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

• 批准号: 8087592
• 负责人: Mark Steven Cohen
• 金额: $ 59.34万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-25 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8087592
• 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 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; 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; method development; mind control; neurofeedback; neuroimaging; novel; operation; programs; public health relevance; relating to nervous system; research study; response; symposium; tool; trend; virtual; volunteer

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.

描述(由申请人提供)：实时功能磁共振成像(RT-fMRI)的神经反馈在成瘾研究和治疗中具有潜力，只有当给予受试者的反馈与必须控制的认知状态有意义地相关时，才能实现这一点。大脑的心理操作过于分散，无法用任何一个脑区或一小群脑区的原始RT-fMRI信号来表示。我们的目标是：1)使用计算机器学习来快速检测RT-fMRI信号中更好地表达认知状态的模式激活；2)使用同时收集的脑电(EEG)数据来增强这些数据；3)开发大脑数据图谱，以识别与成瘾和药物滥用研究相关的认知状态的大脑模式；以及4)使用这种RT-fMRI/EEG机器学习方法来探索RT-fMRI神经反馈。我们的方法将是首先创建模式匹配的快速算法，与成像相比，这些算法更快，从而允许“实时”应用。为此，我们将从图像中选择简明地表达状态之间差异的特征(更专业地说，我们将使用一种称为独立分量分析的方法来降低数据维度。)类似地，我们将通过研究脑电特征来研究它们在大脑中的来源位置，并通过检查它们包含的频率来浓缩它们。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，以及跟踪他们在控制欲望冲动时大脑的变化。在这些研究中，我们将关注重度吸烟者。吸烟本身就是一个严重的健康负担，从更广泛的角度来看，它也是上瘾的一个极好的模式，因为众所周知，它与使用其他滥用药物，如可卡因和甲基苯丙胺有许多共同的神经功能。这是一个阶段性的创新建议。第一阶段将集中于RT-fMRI分析和仪器技术的发展。在它成功完成后，根据具体的里程碑，我们将转移到与人类主体更实用的工作。 与公共健康相关：我们的研究旨在开发和表征一种通过使用磁共振成像和脑电图来快速检测与成瘾研究相关的大脑状态的方法。我们对冲动和吸烟的状态和标志特别感兴趣。我们的最终目标是拥有一种可以在神经反馈的背景下使用的工具，允许人类受试者或患者接收与这些状态相关的大脑活动的指示，并使他们能够通过控制大脑活动来学习控制这些认知/情感状态。