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

22q11 缺失综合征精神病发育风险的嗅觉和面部标记

基本信息

批准号：
10217017
负责人：
DAVID R ROALF
金额：
$ 69.58万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-24 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10217017
关键词：
22q11.2 3-Dimensional Acoustic Rhinometry Behavioral Biometry Body part Brain region Characteristics Classification Clinical Craniofacial Abnormalities Data Development DiGeorge Syndrome Diffuse Dimensions Distress Dysmorphology Ear Etiology Event Exhibits Eye Face Functional disorder Genetic Diseases Genetic Risk Geometry Head Human Image Impaired cognition Impairment Individual Lead Machine Learning Magnetic Resonance Imaging Mandible Measures Medial Mental disorders Minor Modeling Morphology Nasal cavity Neurobiology Nose Odors Olfactory Cortex Olfactory Pathways Oral cavity Patients Performance Peripheral Phenotype Process Psychiatry Psychophysics Psychoses Psychotic Disorders Public Health Research Risk Sampling Schizophrenia Severities Smell Perception Societies Structural defect Structure Symptoms Syndrome Techniques Temporal Lobe Testing Three-Dimensional Imaging Work Youth associated symptom base behavior measurement brain dysfunction clinically relevant cohort cost effective therapy functional outcomes genetic disorder diagnosis high risk improved innovation machine learning algorithm microdeletion multilevel analysis neurodevelopment neuromechanism neuropathology olfactory bulb olfactory sulcus precision medicine psychotic symptoms resilience trait

项目摘要

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.

项目摘要：22q11.2 缺失综合征 (22q11DS) 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 病例有 22q11.2 缺失。因此，有针对性的方法详细说明了具体 22q11DS 的脑功能障碍可能阐明精神病的关键神经机制。具体来说，方法捕获有精神病风险和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恢复力。轻微身体异常 (MPA) 是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、嘴巴和头部等许多身体部位的结构。的异常情况面部和头部可能代表早期胚胎发育的破坏，包括嗅觉系统和面部形态学，为理解神经发育提供了这些有希望的切入点与 22q11DS 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) 的测量嗅觉功能； 2) 嗅觉系统结构异常和 3) 面部结构异常在一大群 22q11DS 患者 (n=100) 中，包括患有和不患有精神病的患者，通常发展中（TD）个体。最后，我们将采用机器学习算法来选择最适合的特征区分 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显着的与精神病维度测量相关的特征。我们相信这种创新方法可以显着增进我们对精神病病因的理解，并提高精确度精神病学医学。通过所提出的多层次分析，这项创新研究将提供我们对精神病神经发育基础的理解取得了实质性进展。