By Paul Thompson PhD, Michael S. Mega MD PhD, Arthur W. Toga PhD
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 wellcharacterized 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 classification, 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 structure and function, and novel algorithms are being applied to relate these patterns to cognitive and genetic parameters. As imaging studies expand into everlarger 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 patients’ scans. Once built, these atlases can be stratified into subpopulations to reflect a particular clinical group. The disease-specific features they resolve can then be linked with demographic factors such as age, gender, handedness, as well as specific clinical or genetic parameters
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