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

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

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.