Integration of brain imaging and multi-omics data for improved diagnosis and prediction of mental disorders

整合脑成像和多组学数据以改进精神障碍的诊断和预测

• 批准号: 10398354
• 负责人: YU-PING WANG
• 金额: $ 58.94万
• 依托单位: TULANE UNIVERSITY OF LOUISIANA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10398354
• 关键词: Address; Behavioral; Biological; Biological Markers; Biology; Bipolar Disorder; Brain; Brain imaging; Classification; Clinical; Clinical Research; Cognitive; Cohort Studies; Communities; Complex; Computer software; Data; Data Set; Detection; Diagnosis; Disease; Functional Magnetic Resonance Imaging; Genes; Genetic; Genetic Markers; Genomics; Genotype; Goals; Image; Interdisciplinary Study; Joints; Knowledge; Learning; Link; Magnetic Resonance Imaging; Measurement; Medical; Mental disorders; Methods; Methylation; Modeling; Multimodal Imaging; Multiomic Data; Neurosciences; Outcome; Pathway interactions; Phenotype; Play; Productivity; Prognosis; Psyche structure; Psychiatrist; Psychiatry; Publishing; Reproducibility; Research; Research Personnel; Role; Schizophrenia; Site; Structure; Symptoms; Syndrome; Testing; Unipolar Depression; Work; accurate diagnosis; clinical phenotype; data integration; disease classification; disease diagnosis; endophenotype; genetic architecture; genomic data; imaging scientist; improved; insight; large datasets; multidisciplinary; multimodal data; multimodality; multiple data types; multiple omics; neuroimaging; novel; open source; personalized care; personalized diagnostics; personalized management; predict clinical outcome; public health relevance; response; risk variant; specific biomarkers; success; symptomatology; synergism; tool; web portal

Title: Integration of brain imaging and multi-omics data for improved diagnosis and prediction of mental disorders Project Summary An overarching goal of this project is to incorporate multiscale omics and brain imaging into clinical studies towards a nosology of psychiatric disorders that are biologically defined, and to uncover their specific genetic architectures. This will transform the current practice of mental disease diagnosis and prognosis, leading to precision psychiatry. Our recent work has demonstrated the value of combining multiscale brain imaging such as fMRI with knowledge of genomics, networks, and biology to detect risk genes and biomarkers. We also developed a set of integrative approaches for joint fMRI and genomics (e.g., SNPs) analysis. Despite our initial success, the following significant challenges remain: 1) how to uncover previously hidden (e.g., nonlinear) relationships among multiple data types for the detection of interaction networks both within and across omics, revealing the specific genetic architecture of psychiatric disorders; 2) how to incorporate phenotype-relevant multi-omics profiling to redefine and differentiate multiple psychiatric disorders with clinically overlapping symptoms such as schizophrenia (SZ), bipolar disorder (BI), and unipolar depression (UD); 3) how to link multi- omics and brain imaging data with phenotypical and cognitive measurements for the prediction of clinical outcomes or disease states (predictome); and 4) how to validate the detected biomarkers with newly collected large cohort studies by incorporating additional brain and omics data (e.g., DTI, methylations). In this proposal we will address the above remaining challenges in neurosciences and clinical psychiatry. We will continue to build on the productivity of our multidisciplinary research team comprising an image analyst and bioinformatician (Wang), an MRI imaging scientist (Calhoun), a geneticist and biostatistician (Deng), and a psychiatrist (Pearlson). To leverage our past success, we propose to accomplish the following specific aims: 1) to detect complex disease-specific non-linear relationships between multi-modal brain imaging and genomics data and further identify interaction networks both within and across omics levels; 2) to incorporate phenotype- specific network and structure information into our integration models for the detection of biomarkers and further validate them on large datasets for the classification of multiple mental disorders and their genetic make-ups; 3) to link multi-omics and brain imaging, including their interactions with behavioral and cognitive measurements, for the prediction of psychiatric disorders (predictome); and 4) to disseminate integrative multi-omics imaging analysis tools featuring non-linear analysis in open source software to the neuroimaging research community. These approaches will lead to better differentiation of mental disorders with overlapping symptomatology and more accurate prediction of clinical outcomes. This will lay the groundwork for personalized care of psychiatric disorders by targeting their specific genetic make-ups and evaluating the effects of medical treatments. By disseminating software to the research community, the project will have a broad and sustained impact.

标题：整合脑成像和多组学数据以改善诊断和预测 精神障碍 项目摘要 该项目的首要目标是将多尺度组学和脑成像纳入临床研究 对生物学定义的精神疾病进行疾病分类，并揭示其特定的遗传学特征。 建筑这将改变目前精神疾病诊断和预后的做法， 精准精神病学我们最近的工作已经证明了多尺度脑成像的价值， 作为功能磁共振成像，利用基因组学、网络和生物学知识来检测风险基因和生物标志物。我们也 开发了一套联合功能磁共振成像和基因组学的综合方法（例如，SNP）分析。尽管我们最初 成功之后，仍然存在以下重大挑战：1）如何发现先前隐藏的（例如，非线性） 用于检测组学内和组学间的交互网络的多种数据类型之间的关系， 揭示精神疾病的特定遗传结构; 2）如何将表型相关 多组学分析，以重新定义和区分临床重叠的多种精神疾病 精神分裂症（SZ）、双相情感障碍（BI）和单相抑郁症（UD）等症状; 3）如何将多个 具有表型和认知测量的组学和脑成像数据，用于预测临床 结果或疾病状态（预测组）;以及4）如何用新收集的生物标志物验证检测到的生物标志物 通过结合额外的脑和组学数据的大群组研究（例如，DTI，甲基化）。 在本提案中，我们将解决神经科学和临床精神病学中的上述剩余挑战。 我们将继续建立我们的多学科研究团队的生产力，包括图像分析师 和生物信息学家（王），MRI成像科学家（卡尔霍恩），遗传学家和生物统计学家（邓），以及 皮尔森（Pearlson）为善用过去的成功经验，我们建议达到以下具体目标： 检测多模态脑成像和基因组学之间复杂的疾病特异性非线性关系 数据并进一步识别组学水平内和组学水平之间的相互作用网络; 2）将表型- 将特定的网络和结构信息整合到我们的集成模型中，用于检测生物标志物， 在大型数据集上验证它们，以分类多种精神障碍及其遗传构成; 3） 将多组学和大脑成像联系起来，包括它们与行为和认知测量的相互作用， 用于预测精神疾病（predictome）;和4）传播综合多组学成像 分析工具，以开放源代码软件中的非线性分析为特色，提供给神经成像研究社区。 这些方法将导致更好地区分精神障碍与重叠的精神病学， 更准确地预测临床结果。这将为精神病患者的个性化护理奠定基础 通过针对其特定的遗传组成和评估药物治疗的效果来治疗疾病。通过 通过向研究界传播软件，该项目将产生广泛和持续的影响。