Olfactory and facial markers of developmental risk for psychosis in 22q11 deletion syndrome

项目摘要：22q11.2缺失综合征(22q11DS)与精神病风险增加相关 精神障碍，包括症状与特发性精神分裂症(SZ)患者相似的精神病，以及 约1-2%的特发性SZ患者存在22q11.2缺失。因此，有针对性的方法详细说明了具体的 22q11DS中的脑功能障碍可能阐明精神病的关键神经机制。具体地说，方法 它捕捉了精神病高危个体和具有精神病遗传风险的个体常见的异常， 例如22q11DS，可能有助于解释精神病的风险和韧性。轻微物理异常(MPA)包括 发育异常的表型异常。MPA包括细微的形态异常 包括许多身体部位的结构，包括眼睛、耳朵、嘴巴和头部。脑血管畸形 面部和头部可能代表着早期胚胎发育的中断，包括嗅觉系统。 和面部形态，为理解神经发育提供了这些有希望的切入点 在这项研究中，我们试图比较1)与22q11ds和精神病相关的神经病理学指标 嗅觉功能；2)嗅觉系统结构异常；3)面部结构异常 在一大群22q11DS患者(n=100)中，包括有精神病和没有精神病的患者，到典型的 发展中的(TD)个人。最后，我们将使用机器学习算法来选择最好的特征 区分22q11DS+和22q11DS-并使用这些特征对具有特发性风险的个人进行分类 精神病(PS)。此外，分析将利用机器学习的最新进展来预测显著 与精神错乱的维度测量相关的特征。我们相信这种创新的方法可以 极大地提高了我们对精神病病因的理解，并提供了精确的进展 精神病学的医学。通过拟议的多层次分析，这项创新研究将提供 我们对精神病的神经发育基础的理解有了实质性的进展。

Project Summary: 22q11.2 deletion syndrome (22q11DS) is associated with an increased risk for psychiatric disorders, including psychosis with similar symptoms to individuals with idiopathic schizophrenia (SZ), and about 1-2% of cases of idiopathic SZ have 22q11.2 deletions. Thus, targeted approaches detailing specific brain dysfunction in 22q11DS may elucidate critical neural mechanisms in psychosis. Specifically, approaches that capture abnormalities common to individuals at-risk for psychosis and with a genetic risk to psychosis, such as 22q11DS, may help explain risk and resilience for psychosis. Minor physical anomalies (MPAs) are phenotypic abnormalities of aberrant development. MPAs include subtle abnormalities of morphological structures encompassing numerous body parts including eyes, ears, mouth and head. Abnormalities of the face and head likely represent a disruption of early embryologic development, including the olfactory system and facial morphology, making these promising entry points for understanding neurodevelopmental neuropathology associated with 22q11DS and psychosis In this study, we seek to compare 1) measures of olfactory function; 2) structural abnormalities of the olfactory system and 3) structural abnormalities of the face in a large cohort of patients with 22q11DS (n=100), including those with and without psychosis, to typically developing (TD) individual. Finally, we will employ machine learning algorithms to select features that best differentiate 22q11DS+ from 22q11DS- and use those features to classify individual with idiopathic risk for psychosis (PS). In addition, analyses will leverage recent advances in machine learning to predict salient features associated with dimensional measures of psychosis. We believe this innovative approach can significantly advance our understanding of the etiology of psychosis and provide advances to precision medicine in psychiatry. Through the proposed multi-level analysis, this innovative research will provide a substantial advance in our understanding of the neurodevelopmental substrates of psychosis.