A Modular Framework for Data-Driven Neurogenetics to Predict Complex and Multidimensional Autistic Phenotypes

数据驱动神经遗传学预测复杂和多维自闭症表型的模块化框架

基本信息

批准号：
10826595
负责人：
Archana Venkataraman
金额：
$ 46.95万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10826595
关键词：
Affect Algorithms Anxiety Architecture Attention Attention deficit hyperactivity disorder Awareness Beds Biological Biological Markers Biological Process Brain Brain region Child Clinical Collection Communities Complex Computational algorithm Computer software Copy Number Polymorphism Data Dependence Diagnostic Diffusion Magnetic Resonance Imaging Dimensions Disease Etiology Formulation Functional Magnetic Resonance Imaging Genes Genetic Genetic Markers Genetic Risk Genetic Variation Genomics Goals Graph Image Impaired cognition Impairment Intervention Learning Linear Models Link Magnetic Resonance Imaging Maps Mental Depression Methods Modality Modeling Multimodal Imaging Multiomic Data Mutation National Human Genome Research Institute Neurologic Ontology Pathway interactions Patients Phenotype Procedures Process Published Comment Research Research Personnel Resources Rest Sex Differences Single Nucleotide Polymorphism Structure Symptoms Testing United States Universities Validation Variant Virginia Visualization Work analytical tool autism spectrum disorder autistic autoencoder base behavioral impairment biological systems biomarker discovery clinical heterogeneity clinical phenotype clinically relevant comorbidity cost data framework deep learning model deep neural network feature selection flexibility genetic architecture genetic variant genome-wide graph neural network imaging genetics innovation insight instrument lens magnetic resonance imaging biomarker model development multidimensional data multimodal data multimodal neuroimaging multimodality neural neural network architecture neurogenetics neuroimaging neuroimaging marker neuropsychiatric disorder neuropsychiatry next generation novel novel strategies novel therapeutics open source tool operation personalized intervention personalized therapeutic phenomenological models social synergism tool user-friendly

Affect Algorithms Anxiety Architecture Attention Attention deficit hyperactivity disorder Awareness Beds Biological Biological Markers Biological Process Brain Brain region Child Clinical Collection Communities Complex Computational algorithm Computer software Copy Number Polymorphism Data Dependence Diagnostic Diffusion Magnetic Resonance Imaging Dimensions Disease Etiology Formulation Functional Magnetic Resonance Imaging Genes Genetic Genetic Markers Genetic Risk Genetic Variation Genomics Goals Graph Image Impaired cognition Impairment Intervention Learning Linear Models Link Magnetic Resonance Imaging Maps Mental Depression Methods Modality Modeling Multimodal Imaging Multiomic Data Mutation National Human Genome Research Institute Neurologic Ontology Pathway interactions Patients Phenotype Procedures Process Published Comment Research Research Personnel Resources Rest Sex Differences Single Nucleotide Polymorphism Structure Symptoms Testing United States Universities Validation Variant Virginia Visualization Work analytical tool autism spectrum disorder autistic autoencoder base behavioral impairment biological systems biomarker discovery clinical heterogeneity clinical phenotype clinically relevant comorbidity cost data framework deep learning model deep neural network feature selection flexibility genetic architecture genetic variant genome-wide graph neural network imaging genetics innovation insight instrument lens magnetic resonance imaging biomarker model development multidimensional data multimodal data multimodal neuroimaging multimodality neural neural network architecture neurogenetics neuroimaging neuroimaging marker neuropsychiatric disorder neuropsychiatry next generation novel novel strategies novel therapeutics open source tool operation personalized intervention personalized therapeutic phenomenological models social synergism tool user-friendly

