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

22q11.2缺失综合征（22 q11 DS）与精神疾病风险增加相关 疾病，包括与特发性精神分裂症（SZ）患者症状相似的精神病，以及 约1-2%的特发性SZ病例具有22q11.2缺失。因此，有针对性的方法详细说明了具体的 22 q11 DS的脑功能障碍可能阐明精神病的关键神经机制。具体而言，方法 这些异常是精神病高危人群和具有精神病遗传风险的人群所共有的， 例如22 q11 DS，可能有助于解释精神病的风险和恢复力。轻微物理异常（MPA） 异常发育的表型异常。MPA包括细微的形态异常， 包括眼睛、耳朵、嘴和头部在内的许多身体部位的结构。的异常 面部和头部可能代表了早期胚胎发育的中断，包括嗅觉系统 和面部形态学，使这些有希望的切入点，了解神经发育 与22 q11 DS和精神病相关的神经病理学在这项研究中，我们试图比较：1） 嗅觉功能; 2）嗅觉系统的结构异常和3）面部的结构异常 在一个大型22 q11 DS患者队列（n=100）中，包括有和无精神病的患者， 个体发展（TD）。最后，我们将采用机器学习算法来选择最适合的特征， 区分22 q11 DS+和22 q11 DS-，并使用这些特征对具有特发性风险的个体进行分类， 精神病（PS）。此外，分析将利用机器学习的最新进展来预测突出的 与精神病的维度测量相关的特征。我们相信这种创新的方法可以 显着推进我们对精神病病因学的理解，并提供精确的进展， 精神病学中的医学通过多层次的分析，本创新性研究将提供一个 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.