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Multi-Source Sparse Learning to Identify MCI and Predict Decline

多源稀疏学习识别 MCI 并预测衰退

基本信息

批准号：
9008380
负责人：
PAUL M THOMPSON
金额：
$ 281.54万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-01 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9008380
关键词：
Academy Algorithms Alzheimer&apos s Disease American Attention Attention deficit hyperactivity disorder Bioinformatics Biological Biological Markers Bipolar Disorder Brain Brain imaging Cerebrospinal Fluid Clinical Clinical Research Clinical Trials Cognitive Communities Complex Computer software Computing Methodologies Country Data Data Collection Data Set Data Sources Databases Dementia Development Disease Early Diagnosis Engineering Exhibits Floods Formulation Frontotemporal Dementia Functional disorder Funding Genetic Impaired cognition Institution Intervention Knowledge Learning Magnetic Resonance Imaging Mathematics Measurement Measures Mental Depression Mental disorders Methods Modality Modeling Monitor Neurocognitive Neurology Parkinson Disease Pathway interactions Patients Pattern Prevention Preventive treatment Process Proteomics Publishing Qualifying Research Research Personnel Research Project Grants Schizophrenia Scientific Evaluation Scientist Software Tools Source Staging Structure System Testing United States National Institutes of Health Vision Work accurate diagnosis base biomarker discovery biomarker identification clinical Diagnosis cognitive testing computer framework early detection biomarkers fluorodeoxyglucose positron emission tomography genetic information high risk improved innovation meetings mild cognitive impairment model building multitask nervous system disorder neuroimaging neuropsychological normal aging novel open source public health relevance screening software development tool

项目摘要

DESCRIPTION (provided by applicant): Patients with Mild Cognitive Impairment (MCI) are at high risk of progression to dementia. MCI offers an opportunity to target the disease process early. Clinicians and researchers are intensifying their efforts to detect MCI pre-symptomatically in order to develop preventive treatments. These efforts generate a large amount of data - brain images of multiple modalities, and proteomics, genetic, and neurocognitive data that provide unprecedented opportunities to investigate MCI-related questions with greater precision and predictive power. Understanding its importance, NIH in 2003 funded the Alzheimer's Disease Neuroimaging Initiative (ADNI) to facilitate scientific evaluation of various biomarkers for the onset and progression of MCI and AD. To realize such an ambitious vision, there is an urgent need for multi-source fusion and disease biomarker discovery frameworks. While promising, large volumes of incomplete data from multiple heterogeneous data sources pose huge challenges to scientists and engineers. For instance, the ADNI-1 data (like many other large datasets) exhibit a block-wise missing pattern: most subjects have MRI, genetic information; about half of the subjects have CSF measures; a different half of the subjects have FDG-PET; and some subjects have proteomics data. Although many bioinformatics tools are available, no existing tools offer an effective way to fuse multi-source incomplete data for disease biomarker discovery. Here we aim to develop a novel computational framework to integrate and analyze multiple, heterogeneous, large volume, incomplete biomedical data for early detection of MCI. Our 4 primary aims are: (1) Develop novel structured sparse learning formulations for multi-source fusion. The computational methods will identify biomarkers to correlate multi-source data with MCI. Novel sparse screening methods will be developed to scale the proposed formulations to very high-dimensional data. (2) Develop computational methods to integrate network data. We will develop novel methods for incorporating existing biological knowledge such as pathways represented as networks into the prediction model. The network structure will be used as prior knowledge to constrain model parameters, to further improve predictive power. (3) Develop computational methods to integrate multiple incomplete data sources. The proposed computational framework will integrate multiple heterogeneous data with a block-wise missing pattern. The proposed framework formulates the multiple incomplete data source fusion problem as a multi-task learning problem by first decomposing the prediction problem into a set of tasks, then building the models for all tasks simultaneously. (4) Develop and disseminate software tools for multi-source fusion and biomarker identification. The software tools will be used for early detection of MCI and will be validated by several clinical research projects. Our open source software will be made freely available to the research communities, including our large community of existing users. One of our current packages, SLEP, has ~4,500 active users from ~25 countries. Our software tools will be easily adaptable for analyzing multi-source data from other neurological and psychiatric disorders.

描述(由申请人提供)：轻度认知障碍(MCI)患者进展为痴呆症的风险很高。MCI提供了一个及早针对疾病过程的机会。临床医生和研究人员正在加紧努力，在症状前检测MCI，以开发预防性治疗。这些努力产生了大量的数据-多种模式的脑图像，以及蛋白质组学、遗传学和神经认知数据，这些数据提供了前所未有的机会，以更高的精确度和预测能力调查与MCI相关的问题。认识到其重要性，美国国立卫生研究院于2003年资助了阿尔茨海默病神经成像倡议(ADNI)，以促进对MCI和AD的发生和发展的各种生物标志物的科学评估。为了实现这一雄心勃勃的愿景，迫切需要多源融合和疾病生物标记物发现框架。尽管前景看好，但来自多个异质数据源的大量不完整数据给科学家和工程师带来了巨大的挑战。例如，ADNI-1数据(与许多其他大型数据集一样)呈现出分块缺失的模式：大多数受试者有MRI、遗传信息；大约一半的受试者有脑脊液检测；另一半的受试者有FDG-PET；一些受试者有蛋白质组学数据。虽然有许多生物信息学工具可用，但现有的工具还没有提供一种有效的方法来融合多源不完整数据来发现疾病生物标记物。在这里，我们的目标是开发一种新的计算框架来整合和分析多个、异质、大容量、不完整的生物医学数据，以用于早期检测MCI。我们的四个主要目标是：(1)开发用于多源融合的新型结构化稀疏学习公式。计算方法将识别生物标记物，以将多源数据与MCI相关联。将开发新的稀疏筛选方法，以将建议的配方扩大到非常高维的数据。(2)开发集成网络数据的计算方法。我们将开发新的方法，将现有的生物学知识，如以网络表示的路径纳入预测模型。将网络结构作为先验知识对模型参数进行约束，进一步提高预测能力。(3)开发集成多个不完整数据源的计算方法。所提出的计算框架将整合多个具有块状缺失模式的异类数据。该框架首先将预测问题分解为一组任务，然后同时为所有任务建立模型，从而将多个不完全数据源融合问题描述为一个多任务学习问题。(4)开发和传播用于多源融合和生物标志物识别的软件工具。该软件工具将用于MCI的早期检测，并将通过几个临床研究项目进行验证。我们的开源软件将免费提供给研究社区，包括我们庞大的现有用户社区。我们当前的套餐之一SLEP拥有来自约25个国家/地区的约4,500名活跃用户。我们的软件工具将很容易地适用于分析来自其他神经和精神疾病的多源数据。