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

• 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
• 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

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.

项目摘要/摘要 情绪和精神病的疾病，例如精神分裂症，躁郁症和单极抑郁症是 令人难以置信的复杂，受遗传和环境因素的影响，临床特征是 当前的诊断方法是基于 症状，在某些情况下会广泛重叠，并且我们应达成的共识越来越多 精神疾病是一种连续的，而不是分类实体。由于遗传和环境因素 在精神疾病中起着重要作用，脑成像和基因组数据的结合被中毒以发挥作用 重要的作用是澄清我们对精神疾病的理解。但是，成像和基因组数据都是 高维，并包含不理解的复杂关系。表征可用的 信息，我们需要使用可以处理高维数据的方法 多个级别（即数据融合），同时提供可解释的解决方案（即，重点是大脑和基因组 网络）。存在另一个挑战，因为可用数据具有混合的临时维度，例如单个 核苷酸多态性（SNP）不会随着时间的流逝而变化，大脑结构随着时间的流逝而缓慢变化，而fMRI 随着时间的流逝，变化迅速。为了应对这些挑战，我们引入了一个名为Flexible的新的统一框架 可以自动识别子空间的子空间分析（FSA）（非模态或多模式的分组 组件）在联合多模式数据中。我们的方法利用了来源分离方法的解释性 并可以通过允许浅层和“深”子空间组合来包括额外的灵活性，从而包括 利用深度学习的力量。我们将将开发的模型应用于大型（n> 60,000）数据集 沿着情绪和精神病的个体评估疾病类别的重要问题。我们 将计算个体的完全交叉验证的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的标准类别的预测精度 （DSM），2）数据驱动的亚组和3）维度关系。我们还将评估单一主题 这些概况在独立数据中的预测能力以最大化概括。所有方法和结果将 与社区共享。高级算法方法以及大N数据的组合 有望提高我们对情绪和精神病障碍的肿瘤的理解，此外还提供了新 可以广泛应用于其他复杂疾病研究的工具。