TY - JOUR
T1 - Computational anatomy and neuropsychiatric disease
T2 - Probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change
AU - Csernansky, John G.
AU - Wang, Lei
AU - Joshi, Sarang C.
AU - Tilak Ratnanather, J.
AU - Miller, Michael I.
N1 - Funding Information:
The authors acknowledge support from PHS grants MH56584 and MH60883, the Silvio Conte Center at Washington University School of Medicine (MH 62130), NIH PO1-CA47982, and the F. M. Kirby Research Center for Functional Brain Imaging (1 P41 RR15241-01A1).
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
KW - Computational anatomy
KW - Neuropsychiatric disease
KW - Neuropsychiatric disorders
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U2 - 10.1016/j.neuroimage.2004.07.025
DO - 10.1016/j.neuroimage.2004.07.025
M3 - Article
C2 - 15501101
AN - SCOPUS:7044231594
SN - 1053-8119
VL - 23
SP - S56-S68
JO - NeuroImage
JF - NeuroImage
IS - SUPPL. 1
ER -