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: 10559628
• 负责人: TULAY ADALI
• 金额: $ 67.57万
• 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 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559628
• 关键词: Address; Algorithms; Behavior; Behavioral; Benchmarking; Biological; Biological Markers; Bipolar Disorder; Brain; Brain imaging; Brain region; Categories; Classification; Clinical; Communities; Complex; Consensus; Data; Data Aggregation; 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; 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; Symptoms; Syndrome; Time; Unipolar Depression; Work; bipolar patients; blind; connectome; data anonymization; data fusion; data reduction; 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.

项目总结/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， 非常复杂，受遗传和环境因素的影响，临床特征是 主要基于症状而不是生物学信息。目前的诊断方法是基于 症状，在某些情况下广泛重叠，并且越来越多的共识认为我们应该接近 精神疾病是一个连续体，而不是一个分类实体。由于遗传和环境因素 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将在精神疾病中发挥重要作用。 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 且包括知之甚少复杂关系。为了描述可用的 信息，我们需要的方法，可以处理高维数据显示的相互作用， 多级（即，数据融合），同时提供可解释的解决方案（即，专注于大脑和基因组 网络）。存在额外的挑战，因为可用数据具有混合的时间维度，例如，单个 核苷酸多态性（SNP）不随时间变化，大脑结构随时间缓慢变化，而fMRI 随着时间的推移而迅速变化。为了应对这些挑战，我们引入了一个新的统一框架， 子空间分析（FSA），可以自动识别子空间（单峰或多峰的分组 在联合多模态数据中。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和“深”子空间的组合而包括额外的灵活性，因此 利用深度学习的力量。我们将开发的模型应用于一个大型（N> 60，000）数据集， 个体沿着情绪和精神病谱来评估疾病分类的重要问题。我们 将计算完全交叉验证的基因组神经行为个人档案，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 这些配置文件在独立数据中的预测能力，以最大限度地推广。所有方法和结果将 与社区分享。结合先进的算法方法和大N数据 承诺推进我们的情绪和精神疾病的疾病分类学的理解，除了提供新的 这些工具可以广泛应用于其他复杂疾病的研究。

项目成果

Objective sleep measures in chronic fatigue syndrome patients: A systematic review and meta-analysis.

慢性疲劳综合征患者的客观睡眠测量：系统评价和荟萃分析。

• DOI: 10.1016/j.smrv.2023.101771
• 发表时间: 2023
• 期刊: Sleep medicine reviews
• 影响因子: 10.5
• 作者: Mohamed,AbdallaZ;Andersen,Thu;Radovic,Sanja;DelFante,Peter;Kwiatek,Richard;Calhoun,Vince;Bhuta,Sandeep;Hermens,DanielF;Lagopoulos,Jim;Shan,ZackY
• 通讯作者: Shan,ZackY

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.

• DOI: 10.1371/journal.pone.0249502
• 发表时间: 2022
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Hassanzadeh R;Silva RF;Abrol A;Salman M;Bonkhoff A;Du Y;Fu Z;DeRamus T;Damaraju E;Baker B;Calhoun VD
• 通讯作者: Calhoun VD

Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study

确定具有功能连接的个体认知特征预测中的四个混杂因素：一项探索性研究

• DOI: 10.1093/cercor/bhac189
• 发表时间: 2022
• 期刊: Cerebral Cortex
• 影响因子: 3.7
• 作者: Feng, Pujie;Jiang, Rongtao;Wei, Lijiang;Calhoun, Vince D;Jing, Bin;Li, Haiyun;Sui, Jing
• 通讯作者: Sui, Jing

Intrinsic neural network dynamics in catatonia.

• DOI: 10.1002/hbm.25671
• 发表时间: 2021-12-15
• 期刊: Human brain mapping
• 影响因子: 4.8
• 作者: Sambataro F;Hirjak D;Fritze S;Kubera KM;Northoff G;Calhoun VD;Meyer-Lindenberg A;Wolf RC
• 通讯作者: Wolf RC

Structure/function interrelationships and illness insight in patients with schizophrenia: a multimodal MRI data fusion study.

• DOI: 10.1007/s00406-023-01566-1
• 发表时间: 2023-12
• 期刊: European archives of psychiatry and clinical neuroscience
• 影响因子: 4.7