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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.

项目总结/摘要真实的时间功能磁共振成像（rt-fMRI）神经反馈技术在成瘾研究中具有潜在的应用价值只有当给予受试者的反馈是相关的，有意义的认知状态，必须加以控制。大脑的运作大脑分布太广，无法用任何一个大脑的原始rt-fMRI信号来表示一个地区或一小群地区。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，状态; 2）用同时收集的脑电图（EEG）增强这些数据数据; 3）开发大脑数据图谱，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。我们的方法将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特征，简明地表达状态之间的差异（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EEG特征通过它们在大脑中的来源位置，以及通过检查频率，它们包含。我们将对志愿者进行实验，以帮助我们了解他们的倾向，冲动的选择-这是众所周知的，与他们成为吸毒者的可能性，以及跟踪他们控制渴望冲动时大脑变化的实验。在这些研究中，我们将研究重度吸烟者。吸烟本身就是一种严重的健康负担的国家，它也是一个很好的模式，成瘾更多一般来说，因为已知它与使用其他药物具有许多共同的神经特征，滥用，如可卡因和甲基苯丙胺。这是一个阶段性的创新方案。第一阶段将侧重于实时功能磁共振成像分析和仪器技术。在其成功完成后，根据具体的里程碑，我们将转向更多的应用工作，科目