title: Probabilistic approaches for atlasing normal and disease-specific brain variability.
abstract: The extreme variability in the structural conformation of the human brain poses significant challenges for the creation of population-based atlases. The ability to statistically and visually compare and contrast brain image data from multiple individuals is essential to understanding normal variability within a particular population as well as differentiating normal from diseased populations. This paper introduces the application of probabilistic atlases that describe specific subpopulations, measures their variability and characterizes the structural differences between them. Utilizing data from structural MRI, we have built atlases with defined coordinate systems creating a framework for mapping data from functional, histological and other studies of the same population. This paper describes the basic approach and a brief description of the underlying mathematical constructs that enable the calculation of probabilistic atlases and examples of their results from several different normal and diseased populations.
authors.affiliation: Reed Neurological Research Center, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095-1769, USA. toga@loni.ucla.edu
authors.forename: A W
authors.initials: AW
authors.lastname: Toga
authors.forename: P M
authors.initials: PM
authors.lastname: Thompson
authors.forename: M S
authors.initials: MS
authors.lastname: Mega
authors.forename: K L
authors.initials: KL
authors.lastname: Narr
authors.forename: R E
authors.initials: RE
authors.lastname: Blanton
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001927:Brain Diseases / Q000473:pathology* / Q000503:physiopathology*; D001931:Brain Mapping; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical