Multivariate methods for identifying multitask/multimodal brain imaging biomarkers

识别多任务/多模式脑成像生物标志物的多变量方法

基本信息

批准号：
9185800
负责人：
VINCE D CALHOUN
金额：
$ 40万
依托单位：
THE MIND RESEARCH NETWORK
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-04-01 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9185800
关键词：
Address Algorithms Area Attention Behavioral Biological Markers Biological Neural Networks Biology Bipolar Disorder Blood flow Brain Brain Diseases Brain imaging Classification Clinical Cognitive Complex Computer software Data Data Set Databases Decision Making Diagnosis Diffusion Magnetic Resonance Imaging Disease Electroencephalography Evaluation Functional Magnetic Resonance Imaging Funding Goals Human Hybrids Image Imagery Imaging technology Joints Learning Link Measurable Measures Mental Depression Mental disorders Methods Mining Modality Modeling Mood Disorders Moods Multimodal Imaging Neurobiology Online Systems Pathway interactions Patients Pharmaceutical Preparations Phase Process Psychotic Disorders Recurrence Reporting Research Research Personnel Rest Risk Sample Size Sampling Schizophrenia Series Shipping Ships Software Tools Symptoms Tars Testing Time Training Unipolar Depression Validation Work abstracting base clinical care clinical phenotype data reduction data sharing deep field survey design gray matter imaging biomarker imaging modality improved multitask neuroinformatics neuropsychiatric disorder next generation novel novel strategies open source potential biomarker repository simulation success tool web portal white matter change

项目摘要

Project Summary/Abstract The brain is extremely complex as we know, involving a complicated interplay between functional information interacting with a structural (but not static) substrate. Brain imaging technology provides a way to sample various aspects of the brain albeit incompletely, providing a rich set of multitask and multimodal information. The field has advanced significantly in its approach to multimodal data, as there are more studies correlating, e.g. func- tional and structural measures. However the vast majority of studies still ignore the joint information among two or more modalities or tasks. Such information is critical to consider as each brain imaging modality reports on a different aspect of the brain (e.g. gray matter integrity, blood flow changes, white matter integrity). The field is still striving to understand how to diagnose and treat complex mental illness, such as schizophrenia, bipolar disorder, depression, and others, and ignoring the joint information among tasks and modalities is to miss a critical, but available, part of the puzzle. Combining multimodal imaging data is not easy since, among other reasons, the combination of multiple data sets consisting of thousands of voxels or timepoints yields a very high dimensional problem, requiring appropriate data reduction strategies. In the previous phase of the project we developed approaches based on multiset canonical correlation analysis (mCCA) and joint independent compo- nent analysis (jICA) that can capture high-dimensional, linear, relationships among 2 or more modalities, and which we showed can identify both modality-unique and modality-common features that are predictive of dis- ease. In this new phase of the project we will focus on two important areas. First, we will build on our previous success by extending our models to allow for incorporation of behavioral/cognitive constraints as well as devel- oping new approaches which leverage recent advances in deep learning enabling us to capture higher order relationships embedded in multimodal and multitask data. Secondly, we will address the key challenge of inte- grating possibly thousands of multimodal features by developing a new meta-modality framework which will enable us to bring together the existing and new features in an intuitive manner. This will also enable us to capture changes in multimodal information which might not be harmful separately but which together are jointly sufficient to convey risk of illness or to identify information flow through the meta-modal space for developing potential targets for treatment. We will apply these approaches to one of the largest multimodal imaging datasets of psychosis and mood disorders. Our proposed approach will be thoroughly evaluated using this large data set which includes multiple illnesses that have overlapping symptoms and which can sometimes be misdiagnosed and treated with the wrong medications for months or years (schizophrenia, bipolar disorder, and unipolar de- pression). As before, we will provide open source tools and release data throughout the duration of the project via a web portal and the NITRIC repository, hence enabling other investigators to compare their own methods with our own as well as to apply them to a large variety of brain disorders. 36

项目摘要/摘要正如我们所知，大脑非常复杂，涉及功能信息之间的复杂相互作用与结构（但不是静态）底物相互作用。脑成像技术提供了一种品尝各种的方法大脑的各个方面尽管不完整，提供了丰富的多任务和多模式信息。领域由于更多的研究相关，例如功能 - 建立和结构措施。但是，绝大多数研究仍然忽略了两个研究或更多方式或任务。当每种大脑成像方式报告在大脑的不同方面（例如，灰质完整性，血流变化，白质完整性）。该领域是仍在努力了解如何诊断和治疗复杂的精神疾病，例如精神分裂症，双极混乱，抑郁症和其他人，忽略任务和方式之间的共同信息是错过关键但可用的是难题的一部分。组合多模式成像数据并不容易，因为原因，由数千个体素或时间点组成的多个数据集的组合产生了很高的维度问题，需要适当的数据减少策略。在项目的前阶段，我们开发的方法基于多种规范相关分析（MCCA）和联合独立组合可以捕获两种或更多方式之间的高维，线性关系的NENT分析（JICA），我们展示舒适。在该项目的这个新阶段，我们将重点关注两个重要领域。首先，我们将建立以前的通过扩展我们的模型以允许纳入行为/认知约束以及开发的成功来取得成功开发新的方法，这些方法利用了最新的深度学习进步，使我们能够捕捉更高秩序嵌入在多模式和多任务数据中的关系。其次，我们将解决Inte的主要挑战通过开发一个新的元模式框架来使可能数以千计的多模式特征进行光栅使我们能够以直观的方式将现有和新功能汇总在一起。这也将使我们能够捕获多模式信息的变化，这些信息可能不是分别有害但共同有害的足以传达疾病的风险或识别通过元模式空间的信息流以发展潜在的治疗靶标。我们将把这些方法应用于最大的多模式成像数据集之一精神病和情绪障碍。我们提出的方法将使用此大数据集进行彻底评估其中包括多种症状重叠且有时会被误诊的疾病并用错误的药物治疗数月或数年（精神分裂症，躁郁症和单极性疾病压力）。和以前一样，我们将在项目的整个过程中提供开源工具和发布数据通过Web门户和一氮存储库，使其他研究人员能够比较自己的方法凭借我们的并将其应用于各种各样的脑部疾病。 36