title: Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change.
abstract: Three components of computational anatomy (CA) are reviewed in this paper: (i) the computation of large-deformation maps, that is, for any given coordinate system representations of two anatomies, computing the diffeomorphic transformation from one to the other; (ii) the computation of empirical probability laws of anatomical variation between anatomies; and (iii) the construction of inferences regarding neuropsychiatric disease states. CA utilizes spatial-temporal vector field information obtained from large-deformation maps to assess anatomical variabilities and facilitate the detection and quantification of abnormalities of brain structure in subjects with neuropsychiatric disorders. Neuroanatomical structures are divided into two types: subcortical structures-gray matter (GM) volumes enclosed by a single surface-and cortical mantle structures-anatomically distinct portions of the cerebral cortical mantle layered between the white matter (WM) and cerebrospinal fluid (CSF). Because of fundamental differences in the geometry of these two types of structures, image-based large-deformation high-dimensional brain mapping (HDBM-LD) and large-deformation diffeomorphic metric matching (LDDMM) were developed for the study of subcortical structures and labeled cortical mantle distance mapping (LCMDM) was developed for the study of cortical mantle structures. Studies of neuropsychiatric disorders using CA usually require the testing of hypothesized group differences with relatively small numbers of subjects per group. Approaches that increase the power for testing such hypotheses include methods to quantify the shapes of individual structures, relationships between the shapes of related structures (e.g., asymmetry), and changes of shapes over time. Promising preliminary studies employing these approaches to studies of subjects with schizophrenia and very mild to mild Alzheimer's disease (AD) are presented.
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA. jgc@conte.wustl.edu
authors.forename: John G
authors.initials: JG
authors.lastname: Csernansky
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.forename: Sarang C
authors.initials: SC
authors.lastname: Joshi
authors.forename: J Tilak
authors.initials: JT
authors.lastname: Ratnanather
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000473:pathology; D019295:Computational Biology; D007839:Functional Laterality / Q000502:physiology*; D006801:Humans; D001523:Mental Disorders / Q000473:pathology*; D015233:Models, Statistical; D009422:Nervous System Diseases / Q000473:pathology*; D012559:Schizophrenia / Q000473:pathology; D013997:Time Factors