H. The Atlas Is the Database
IX. Summary
An introduction to cartography of the brain is, by its very intent, broad in its coverage. This chapter has described the emergence of cartographic strategies and their application to a better understanding of brain structure and function.
Maps of the brain are different from maps of other objects, because they must accommodate so many diverse aspects of neuroscientific inquest. Therefore, there are many different versions of brain maps. The degree to which each is suc- cessful depends not only upon the available technology for acquiring the data, analyzing them, and taking advantage of them, but on how it is used with complete understanding of its underlying assumptions and limitations. Equally impor- tant is the faith garnered by these maps. The degree to which Figure 6Statistical maps. (A) The inherent variability contained across a subpopulation of subjects offers the opportunity to characterize that popula- tion using a probability map. The map comprises a collection of data sets, each transformed to the same coordinate space. Anatomic structures are identified and delineated for each contributing data set and combined in the probabilistic map. The degree of consistency or variability for each structure can be meas- ured and displayed in one of several ways. Part of the subvolume probabilistic atlas for the Alzheimer’s disease (AD) population is shown. The atlas is con- structed using the MRI data from 30 AD subjects randomly chosen from a large AD database. The focus is centered around the left frontal lobe with white matter (green color), gray matter (blue), and cerebrospinal fluid (red) clearly visible as fuzzy clouds. The color saturation indicates the chance that each voxel is part of the specified tissue type of the left frontal lobe. Higher probability values yield higher confidence that the 3D coordinate is part of the region of interest and conversely, lower color saturation shows decreased confidence associating the stereotactic location to the corresponding region. The atlas con- tains 180 probabilistic regions and is used for automated volumetric studies, as well as for statistical analysis of functional brain data for the AD and elderly populations. (B) Average cortical surface model from 15 male normal subjects. Parametric cortical models were extracted from T1-weighted magnetic res- onance data using signal intensity information after the removal of extracortical tissue. A complex vector-valued flow field drives cortical surface anatomy from each subject into correspondence and retains variability information about the extent of the deformation in color. Hotter colors represent increased vari- ability in cortical surface anatomy within the group. (C) Tensor map of cortex. Striking differences are found, even among normal human subjects, in the gyral patterns of the cerebral cortex. Tensor maps can be used to visualize these complex patterns of anatomical variation. In this map, color distinguishes regions of high variability (pink colors) from areas of low variability (blue). Ellipsoidal glyphs indicate the principal directions of variation—they are most elongated along directions in which there is greatest anatomic variation across subjects. Each glyph represents the covariance tensor of the vector fields that map individual subjects onto their group average anatomic representation. This map is based on a group of 20 elderly normal subjects. The resulting infor- mation can be leveraged to distinguish normal from abnormal anatomical variants using random tensor field algorithms. (D) 3D cortical surface and sulcal variability map. The surface model represents the average cortical surface from 28 normal subjects. The color indexes variability measured from homolo- gous coordinate points from cortical surface models extracted from individual subjects in 3D stereotaxic space. The major cortical sulci are averaged and mapped onto the average cortical surface model and variability is indexed in different colors. Variability is calculated as the root mean square magnitude of displacement vectors required to map equivalent coordinate points from each anatomical model onto the group average. (E) The anatomic variation in gyral/sulcal cortical anatomy in a group of normal children (ages 6–16) is shown. (F) A representative picture of the subparcellation of the cerebral lobes in a child. In order to describe changes in tissue types (gray, white, and CSF) and volumetry during normal childhood and adolescence, we delineate the cere- bral hemispheres into component lobes to assess which regions may most be affected by developmental processes.
maps of the brain are developed and used by the scientific community depends on the collective belief that the data are accurate, reliable, representative, and above all, useful.
Acknowledgments
The authors thank colleagues in the Laboratory of Neuro Imaging and Brain Mapping Center for their assistance. Special thanks go to Andrew Lee for his digital artwork. This work was supported in part by the National Institutes of Health (RR13642 and RR00865), the National Library of Medicine (R01 LM05639), and the Human Brain Project (P20 MH52176) funded jointly by the National Institute of Mental Health, National Institute on Drug Abuse, National Cancer Institute, and National Institute for Neurologic Disease and Stroke.</ACK>
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