Neurodevelopmental variation of intrinsic functional connectivity and its relationship to psychosis risk and gene expression

项目总结/摘要 更好地了解精神病如何在儿童和青少年时期出现将有助于 找出病因和治疗目标，以便及早发现和介入治疗。主要 本K 01的目标是：1）确定青年人静息状态功能连接的年龄相关变化， 这种变化与精神病谱症状的关系，2）确定这些精神病在多大程度上- 相关的特征存在于临床高危的寻求帮助的青少年中，以发展精神病和3） 确定遗传因素，有助于典型和非典型的神经发育的静息态内在 功能性连接。目标1将联合收割机档案静息态功能磁共振成像 来自费城神经发育队列（PNC，N=907）的rsfMRI数据和高度兼容的 规范性发展的纵向研究（Luna队列，N=223）。将计算图论度量 从rsfMRI数据，并用于确定精神病谱青年偏离的程度， 典型的发展。对于目标2，我将收集10岁以上青年（N=40）和典型发育中的rsfMRI数据， 对照组（N=50），纵向，目标是确定在多大程度上年龄相关的变化之间- 和网络内的连通性存在于青少年中。精神病阳性症状，心理社会 将在基线评估和随访时收集功能和神经认知指标 评估。我将探索内在功能连接如何预测精神病症状的增加， 功能和/或认知。在目标3中，来自PNC和Luna队列的遗传信息将 用于确定精神分裂症风险和神经发育的预期基因表达谱 基因与功能间和功能内连接的发展有关。我的训练计划将 专注于1）整合基因组和神经影像学数据，以了解精神疾病的发展 2）对高维数据集进行分析，以识别疾病的机制和潜在风险 精神病的因素，以及3）获得发育神经科学的专业知识，并将这些知识应用于 精神病的神经发育模型。这项研究的结果将有助于我们确定机械过程 大脑发育和功能，并确定在多大程度上与年龄相关的网络连接的变化， 在精神病发作前就已经存在了通过培训计划，我将成为一名专家 在建模高维数据和识别青春期大脑的变化方面，我将用它来 改善精神病的预测，并确定干预的关键时间段。

Project Summary/Abstract Gaining a better understanding of how psychosis emerges during childhood and adolescence will help us identify causes of the illness and treatment targets to facilitate early detection and intervention. The main goals of this K01 are to 1) identify age-associated variation in resting-state functional connectivity in youth, and how that variation relates to psychosis spectrum symptoms, 2) determine to what extent these psychosis- related features are present in help-seeking youth at clinical high risk (CHR) for developing psychosis and 3) identify genetic factors that contribute to typical and atypical neurodevelopment of resting-state intrinsic functional connectivity. Aim 1 will combine archival resting-state functional magnetic resonance imaging (rsfMRI) data from the Philadelphia Neurodevelopmental Cohort (PNC, N=907) and a highly compatible longitudinal study of normative development (Luna cohort, N=223). Graph theory measures will be calculated from the rsfMRI data and be used to determine the extent to which psychosis spectrum youth deviate from typical development. For Aim 2, I will collect rsfMRI data on CHR youth (N=40) and typically developing controls (N=50), longitudinally, with a goal to determine to what extent age-associated alterations in between- and within- network connectivity are present in CHR youth. Positive symptoms of psychosis, psychosocial functioning, and neurocognitive measures will be collected at the baseline assessment and follow-up assessments. I will explore how intrinsic functional connectivity predicts increases in psychotic symptoms, functioning and/or cognition in this cohort. In Aim 3, genetic information from the PNC and Luna cohort will used to determine how expected gene expression profiles of schizophrenia risk and neurodevelopmental genes are associated with development of between- and within- functional connectivity. My training plan will focus on 1) integrating genomic and neuroimaging data to understand the development of psychiatric disorders, 2) conducting analyses of high dimensional data sets to identify mechanisms of and potential risk factors for psychosis, and 3) acquiring expertise in developmental neuroscience and apply this knowledge to neurodevelopmental models of psychosis. Results from this study will help us identify mechanistic processes of brain development and function and identify to what extent age-associated changes in network-connectivity are intact or already present prior to the onset of psychosis. Through the training plan, I will become an expert in modeling high dimensional data and identifying changes in the brain during adolescence, which I will use to improve the prediction of psychosis and identify critical time periods for intervention.