Paul M. Thompson, Michael S. Mega, Jacob Moussai, Shahin Zohoori, Lu-Qing Xu, Amir Goldkorn, Aelia A. Khan, Jessica Coryell, Gary W. Small, Jeffrey Cummings and Arthur W. Toga
Laboratory of Neuro Imaging, Department of Neurology, Division of Brain Mapping, UCLA School of Medicine, Los Angeles, California 90095
A high-resolution probabilistic atlas of the Alzheimer's brain was constructed from a reference archive of MRI scans of patients with clinically-determined Alzheimer's Disease. MRI volumes were acquired from 10 subjects (5 males, mean age: 72 yrs.) as datasets of 170 contiguous T1-weighted 1-mm thick slices, and transformed into Talairach stereotaxic space. Connected systems of parametric meshes were used to model 29 different structures including major lobar, ventricular and cytoarchitectural boundaries in 3 dimensions. The ventricular system was also partitioned into a system of 14 connected surface elements whose junctions reflected cytoarchitectonic boundaries of the adjacent tissue. A family of surface maps was constructed, encoding statistical properties of local anatomical variation across the surfaces of individual structures. A complete system of probability density functions was computed, yielding confidence limits on surface variation within the Talairach stereotaxic grid. Local variability maps revealed striking directional trends in the patterns of local anatomic variation. Confidence limits on surface variation increased dramatically towards the posterior Sylvian and anterior cingulate cortex.
These probabilistic atlasing techniques provide a basis for the generation of anatomical templates and expert diagnostic systems which retain quantitative information on inter-subject and inter-group variations in brain architecture.
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