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

描述（由申请人提供）：实时功能MRI （rt-fMRI）的神经反馈具有成瘾研究和治疗的潜力，只有当给予受试者的反馈与必须控制的认知状态有意义相关时，才能实现。大脑的心理活动太分散了，无法用任何一个大脑区域或一小群区域的原始rt-fMRI信号来表示。我们的目标是：1)利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)用同时收集的脑电图（EEG）数据增强这些数据；3)开发大脑数据图谱，识别与成瘾和药物滥用研究相关的认知状态的大脑模式；4)利用这种rt-fMRI/EEG机器学习方法探索rt-fMRI神经反馈。我们的方法是首先创建快速的模式匹配算法，与成像相比速度更快，从而允许“实时”应用。为此，我们将从图像中选择简洁地表达状态之间差异的特征（更技术性地说，我们将使用一种称为独立分量分析的方法来降低数据维数）。我们将通过研究脑电图的来源在大脑中的位置，以及检查它们所包含的频率，类似地压缩脑电图的特征。我们将在志愿者身上进行实验，以帮助我们了解他们做出冲动选择的倾向——众所周知，这与他们成为吸毒者的可能性有关，我们还将进行实验，追踪他们在控制渴望冲动时大脑的变化。在这些研究中，我们将着眼于重度吸烟者。吸烟本身就是一个严重的健康负担，它也是一个很好的模型，更普遍的成瘾，因为众所周知，它有许多神经特征与使用其他滥用药物，如可卡因和甲基苯丙胺的共同之处。这是一个分阶段的创新方案。第一阶段将集中于rt-fMRI分析和仪器技术的发展。在其成功完成后，基于特定的里程碑，我们将转向与人类受试者更多的应用工作。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: Our research aims to develop and characterize a means of rapidly detecting brain states relevant to addiction research through the use of magnetic resonance imaging and electroencephalography. We are interested specifically in states and markers of impulsivity and cigarette craving. Our goal ultimately is to have a tool that can be used in the context of neurofeedback, allowing human subject or patient to receive an indication of activity in their brains associated with these states and to enable them to learn to control these cognitive/affective states by controlling the brain activity.