QuBBD: Collaborative Research: Personalized Predictive Neuromarkers for Stress-Related Health Risks
QuBBD:合作研究:压力相关健康风险的个性化预测神经标志物
基本信息
- 批准号:1557572
- 负责人:
- 金额:$ 9.12万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-09-15 至 2016-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Coronary heart disease (CHD) remains the leading cause of premature death among adults in the U.S. and other postindustrial nations. Predicting CHD risk - especially for a particular individual - remains a fundamental challenge. This project will investigate the application of statistical machine learning approaches to multimodal brain imaging, behavioral, biological, and related data to enhance the prediction of CHD risk. It specifically addresses the question of whether particular patterns of human brain activity during psychological stress reliably predict known risk markers of CHD; namely, stress-related rises in blood pressure and arterial morphology. From a basic science perspective, this research will advance our mechanistic understanding of how the brain relates to our physical health. From a public health perspective, this research will help to identify markers of brain activity that could be objectively identified and possibly targeted for modification in otherwise healthy people at risk for future CHD. The key challenge with mapping neuroimaging data to CHD risk lies in being able to very precisely regress observed psychological stress reactions on the time-series of brain activity recorded in thousands of voxels, and identify which brain regions are most relevant for the regression. Conventional analytic approaches involve forming coarse temporal summaries by committing to specific parametric models, such as a generalized linear model and a fixed model for hemodynamic response, that result in poor accuracy. This award supports initiation of a collaborative research project that brings together a highly cross-disciplinary team of statistical machine learning, neuroimaging and health psychology researchers to tackle the following two goals: 1) identify a generalizable model and neuromarkers that predict individual differences in cardiovascular risk factors based on neural dynamics under psychological stress. This will be enabled through novel methods for functions-to-real and functions-to-function lasso regression; 2) characterize how neural patterns can be integrated with other physiological and anthropometric factors to personalize individual risk scores and neuro-biomarkers. This award is supported by the National Institutes of Health Big Data to Knowledge (BD2K) Initiative in partnership with the National Science Foundation Division of Mathematical Sciences.
在美国和其他后工业化国家,冠心病(CHD)仍然是成年人过早死亡的主要原因。预测冠心病风险--尤其是对特定个体而言--仍然是一个根本性的挑战。该项目将研究统计机器学习方法在多模式脑成像、行为、生物学和相关数据中的应用,以增强对CHD风险的预测。它具体解决了心理应激期间人类大脑活动的特定模式是否可靠地预测已知的冠心病风险标记物的问题,即与应激相关的血压和动脉形态的上升。从基础科学的角度来看,这项研究将推进我们对大脑如何与我们的身体健康相关的机械性理解。从公共卫生的角度来看,这项研究将有助于确定脑活动的标记物,这些标记物可以被客观地识别,并可能有针对性地在其他健康的人中进行修改,这些人面临着未来冠心病的风险。将神经成像数据映射到CHD风险的关键挑战在于,能够非常精确地回归在以数千个体素记录的大脑活动时间序列上观察到的心理应激反应,并确定哪些大脑区域与回归最相关。传统的分析方法涉及通过致力于特定的参数模型来形成粗略的时间摘要,例如用于血流动力学响应的广义线性模型和固定模型,这导致了较差的准确性。该奖项支持发起一个合作研究项目,该项目汇集了一个由统计机器学习、神经成像和健康心理学研究人员组成的高度跨学科的团队,以实现以下两个目标:1)确定基于心理压力下的神经动力学预测心血管风险因素个体差异的通用模型和神经标记物。这将通过新的功能到真实和功能到功能套索回归的方法来实现;2)表征神经模式如何与其他生理和人体测量因素相结合,以个性化个人风险评分和神经生物标记物。该奖项由国家卫生研究院大数据向知识转化(BD2K)倡议与国家科学基金会数学科学部合作支持。
项目成果
期刊论文数量(0)
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专利数量(0)
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Aarti Singh其他文献
Noise-Adaptive Margin-Based Active Learning and Lower Bounds under Tsybakov Noise Condition
Tsybakov 噪声条件下基于噪声自适应裕度的主动学习和下界
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
Yining Wang;Aarti Singh - 通讯作者:
Aarti Singh
Design of an Intelligent and Adaptive Mapping Mechanism for Multiagent Interface
一种智能自适应多智能体接口映射机制设计
- DOI:
10.1007/978-3-642-22577-2_51 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Aarti Singh;Dimple Juneja;A. Sharma - 通讯作者:
A. Sharma
A closer look at jobless recoveries
仔细观察失业复苏
- DOI:
- 发表时间:
2003 - 期刊:
- 影响因子:0
- 作者:
Stacey L. Schreft;Aarti Singh - 通讯作者:
Aarti Singh
Hybrid Reinforcement Learning from Offline Observation Alone
仅从离线观察中进行混合强化学习
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Yuda Song;J. Bagnell;Aarti Singh - 通讯作者:
Aarti Singh
Incomplete Credit Markets and Monetary Policy
不完整的信贷市场和货币政策
- DOI:
10.20955/wp.2015.010 - 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Costas Azariadis;James Bullard;Aarti Singh;Jacek Suda - 通讯作者:
Jacek Suda
Aarti Singh的其他文献
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{{ truncateString('Aarti Singh', 18)}}的其他基金
AI Institute for Societal Decision Making (AI-SDM)
人工智能社会决策研究所 (AI-SDM)
- 批准号:
2229881 - 财政年份:2023
- 资助金额:
$ 9.12万 - 项目类别:
Cooperative Agreement
Collaborative Research: New Perspectives on Deep Learning: Bridging Approximation, Statistical, and Algorithmic Theories
合作研究:深度学习的新视角:桥接近似、统计和算法理论
- 批准号:
2134133 - 财政年份:2021
- 资助金额:
$ 9.12万 - 项目类别:
Standard Grant
15th IMS New Researchers Conference
第15届IMS新研究员大会
- 批准号:
1301845 - 财政年份:2013
- 资助金额:
$ 9.12万 - 项目类别:
Standard Grant
CAREER: Distilling information structure from big and dirty data: Efficient learning of clusters and graphs in modern datasets
职业:从大数据和脏数据中提取信息结构:现代数据集中集群和图的高效学习
- 批准号:
1252412 - 财政年份:2013
- 资助金额:
$ 9.12万 - 项目类别:
Continuing Grant
BIGDATA: Mid-Scale: DA: Distribution-based machine learning for high dimensional datasets
BIGDATA:中规模:DA:针对高维数据集的基于分布的机器学习
- 批准号:
1247658 - 财政年份:2013
- 资助金额:
$ 9.12万 - 项目类别:
Continuing Grant
III: Small: Spectral Methods for Active Clustering and Bi-Clustering
III:小:主动聚类和双聚类的谱方法
- 批准号:
1116458 - 财政年份:2011
- 资助金额:
$ 9.12万 - 项目类别:
Standard Grant
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