权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

项目总结/文摘