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

项目摘要/摘要 情绪障碍和精神障碍，如精神分裂症、双相情感障碍和单相抑郁 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 主要是基于症状而不是生物信息。当前的诊断方法基于 症状，在某些情况下有很大的重叠，人们越来越一致地认为，我们应该 精神疾病是一个连续体，而不是一个绝对的实体。因为遗传和环境因素都有 在精神疾病中扮演着重要的角色，大脑成像和基因组数据的结合有望发挥 重要的作用是澄清我们对精神疾病的理解。然而，成像和基因组数据都是 维度很高，包括人们很少了解的复杂关系。要描述可用的 信息，我们需要能够处理高维数据的方法，这些数据在 多层次(即数据融合)，同时提供可解释的解决方案(即，侧重于大脑和基因组 网络)。因为可用数据具有混合的时间维度，例如单一的时间维度，所以存在额外的挑战 核苷酸多态(SNPs)不会随着时间的推移而改变，大脑结构会随着时间的推移而缓慢变化，而fMRI 随着时间的推移变化很快。为了应对这些挑战，我们引入了一个新的统一框架，称为灵活 子空间分析(FSA)，可自动识别子空间(单峰或多峰分组 组件)在联合多模式数据中。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子空间和深子空间的组合来包括额外的灵活性，因此 利用深度学习的力量。我们将把开发的模型应用于(N&>；60,000)的大型数据集 个体沿着心境和精神病谱来评价疾病分类的重要问题。我们 将计算完全交叉验证的个体的基因组神经行为概况，包括比较 1)《精神障碍诊断和统计手册》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。我们还将评估单一科目 在独立数据中预测这些配置文件的能力，以最大限度地提高概括性。所有方法和结果都将 与社区共享。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，除了提供新的 可广泛应用于其他复杂疾病研究的工具。

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.