imaging studies. These atlases require novel analytical tools to fuse data across subjects, modalities, and time, enabling detection of group-specific features not apparent in individual patints' scans. METHODS: High resolution 3D T1-weighted fast SPGR (spoiled GRASS) MRI volumes were acquired from 26 subjects diagnosed with mild to moderate Alzheimer's Disease and 20 elderly control subjects. 36 major external fissures and sulci in the brain were manually outlined on highly magnified surface-rendered images of each cortical surface. In both hemispheres, 3D curves were drawn to represent superior and inferior frontal, central, postcentral, intraparietal, superior and inferior temporal, collateral, olfactory and occipito-temporal sulci, as well as the Sylvian fissures. RESULTS: 3D displacement fields were recovered mapping each patient into gyrus-by-gyrus correspondence with the average cortex, after affine differences between each individual brain and the average template were factored out. Anatomic variability was then defined at each point on the average cortical surfaces as the root mean square magnitude of the 3D displacement vectors, assigned to each point, in the surface maps driving individuals onto the group average. CONCLUSION: Encoding patterns of anatomical vriation in diseased populations presents significant challenges. By presenting an atlasing scheme that treats intensity and geometric variation separately, we described the creation of well resolved image templates and probabilistic models of anatomy that reflect the average morphology of a group. The continual refinement of anatomic templates ultimately will enable deformation-based morphometry in large image databases, and shows promise in linking imaging findings with demographic, genetic, and therapeutic parameters.
Source: 2000 May;:131-177.
Author: Thompson PM, Mega MS, Toga AW.
Abstract:
ABSTRACT: Atlases of the human brain, in health and disease, provide a comprehensive framework for understanding brain structure and function. The complexity and variability of brain structure, especially in the gyral patterns of the human cortex, present challenges in creating standardized brain atlases that reflect the anatomy of a population. This paper introduces the concept of a population-based, disease-specific brain atlas that can reflect the unique anatomy and physiology of a particular clinical subpopulation. Based on well-characterized patient groups, disease-specific atlases contain thousands of structure models, composite maps, average templates, and visualizations of structural variability, asymmetry and group-specific differences. They correlate the structural, metabolic, molecular and histologic hallmarks of the disease. Rather than simply fusing information from multiple subjects and sources, new mathematical strategies are introduced to resolve group-specific features not apparent in individual scans. High-dimensional elastic mappings, based on covariant partial differential equations, are developed to encode patterns of cortical variation. In the resulting brain atlas, disease-specific features and regional asymmetries emerge that are not apparent in individual anatomies. The resulting probabilistic atlas can identify patterns of altered structure and function, and can guide algorithms for knowledge-based image analysis, automated image labeling, tissue classifications, data mining and functional image analysis. INTRODUCTION: Advanced brain imaging technologies now provide a means to investigate disease and therapeutic response in their full spatial and temporal complexity. Imaging studies of clinical populations continue to uncover new patterns of altered stucture and function, and novel algorithims are being applied to relate these patterns to cognitive and genetic parameters. As imaging studes expand into ever-larger patient populations, population-based brain atlases will offer a powerful framework to synthesize the results of disparate imaging studies. These atlases require novel analytical tools to fuse data across subjects, modalities, and time, enabling detection of group-specific features not apparent in individual patints' scans. METHODS: High resolution 3D T1-weighted fast SPGR (spoiled GRASS) MRI volumes were acquired from 26 subjects diagnosed with mild to moderate Alzheimer's Disease and 20 elderly control subjects. 36 major external fissures and sulci in the brain were manually outlined on highly magnified surface-rendered images of each cortical surface. In both hemispheres, 3D curves were drawn to represent superior and inferior frontal, central, postcentral, intraparietal, superior and inferior temporal, collateral, olfactory and occipito-temporal sulci, as well as the Sylvian fissures. RESULTS: 3D displacement fields were recovered mapping each patient into gyrus-by-gyrus correspondence with the average cortex, after affine differences between each individual brain and the average template were factored out. Anatomic variability was then defined at each point on the average cortical surfaces as the root mean square magnitude of the 3D displacement vectors, assigned to each point, in the surface maps driving individuals onto the group average. CONCLUSION: Encoding patterns of anatomical vriation in diseased populations presents significant challenges. By presenting an atlasing scheme that treats intensity and geometric variation separately, we described the creation of well resolved image templates and probabilistic models of anatomy that reflect the average morphology of a group. The continual refinement of anatomic templates ultimately will enable deformation-based morphometry in large image databases, and shows promise in linking imaging findings with demographic, genetic, and therapeutic parameters.