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项目摘要

Project Summary/Abstract Autism Spectrum Disorder (ASD) can be viewed through three complementary lenses: neurologically, it is linked to distributed changes in brain structure, function, and connectivity; biologically, it is associated with genome-wide mutations across multiple pathways; and clinically, it manifests as a diverse spectrum of behavioral and cogni- tive impairments. Despite this richness, treatment options for ASD are based on coarse diagnostics and target a few speciﬁc symptoms, such as social awareness, irritability, and depression. As a result, they have varied, and often limited, efﬁcacy across patients. Taken together, next-generation therapeutics for ASD will crucially depend on our ability to bridge its neurological, biological, and clinical viewpoints for personalized intervention. Imaging-genetics is an emerging ﬁeld that attempts to link neuroimaging features with genetic variants. How- ever, most methods focus on a restricted set of biomarkers, and they do not account for clinical phenotype, both of which provide an incomplete picture of the impacted processes. Our long-term goal is to develop a modu- lar platform that fuses multimodal neuroimaging and multi-omics data to unravel the complex etiology of ASD. The overall objective of this proposal is to develop and validate interpretable deep learning models to combine genome-wide variants with whole-brain structural and functional MRI data. Our innovative strategy is to use biologically-informed neural network architectures to project each of the modalities to a shared latent space that is simultaneously predictive of patient-level clinical phenotype. This unique formulation can easily accommodate missing data modalities, thus maximally utilizing all of the available information. We will devise, implement, vali- date, and disseminate our model via four speciﬁc aims. In Aim 1 we will develop a graph neural network, whose connections mimic a well-known gene ontology. Hierarchical pooling operations will capture the information ﬂow through the network, while an attention layer will learn the discriminative biological pathways associated with the phenotype. In Aim 2 we will develop and integrate a Bayesian feature selection procedure to identify ROI-based neuroimaging biomarkers and a matrix autoencoder to extract discriminative functional subnetworks from brain connectivity data. In Aim 3 we will use the fused imaging and genetic architectures to uncover the neural and biological bases underlying the observed clinical heterogeneity of ASD. Finally, in Aim 4 we will package and dis- seminate our model as a user-friendly tool for the broader research community. We anticipate the proposed work will have a transformative impact on ASD research by allowing us to develop reﬁned diagnostic instruments and on the ﬁeld of imaging-genetics by providing a new approach for multimodal biomarker discovery.

项目摘要/摘要自闭症谱系障碍（ASD）可以通过三个完整的镜头观看：神经学上，它是链接的大脑结构，功能和连通性的分布变化；在生物学上，它与全基因组有关跨多个途径的突变；在临床上，它表现为行为和认知的不同范围损害。尽管如此，ASD的治疗选择仍基于粗糙的诊断和目标一些特定的符号，例如社会意识，易怒和沮丧。结果，它们变化了，并且通常有限，患者的效率。两者合计，对ASD的下一代疗法将至关重要取决于我们桥接其神经，生物学和临床观点以进行个性化干预的能力。成像遗传学是一种新兴领域，试图将神经影像特征与遗传变异联系起来。如何- 有史以来，大多数方法都集中在一组受限制的生物标志物上，并且它们不考虑临床表型，两者都其中提供了不完整的影响过程。我们的长期目标是开发一个模型融合多模式神经影像学和多媒体数据的LAR平台以揭示ASD的复杂病因。该建议的总体目的是开发和验证可解释的深度学习模型以结合具有全脑结构和功能性MRI数据的全基因组变体。我们的创新策略是使用生物知识的神经网络体系结构将每种模式都投射到共享的潜在空间仅预测患者级临床表型。这个独特的公式可以轻松容纳缺少数据模式，因此最大程度地利用了所有可用信息。我们将设计，实施，vali- 日期，并通过四个特定目标传播我们的模型。在AIM 1中，我们将开发一个图形神经网络，谁连接模仿众所周知的基因本体。分层池操作将捕获信息流通过网络，注意力层将学习与表型。在AIM 2中，我们将开发和集成一个贝叶斯特征选择程序，以识别基于ROI的基于ROI 神经成像生物标志物和矩阵自动编码器从脑中提取歧视功能子网连接数据。在AIM 3中，我们将使用融合的成像和遗传体系结构来揭示神经和观察到的ASD临床异质性的基础生物基础。最后，在AIM 4中，我们将包装并脱颖而出我们的模型将我们的模型作为更广泛的研究社区的用户友好工具。我们期待拟议的工作通过允许我们开发重定的诊断工具，将对ASD研究产生变革性的影响并通过为多模式生物标志物发现的新方法提供成像基因的领域。