Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood d

精神病和情绪障碍的男性/女性差异：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: 10093861
• 负责人: TULAY ADALI
• 金额: $ 15.55万
• 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/10093861
• 关键词: 3-Dimensional; Address; Algorithms; Biological; Biological Markers; Bipolar Disorder; Brain; Brain imaging; Categories; Clinical; Cognitive; Communities; Complex; Consensus; Data; Data Set; Diagnosis; Diagnostic; Diagnostic and Statistical Manual of Mental Disorders; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Environmental Risk Factor; Exhibits; Female; Functional Magnetic Resonance Imaging; Genetic; Genomics; Goals; Grouping; Image; Individual; Joints; Measures; Mental disorders; Methods; Modeling; Mood Disorders; Moods; Play; Psychotic Disorders; Role; Schizophrenia; Services; Single Nucleotide Polymorphism; Source; Structure; Subgroup; Symptoms; Time; Unipolar Depression; base; data fusion; disease classification; flexibility; genomic data; male; meetings; multidimensional data; multimodal data; multimodality; neurobehavioral; novel; sex; task analysis; tool

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 in 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 subsumes existing models while providing important extensions. FSA 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 both linear and nonlinear (shallow and deep) subspaces. We will apply the developed models to a large (N~80,000) dataset including 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.

情绪障碍和精神障碍，如精神分裂症、双相情感障碍和单相情感障碍 抑郁症非常复杂，受遗传和环境因素的影响，而且 临床特征主要是基于症状而不是生物学上的。 信息。目前的诊断方法是基于症状的，这些症状有很大的重叠 在某些情况下，越来越多的人达成共识，认为我们应该将精神疾病视为一种 连续体，而不是作为一个绝对实体。因为遗传和环境因素都有 在精神疾病中起着很大的作用，大脑成像和基因组数据的结合是 准备在澄清我们对精神疾病的理解方面发挥重要作用。然而，两者 成像和基因组数据是高维的，包括复杂的关系， 人们对此知之甚少。为了描述可用的信息，我们需要的方法是 可以处理表现出多个层次上的交互的高维数据(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)。一个 由于可用数据具有混合的时间维度，例如， 单核苷酸多态(SNPs)不会随着时间的推移而改变，大脑结构会改变 随着时间的推移，fMRI变化很慢，而fMRI随着时间的推移变化很快。为了应对这些挑战，我们 引入一种称为柔性子空间分析(FSA)的新统一框架，该框架包含 现有的模型，同时提供重要的扩展。FSA可以自动识别子空间 (单峰或多峰分量分组)联合多峰数据。我们的方法 利用源分离方法的可解释性，并可包括其他 通过允许线性和非线性(浅和深)的组合来实现灵活性 子空间。我们将开发的模型应用于一个大型(N~80,000)数据集，包括 沿着心境和精神病谱来评价个体的重要问题 疾病分类。我们将计算完全交叉验证的基因组-神经行为图谱 包括1)标准类别的预测准确性的比较 精神障碍诊断和统计手册(DSM)，2)数据驱动的亚组，以及 3)维度关系。我们还将评估这些指标的单项预测能力。 独立数据中的配置文件，以最大限度地提高概括性。所有方法和结果都将共享 和社区在一起。先进的算法方法与大N数据相结合 承诺促进我们对情绪和精神障碍的病因学的理解 此外，还提供了可广泛应用于其他复杂疾病研究的新工具。