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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

基本信息

批准号：
9980905
负责人：
TODD OGDEN
金额：
$ 36.02万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-17 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9980905
关键词：
Address Affect Age Algorithms Alzheimer&apos s Disease Amyloid beta-Protein Anxiety Disorders Binding Biological Biological Process Bipolar Disorder Blood Brain Brain imaging Categories Clinical Research Communities Complex Computer software DNA Data Data Analyses Depressed mood Diagnostic Disease Human Image Individual Kinetics Measurement Measures Mental Depression Methodology Methods Modeling Outcome Measure Patients Pattern Population Positron-Emission Tomography Post-Traumatic Stress Disorders Procedures Process Proteins Psyche structure Research Schizophrenia Serotonergic System Statistical Data Interpretation Structure Techniques Testing Time Tracer analytical tool base burden of illness clinical application cost data modeling density flexibility improved interest kinetic model method development neuropsychiatric disorder neuropsychiatry radiotracer receptor density sugar suicide attempter tool treatment response virtual

项目摘要

Summary Mental and neuropsychiatric illnesses (including depression, Alzheimer's Disease, and many others) will affect roughly 20% of the population sometime during their lifetimes. By some measurements these illnesses represent the leading category of disease burden worldwide. Positron Emission Tomography (PET) of the brain has become an invaluable research tool for studying such illnesses because it allows quantiﬁcation of the density of various molecules throughout the brain. In the current state of the art in the analysis of PET imaging data, there are two major drawbacks. The ﬁrst is that analysis is always done as a “two-stage” process: Stage 1 consists of modeling the PET data over time to get a single (scalar) estimate of receptor density, either for each voxel or for each of one or more regions of interest. Subsequently, in Stage 2 these estimates are effectively regarded as the observed data, and statistical analysis involves comparing these estimates across individuals, between diagnostic groups, etc. This is an inefﬁcient use of data and it does not allow good precision when investigating some subtle systematic effects. The second major drawback is that the ﬁeld relies almost exclusively on parametric models. The basic model for PET data in a voxel or ROI is a kinetic model that relies on some fairly strong assumptions about the biological processes that, while they are often reasonable approximations to the truth in some instances, are often thought to be violated. By relying on principles of functional data analysis (FDA), we can open up a powerful new analysis structure for investigating differences among individuals, among groups, and for making individual- level predictions (e.g., response to treatment). This project will undertake the following three aims. 1. To develop methodology based on parametric models that combines both Stage 1 and Stage 2 into a single analysis process. This will allow for much more reﬁned analysis that can look for differences between groups in individual kinetic rate parameters, rather than relying only on aggregate outcome measures. 2. To develop FDA-based tools for comparing PET imaging data across subjects, across groups, etc. This will require new analysis methods since the relevant functional data are not observed directly but can only be estimated using some form of nonparametric deconvolution algorithm of the observed PET data over time. 3. To incorporate recent advances made by our group and others, in the contexts of both the parametric and the nonparametric approaches, to the situation in which blood data and/or a “reference region” is not available. Aim 1 is intended for PET radiotracers in which parametric models exist and provide a reasonable ﬁt for the data. Aim 2 is intended both for tracers not described well by usual parametric models and also as supplementary nonparametric analysis. Aim 3 will extend the reach of these methods and widen the potential application of PET imaging. These new advances have the potential to greatly enhance our understanding of the biological underpinnings of many neuropsychiatric diseases as well as response to treatment.

总结