Default mode connectivity in typical development & the schizophrenia prodrome

描述（由申请人提供）：大多数精神分裂症患者在发病前都有前驱症状。前驱期的脑完整性测量可能反映了精神分裂症的易感性。然而，精神分裂症中神经发育改变的时间仍然未知。虽然围产期脑损伤与精神分裂症的发病机制有关，但青春期也引起了人们的兴趣，因为正是在这个发育窗口期，临床症状通常会出现。一些理论认为，青春期神经发育异常是发展为全面精神分裂症的基础。然而，很少有研究关注大规模神经网络的连通性，或直接比较健康青少年和精神分裂症临床高风险青少年（CHR）的大脑发育轨迹。甚至更少的研究考察了环境因素，如物质使用对CHR个体大脑发育的影响。特别是，大麻使用已被确定为精神分裂症病因学中的潜在环境风险因素，这引发了关于大麻暴露如何与与CHR表型相关的大脑改变相互作用的问题。对大脑网络连通性的分析可能特别适合于解决与CHR相关的神经发育轨迹问题，以及大麻暴露对精神病风险的潜在相互作用。“默认模式网络”（DMN）是一种神经网络，与认知任务条件相比，它在休息时显示出更强的fMRI活动。先前的精神分裂症研究报告精神病症状与DMN的超连通性有关，扩散张量成像（DTI）分析发现连接DMN区域的纤维通路的微观结构一致性较差。总之，这些发现表明，精神分裂症患者DMN连通性的结构和功能方面都发生了改变，支持了对CHR个体DMN发育轨迹的关注。此外，由于DMN在没有任务表现的情况下是可检测的，因此它很好地用于评估发育临床研究中的脑行为关系，在这些研究中，组间认知任务表现的巨大差异可能会使传统的基于任务的fMRI研究的解释复杂化。该研究将结合静息状态fMRI和DTI分析，以测量DMN连接的功能和结构方面，并重点研究CHR和健康对照受试者从青春期到成年期DMN的发展轨迹。此外，我们将研究DMN功能和结构完整性如何与CHR青年的大麻使用有关。最后，我们将研究DMN连接是否与症状严重程度有关，并预测转化为精神病。考虑到与精神分裂症谱系相关的广泛认知影响，神经系统的视角可能特别有利于阐明易感个体精神病进展背后的大脑改变。

DESCRIPTION (provided by applicant): Most patients with schizophrenia experience a prodrome, prior to the onset of frank illness. Measures of brain integrity during this prodromal period may reflect schizophrenia vulnerability. However, the timing of neuro- developmental alterations in schizophrenia remains unknown. Although perinatal brain insults have been implicated in the pathogenesis of schizophrenia, adolescence has also been of interest because it is during this developmental window that clinical symptoms typically arise. Several theories pose that abnormal adolescent neuromaturation underlies progression to full-blown schizophrenia. However, few studies have looked at connectivity of large-scale neural networks, or directly compared the trajectories of brain development between healthy adolescents and those at clinical high risk for developing schizophrenia (CHR). Even less research has examined the role of environmental factors such as substance use on brain development in CHR individuals. In particular, cannabis use has been identified as a potential environmental risk factor in the etiology of schizophrenia, motivating questions about how cannabis exposure might interact with brain alterations associated with the CHR phenotype. Analysis of brain network connectivity may be particularly well-suited to addressing questions concerning the neurodevelopmental trajectory associated with CHR, as well as the potential interaction of cannabis exposure on psychosis risk. The "default-mode network" (DMN) is a neural network that shows increased fMRI activity at rest, compared to cognitive task conditions. Prior studies of schizophrenia report associations of psychotic symptoms with hyperconnectivity of the DMN, and diffusion tensor imaging (DTI) analyses have detected poorer microstructural coherence of the fiber pathways connecting DMN regions. Together, these findings suggest that both structural and functional aspects of DMN connectivity are altered in schizophrenia, supporting a focus on the DMN's developmental trajectory in CHR individuals. Moreover, because the DMN is detectable in the absence of task performance, it is well-poised to evaluate brain-behavior relationships in developmental clinical studies, in which sizeable between-group cognitive task performance differences can complicate interpretation of traditional task-based fMRI studies. The proposed study will combine analyses of resting state fMRI with DTI in order to measure both functional and structural aspects of DMN connectivity, with a focus on examining the DMN's developmental trajectory from adolescence to adulthood in CHR and healthy comparison subjects. Further, we will examine how DMN functional and structural integrity relates to cannabis use in CHR youth. Lastly, we will examine whether DMN connectivity relates to symptom severity and predicts conversion to psychosis. Given the wide range of cognitive effects associated with the schizophrenia spectrum, a neural systems perspective may be particularly profitable in shedding light on the brain alterations underlying progression to psychosis in vulnerable individuals. PUBLIC HEALTH RELEVANCE: This study aims to identify biomarkers of vulnerability to schizophrenia that may promote the development of early interventions to prevent or attenuate psychotic symptoms and improve functional outcome in the lives of affected individuals.