Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders

用于表征和预测精神病和情绪障碍的动态成像基因组模型

基本信息

批准号：
10359205
负责人：
TULAY ADALI
金额：
$ 70.53万
依托单位：
GEORGIA STATE UNIVERSITY
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：
2019-05-25 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10359205
关键词：
3-Dimensional Address Algorithms Behavior Behavioral Benchmarking Biological Biological Markers Bipolar Disorder Brain Brain imaging Brain region Categories Clinical Communities Complex Consensus Data Data Set Dependence Diagnosis Diagnostic Diagnostic and Statistical Manual of Mental Disorders Dimensions Disease Environmental Risk Factor Evaluation Exhibits Functional Magnetic Resonance Imaging Future Genes Genetic Genetic Risk Genomics Goals Grouping Image Individual Joints Lead Link Major Depressive Disorder Maps Mental disorders Methods Modeling Mood Disorders Moods Noise Pathway interactions Patients Pattern Play Property Psychoses Research Personnel Role Sampling Schizoaffective Disorders Schizophrenia Signal Transduction Single Nucleotide Polymorphism Source Structure Subgroup Supervision Symptoms Syndrome Time Unipolar Depression Work base bipolar patients blind connectome data anonymization data fusion data repository deep learning diagnostic strategy disease classification flexibility genomic data genomic locus independent component analysis multidimensional data multimodal data multimodality neurobehavioral novel profiles in patients psychiatric genomics psychotic symptoms statistics tool user friendly software

项目摘要

Project Summary/Abstract Disorders of mood and psychosis such as schizophrenia, bipolar disorder, and unipolar depression are incredibly complex, influenced by both genetic and environmental factors, and the clinical characterizations are primarily based on symptoms rather than biological information. Current diagnostic approaches are based on symptoms, which overlap extensively in some cases, and there is growing consensus that we should approach mental illness as a continuum, rather than as a categorical entity. Since both genetic and environmental factors play a large role in mental illness, the combination of brain imaging and genomic data are poised to play an important role is clarifying our understanding of mental illness. However, both imaging and genomic data are high dimensional and include complex relationships that are poorly understood. To characterize the available information, we are in need of approaches that can deal with high-dimensional data exhibiting interactions at multiple levels (i.e., data fusion), while providing interpretable solutions (i.e., a focus on brain and genomic networks). An additional challenge exists because the available data has mixed temporal dimensionality, e.g., single nucleotide polymorphisms (SNPs) do not change over time, brain structure changes slowly over time, while fMRI changes rapidly over time. To address these challenges, we introduce a new unified framework called flexible subspace analysis (FSA) that can automatically identify subspaces (groupings of unimodal or multimodal components) in joint multimodal data. Our approach leverages the interpretability of source separation approaches and can include additional flexibility by allowing for a combination of shallow and ‘deep’ subspaces, thus leveraging the power of deep learning. We will apply the developed models to a large (N>60,000) dataset of individuals along the mood and psychosis spectrum to evaluate the important question of disease categorization. We will compute fully cross-validated genomic-neuro-behavioral profiles of individuals including a comparison of the predictive accuracy of 1) standard categories from the diagnostic and statistical manual of mental disorders (DSM), 2) data-driven subgroups, and 3) dimensional relationships. We will also evaluate the single subject predictive power of these profiles in independent data to maximize generalization. All methods and results will be shared with the community. The combination of advanced algorithmic approach plus the large N data promises to advance our understanding of the nosology of mood and psychosis disorders in addition to providing new tools that can be widely applied to other studies of complex disease.

项目概要/摘要情绪障碍和精神病，如精神分裂症、双相情感障碍和单相抑郁症极其复杂，受到遗传和环境因素的影响，临床特征是目前的诊断方法主要基于症状而不是生物学信息。症状，在某些情况下主要是重叠的，并且越来越多的人认为我们应该解决由于遗传和环境因素，精神疾病被视为一个连续体，而不是一个分类实体。在精神疾病中发挥着重要作用，脑成像和基因组数据的结合有望发挥作用重要的作用是澄清我们对精神疾病的理解然而，影像和基因组数据都是如此。高维度并包括人们知之甚少的复杂关系。信息，我们需要能够处理显示交互的高维数据的方法多个层次（即数据融合），同时提供可解释的解决方案（即关注大脑和基因组）网络）存在额外的挑战，因为可用数据具有混合时间维度，例如单一数据。核苷酸多态性 (SNP) 不会随时间变化，大脑结构随时间缓慢变化，而功能磁共振成像 (fMRI) 为了应对这些挑战，我们引入了一个称为“灵活”的新统一框架。子空间分析 (FSA)，可以自动识别子空间（单峰或多峰分组）我们的方法利用了源分离方法的可解释性。并且可以通过允许浅层和“深层”子空间的组合来包含额外的灵活性，因此我们将利用深度学习的力量将开发的模型应用于大型（N>60,000）数据集。我们对情绪和精神病谱系中的个体进行评估，以评估疾病分类的重要问题。将计算完全交叉验证的个体基因组神经行为特征，包括比较 1) 《精神障碍诊断和统计手册》中标准类别的预测准确性（DSM），2）数据驱动的子组，以及3）维度关系我们还将评估单个主题。这些概况在独立数据中的预测能力将最大化所有方法和结果。与社区共享先进的算法方法和大N数据的结合。除了提供新的知识之外，有望增进我们对情绪和精神病疾病的疾病分类学的理解可广泛应用于其他复杂疾病研究的工具。