Csernansky John G, Wang Lei, Joshi Sarang C, Ratnanather J Tilak, Miller Michael I
Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA.
Neuroimage. 2004;23 Suppl 1:S56-68. doi: 10.1016/j.neuroimage.2004.07.025.
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.
本文回顾了计算解剖学（CA）的三个组成部分：（i）大变形映射的计算，即对于两个解剖结构的任何给定坐标系表示，计算从一个到另一个的微分同胚变换；（ii）解剖结构之间解剖变异的经验概率定律的计算；以及（iii）关于神经精神疾病状态的推断构建。CA利用从大变形映射获得的时空矢量场信息来评估解剖变异性，并促进对神经精神疾病患者脑结构异常的检测和量化。神经解剖结构分为两种类型：皮质下结构——由单个表面包围的灰质（GM）体积——和皮质套结构——大脑皮质套在白质（WM）和脑脊液（CSF）之间分层的解剖学上不同的部分。由于这两种类型结构的几何形状存在根本差异，基于图像的大变形高维脑图谱（HDBM-LD）和大变形微分同胚度量匹配（LDDMM）被开发用于研究皮质下结构，而标记皮质套距离映射（LCMDM）被开发用于研究皮质套结构。使用CA对神经精神疾病的研究通常需要在每组受试者数量相对较少的情况下检验假设的组间差异。提高检验此类假设的效力的方法包括量化个体结构形状、相关结构形状之间的关系（例如不对称性）以及形状随时间变化的方法。本文展示了采用这些方法对精神分裂症患者以及非常轻度至轻度阿尔茨海默病（AD）患者进行研究的有前景的初步研究。
