Real-Time Automated Detection of Craving States with fMRI and EEG

利用功能磁共振成像和脑电图实时自动检测渴望状态

基本信息

批准号：
8288263
负责人：
Mark Steven Cohen
金额：
$ 56.68万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-25 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8288263
关键词：
Abstinence Address Affective Algorithms Anterior Atlases Base of the Brain Behavior Brain Brain region Cigarette Classification Clinical Cocaine Cognitive Complex Computer-Assisted Image Analysis Computing Methodologies Data Data Analyses Data Collection Data Set Databases Detection Development Devices Dimensions Discrimination Drug abuse Drug user Electroencephalogram Electroencephalography Electrophysiology (science)Epilepsy Feedback Four-dimensional Frequencies Functional Magnetic Resonance Imaging Galvanic Skin Response Generations Goals Health Healthcare Image Imaging technology Impulsivity Intervention Learning Location Machine Learning Magnetic Resonance Imaging Maps Measures Medical Methamphetamine Methods Modeling Patients Pattern Pharmaceutical Preparations Phase Physiological Proxy Psyche structure Reporter Reporting Research Rest Running Scalp structure Schizophrenia Short-Term Memory Signal Transduction Source Stimulus Study Subject Technology Testing Time Traumatic Brain Injury United States National Institutes of Health Work abstracting addiction base blind chronic pain cingulate cortex cognitive control craving data space design discounting drug of abuse effective intervention human subject improved independent component analysis innovation instrument instrumentation interest machine abstracting method development mind control neurofeedback neuroimaging novel operation programs relating to nervous system research study response symposium tool trend virtual volunteer

项目摘要

Project Summary/Abstract Neurofeedback by real time functional MRI (rt-fMRI) has potential for addiction research and treatment that will be realized only if the feedback given the subject is related meaningfully to the cognitive states that must be controlled. The mental operations of the brain are too distributed to be represented by the raw rt-fMRI signal in any one brain region or small group of regions. Our aims are to: 1) Use computational machine learning to rapidly detect patterned activation in the rt-fMRI signal that better expresses cognitive state; 2) augment these data with concurrently-collected electroencephalographic (EEG) data; 3) develop an atlas of brain data that identifies brain patterns with cognitive states relevant to addiction and drug abuse research and 4) to explore rt-fMRI neurofeedback using this rt-fMRI/EEG machine learning method. Our approach will be to first create rapid algorithms for pattern matching that are fast compared with the imaging, thereby allowing "real-time" application. To do so we will select features from the images that express the differences among state concisely (more technically, we will use a method known as independent components analysis to reduce the data dimensionality.) We will similarly condense the EEG features by studying them by the location of their sources within the brain, and by examining the frequencies that they contain. We will run experiments on volunteers designed to help us see their tendency to make impulsive choices - which is known to relate to their likelihood to become drug users, as well as experiments that track changes in their brain as they control their craving urges. For these studies we will look at heavy cigarette users. Cigarette use on its own is a serious health burden to the nation, and it is also an excellent model for addiction more generally, as it is known to have many neural features in common with use of other drugs of abuse, such as cocaine and methamphetamine. This is a phased innovation proposal. The first phase will be focused on the developments of the rt-fMRI analysis and instrumentation technology. On its successful completion, based on specific milestones, we will move to the more applied work with human subjects.

项目概要/摘要实时功能性 MRI (rt-fMRI) 神经反馈具有用于成瘾研究的潜力只有当给予受试者的反馈是相关的时才会实现的治疗对必须控制的认知状态有意义。人的心理运作大脑的分布过于分散，无法用任何一个大脑中的原始 rt-fMRI 信号来表示区域或一小群区域。我们的目标是：1）使用计算机器学习快速检测 rt-fMRI 信号中的模式激活，更好地表达认知状态; 2）通过同时收集的脑电图（EEG）增强这些数据数据; 3）开发大脑数据图集，识别具有认知状态的大脑模式与成瘾和药物滥用研究相关，4) 探索 rt-fMRI 神经反馈使用这种 rt-fMRI/EEG 机器学习方法。我们的方法是首先创建快速的模式匹配算法与成像相比，从而允许“实时”应用。为此，我们将从图像中选择能够简洁表达状态差异的特征（更多从技术上讲，我们将使用一种称为独立成分分析的方法来减少数据维度。）我们将通过研究它们来类似地浓缩脑电图特征通过它们在大脑中的来源位置，并通过检查频率它们包含。我们将对志愿者进行实验，旨在帮助我们了解他们的倾向冲动选择——众所周知，这与他们成为吸毒者的可能性有关，如以及追踪他们控制食欲时大脑变化的实验。在这些研究中，我们将关注大量吸烟者。单独使用香烟是一种给国家带来严重的健康负担，也是成瘾的绝佳典范更多一般来说，因为已知它与其他药物的使用有许多共同的神经特征滥用，例如可卡因和甲基苯丙胺。这是一个分阶段的创新提案。第一阶段将重点关注发展 rt-fMRI 分析和仪器技术。其圆满完成后，基于具体的里程碑，我们将转向与人类一起进行更实用的工作科目。