Computational Morphometry in Schizophrenia and Related Disorders

描述（由申请人提供）：此R01申请资助五年，用于开发，评估和应用新型计算工具，以了解与精神分裂症相关的神经解剖结构的形态测量变化。形状测量对精神分裂症很有意义，因为这种疾病被一些人视为起源于神经发育，因为有证据表明，在大脑的形态发生过程中，异常的压力和/或组织形成可能会改变大脑结构的形状，特别是大脑中线的结构，并影响新皮层的折叠模式。我们相信计算形态测量工具对于准确表征和量化形状变化至关重要。事实上，神经科学研究作为一个整体，对计算机辅助形状研究越来越感兴趣，包括但不限于正常神经发育、阿尔茨海默病、精神分裂症和分裂型人格障碍（SPD）、双相情感障碍、精神病性情感障碍和胎儿酒精暴露。因此，我们的主要目标是进一步开发新的图像分析技术，以实现种群之间形状差异的检测和定位。我们将把这项新技术应用于首发精神分裂症患者与首发情感性受试者（主要是躁狂）和正常对照的选定脑结构。我们的第二个目标是定量地评估和比较当前最先进的形状分析工具，包括我们的，因为很少有算法得到验证。因此，将创建具有已知形状修改的合成数据集以用于控制实验。此外，我们将在真实数据上评估所有形状技术，这些数据具有先前观察到的形状变化（即SPDs的尾状核和精神分裂症患者的杏仁核-海马）。最后，为了促进开放科学，我们将向科学界公开所有结果、数据、参数和算法。通过表征和描述精神分裂症的形状异常，我们将进一步了解脑形态测量异常在精神分裂症中的作用，精神分裂症是一种主要的公共卫生问题，影响着近1%的普通人群。

DESCRIPTION (provided by applicant): This R01 application is for five years of funding to develop, evaluate and apply novel computational tools for the purpose of understanding morphometric changes in neuroanatomical structures related to schizophrenia. Shape measures are of interest in schizophrenia because this disorder is viewed by some as a neurodevelopmental in origin and because there is evidence to suggest that during morphogenesis of the brain, abnormal pressures and/or tissue formations likely change the shape of brain structures, particularly those in the midline of the brain, as well as influencing folding patterns of the neocortex. We believe that computational morphometry tools are critical to characterize and to quantify shape changes accurately. In fact, neuroscience research as a whole has shown a growing interest in computer assisted shape studies for numerous conditions including, but not limited to, normal neurodevelopment, Alzheimer's disease, schizophrenia and schizotypal personality disorder (SPD), bipolar disorder, psychotic affective disorder, and fetal alcohol exposure. Our primary objective is thus to develop further new image analysis techniques to enable the detection and localization of shape differences between populations. We will apply this new technology to selected brain structures in first episode schizophrenic subjects compared to first episode affective subjects (mainly manic), and normal controls. Our secondary objective is to quantitatively evaluate and compare current state-of-the-art shape analysis tools, including ours, as few algorithms have been validated. Accordingly, a synthetic data set with known shape modifications will be created to use in a control experiment. In addition, we will evaluate all shape techniques on real data with previously observed shape changes (i.e., caudate nucleus in SPDs and amygdala-hippocampus in patients with schizophrenia). Finally, in an effort to promote open science, we will make all results, data, parameters and algorithms publicly available to the scientific community. By characterizing and delineating shape abnormalities in schizophrenia, we will understand further the role of brain morphometry abnormalities in schizophrenia, a disorder that is a major public health problem, affecting close to 1% of the general population.