Statistical Models of Suicidal Behavior and Brain Biology Using Large Data Sets
使用大数据集的自杀行为和脑生物学的统计模型
基本信息
- 批准号:10207368
- 负责人:
- 金额:$ 14.66万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-07-19 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAdoptedBig DataBiologicalBiological FactorsBiologyBrainBrain imagingCategoriesComplexDNADNA Microarray ChipDataData AnalysesData SetDepressed moodDiagnosisDiffusion Magnetic Resonance ImagingDimensionsElementsEnsureEpigenetic ProcessEventFrequenciesFunctional Magnetic Resonance ImagingFutureGene ExpressionGeneticGenotypeGoalsHeartImageKnowledgeLightMagnetic Resonance SpectroscopyMeasuresMental DepressionMental disordersMethodologyMethodsMethylationModelingNeurobiologyOutcomePatientsPerformancePositron-Emission TomographyProceduresProcessResearchScanningSeriesSerotonergic SystemSeveritiesStatistical MethodsStatistical ModelsStructureSuicideSuicide attemptSurvival AnalysisTechniquesTimeTrainingTranslatingValidationbasecomplex datacontextual factorsdata modelingdepressed patientfunctional MRI scangenome-widehigh dimensionalityhigh riskimprovedindexinginsightinterestlarge datasetsmachine learning algorithmmodel developmentmultidimensional dataneglectoutcome predictionpredicting responserisk predictionsuicidal behaviorsuicide attemptertooltraitvalidation studies
项目摘要
SUMMARY-PROJECT 6
The primary objective of the Conte Center is to better understand the causes of suicidal behavior and,
ultimately, to translate that knowledge into tools that have the potential for detecting patients at higher risk for
more lethal suicide attempts. Towards that aim, a great deal of valuable data is being collected. This project is
primarily concerned with very high-dimensional data, including brain imaging data, DNA genetic and epigenetic
data, and gene expression data. In particular, we are interested in using such data as predictors in statistical
models. Our ultimate goal is to build and implement models that can be used to distinguish depressed suicide
attempters from other depressed patients.
The greatest hurdle is dealing with very high dimensional data, a situation that classical statistical modeling
techniques are not equipped to handle. Our approach to modeling with such high-dimensional data seeks to
achieve three primary goals: (1) to provide good predictive accuracy; (2) to build interpretable models that can
give meaningful insight into the relationship between the various predictors and the outcome variable; (3) to
ensure stability of the models through validation studies. To accomplish these goals our approach will
incorporate a regularization component in the model selection and fitting process by applying a penalty to
model complexity. In applications, the amount of penalization will be determined by cross-validation or another
related technique. In both model development and application, we will consider a wide range of outcome
variables including continuous or nearly continuous (e.g., severity of depression, aggressive traits), ordinal
(e.g., lethality of suicide attempts), categorical (e.g., diagnosis), and binary (yes/no, e.g., suicide attempt), as
well as time-to-event data (e.g., time to a suicide attempt), which will draw on survival analysis techniques.
This project focuses specifically on situations in which the number of possible predictors is in the tens of
thousands or more. Examples of existing data and data to be gathered as part of the Conte Center include
brain imaging data (positron emission tomography (PET) images of the serotonin system, diffusion tensor
imaging (DTI) scans, functional magnetic resonance imaging (fMRI) scans, magnetic resonance spectroscopy
(MRS) scans), genome-wide DNA genotyping and methylation data, and gene expression data.
摘要-项目 6
康特中心的主要目标是更好地了解自杀行为的原因,并且,
最终,将这些知识转化为有潜力检测高风险患者的工具
更致命的自杀企图。为了实现这一目标,我们正在收集大量有价值的数据。这个项目是
主要涉及非常高维的数据,包括脑成像数据、DNA 遗传和表观遗传
数据和基因表达数据。特别是,我们有兴趣使用此类数据作为统计中的预测变量
模型。我们的最终目标是建立和实施可用于区分抑郁症自杀的模型
来自其他抑郁症患者的尝试者。
最大的障碍是处理非常高维的数据,这是经典统计建模无法解决的情况
技术不具备处理能力。我们使用此类高维数据进行建模的方法旨在
实现三个主要目标:(1)提供良好的预测准确性; (2) 建立可解释的模型
对各种预测变量和结果变量之间的关系提供有意义的见解; (3) 至
通过验证研究确保模型的稳定性。为了实现这些目标,我们的方法将
通过对模型进行惩罚,将正则化组件纳入模型选择和拟合过程中
模型复杂度。在应用中,惩罚的金额将通过交叉验证或其他方法来确定
相关技术。在模型开发和应用中,我们都会考虑广泛的结果
变量包括连续或接近连续(例如抑郁的严重程度、攻击性特征)、序数
(例如,自杀未遂的致死率)、分类(例如,诊断)和二元(是/否,例如,自杀未遂),如
以及事件发生时间数据(例如,自杀企图的时间),这将利用生存分析技术。
该项目特别关注可能的预测变量数量为数十的情况
数千或更多。现有数据和作为 Conte 中心一部分收集的数据的示例包括
脑成像数据(血清素系统的正电子发射断层扫描 (PET) 图像、扩散张量
成像 (DTI) 扫描、功能磁共振成像 (fMRI) 扫描、磁共振波谱
(MRS) 扫描)、全基因组 DNA 基因分型和甲基化数据以及基因表达数据。
项目成果
期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
专利数量(0)
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{{ truncateString('TODD OGDEN', 18)}}的其他基金
Advanced Modeling Techniques for Brain Imaging Data with PET
使用 PET 进行脑成像数据的先进建模技术
- 批准号:
9980905 - 财政年份:2017
- 资助金额:
$ 14.66万 - 项目类别:
Statistical Models with High-Dimensional Predictors
具有高维预测变量的统计模型
- 批准号:
8917367 - 财政年份:2014
- 资助金额:
$ 14.66万 - 项目类别:
Statistical Models with High-Dimensional Predictors
具有高维预测变量的统计模型
- 批准号:
8605258 - 财政年份:2013
- 资助金额:
$ 14.66万 - 项目类别:
Statistical Models of Suicidal Behavior and Brain Biology Using Large Data Sets
使用大数据集的自杀行为和脑生物学的统计模型
- 批准号:
10408798 - 财政年份:2013
- 资助金额:
$ 14.66万 - 项目类别:
Functional Regress Models with Application in Brain Imaging Studies
功能回归模型在脑成像研究中的应用
- 批准号:
7899424 - 财政年份:2010
- 资助金额:
$ 14.66万 - 项目类别:
Functional Regress Models with Application in Brain Imaging Studies
功能回归模型在脑成像研究中的应用
- 批准号:
8096704 - 财政年份:2010
- 资助金额:
$ 14.66万 - 项目类别:
Functional Regress Models with Application in Brain Imaging Studies
功能回归模型在脑成像研究中的应用
- 批准号:
8246500 - 财政年份:2010
- 资助金额:
$ 14.66万 - 项目类别:
Statistical Models with High-Dimensional Predictors
具有高维预测变量的统计模型
- 批准号:
9099972 - 财政年份:
- 资助金额:
$ 14.66万 - 项目类别:
Statistical Models with High-Dimensional Predictors
具有高维预测变量的统计模型
- 批准号:
8704228 - 财政年份:
- 资助金额:
$ 14.66万 - 项目类别:
Statistical Models of Suicidal Behavior and Brain Biology Using Large Data Sets
使用大数据集的自杀行为和脑生物学的统计模型
- 批准号:
9490063 - 财政年份:
- 资助金额:
$ 14.66万 - 项目类别:
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