Cerebellar Contributions to Disease Course in Youth At High-Risk of Psychosis

描述(由申请人提供)：拟议研究的长期目标是描述精神病超高风险(UHR)个体的小脑-前额叶网络与症状严重性和病程之间的关系。UHR个体罹患I型轴精神障碍的风险要高得多，识别与症状学和病程相关的神经差异是开发精神病预测生物标记物的关键第一步。这样的生物标记物将为更有针对性的预防性治疗打开大门。虽然与纹状体功能相关的运动异常与转化为精神病有关，但我们也发现明显的证据表明，UHR患者的症状严重程度与小脑有关。虽然小脑在精神分裂症中已经得到了很好的研究，它的网络，特别是与前额叶皮质相关的网络，与精神分裂症中出现的功能障碍的认知障碍框架有关，但在UHR人群中，对它的研究相对较少。有一些证据表明，UHR组的小脑体积减少，精神分裂症患者的一级亲属的静息状态下小脑-皮质连接减少，但有关这一主题的文献一般很少。鉴于最近发现小脑功能障碍与症状严重程度之间的关系，以及小脑在精神分裂症和认知功能障碍中的作用，小脑网络是UHR人群研究的重要目标。在此，我们旨在1)研究两组在静息状态下功能性小脑-前额叶皮质网络的差异，以及2)UHR与健康对照组在大脑结构和小脑-前额叶皮质网络结构连通性方面的差异。至关重要的是，我们还将使用两年的纵向设计，就症状严重程度、认知功能和病程而言，研究这些网络(结构和功能)的完整性与这些网络中小脑和前额叶节点的体积之间的关系。vbl.使用 多模式神经成像我们将每年结合高分辨率解剖扫描收集静息状态连接MRI(FcMRI)和扩散张量成像(DTI)。此外，所有参与者将完成认知测试，以及临床评估，以量化UHR个人的症状严重程度和疾病进展。我将接受翻译研究以及DTI和结构解剖分析方法方面的关键培训。了解小脑-前额叶网络和精神病发展之间的关系对于全面了解精神分裂症的病因至关重要。这样做将有助于解释 小脑在精神分裂症中的作用及其病因。此外，这可能会促进 制定有针对性的干预措施，以改善高危人群的病程和治疗结果。

DESCRIPTION (provided by applicant): The long-term goal of the proposed study is to characterize the relationship between cerebello-prefrontal networks with respect to symptom severity and course of illness in individuals at ultra high-risk (UHR) for psychosis. UHR individuals are at much higher risk for the development of an Axis I psychotic disorder, and identifying neural differences associated with symptomatology and the course of illness is a key first step towards the development of predictive biomarkers for psychosis. Such biomarkers would open the door to more targeted preventative therapeutics. While movement abnormalities associated with striatal function are associated with the conversion to psychosis, we have also found evidence distinctly implicating the cerebellum in symptom severity in UHR individuals. While the cerebellum has been well studied in schizophrenia, and its networks, particularly networks associated with the prefrontal cortex, are implicated in the cognitive dysmetria framework for the dysfunction seen in schizophrenia, it has been relatively understudied in UHR populations. There is some evidence to indicate cerebellar volumetric decreases in UHR groups, and there is decreased resting state cerebello-cortical connectivity in first-degree relatives of schizophrenia patients, but the literature on this topic is generally sparse. Given ou recent finding of a relationship between cerebellar dysfunction and symptom severity, along with the contributions of the cerebellum to schizophrenia and cognitive dysmetria, cerebellar networks are an important target for research in UHR populations. Here, we aim to 1) investigate group differences in resting state functional cerebello-prefrontal cortical networks and 2) investigate group differences in brain structure and structural connectivity of cerebello-prefrontal cortical networks between UHR and healthy controls. Crucially, we will also investigate the relationship between the integrity of these networks (structural and functional), and the volume of cerebellar and prefrontal nodes in these networks, with respect to symptom severity, cognitive function, and the course of illness using a two year longitudinal design. Using multi-modal neuroimaging we will collect resting state connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) in conjunction with high-resolution anatomical scans annually. In addition, all participants will complete cognitive testing, along with clinical assessments to quantify symptom severity and disease progression in UHR individuals. I will receive key training in translational research and in both DTI and structural anatomical analysis methods. Knowledge of the relationships between cerebellar-prefrontal networks and the development of psychosis is crucial for gaining a complete picture of the etiology of schizophrenia. Doing so will help explain the role of the cerebellum in schizophrenia, and its etiology. Furthermore, this may facilitate the development of targeted interventions that may improve disease course and treatment outcomes in at-risk populations.