REAL TIME DETECTION OF COGNITIVE STATES

实时检测认知状态

• 批准号: 8171195
• 负责人: Mark Steven Cohen
• 金额: $ 0.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171195
• 关键词: Affective; Algorithms; Atlases; Brain; Brain region; Cigarette; Cocaine; Cognitive; Computer Retrieval of Information on Scientific Projects Database; Data; Detection; Development; Drug abuse; Drug user; Electroencephalography; Feedback; Frequencies; Functional Magnetic Resonance Imaging; Funding; Goals; Grant; Health; Image; Impulsivity; Institution; Learning; Location; Machine Learning; Magnetic Resonance Imaging; Methamphetamine; Methods; Modeling; Patients; Pattern; Phase; Psyche structure; Research; Research Personnel; Resources; Running; Signal Transduction; Source; Technology; Time; United States National Institutes of Health; Work; addiction; base; cognitive control; craving; design; drug of abuse; human subject; independent component analysis; innovation; instrumentation; interest; mind control; neurofeedback; operation; public health relevance; relating to nervous system; research study; tool; volunteer

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 实时功能 MRI (rt-fMRI) 的神经反馈具有用于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，这种潜力才能实现。大脑的心理操作过于分散，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）使用计算机器学习快速检测 rt-fMRI 信号中的模式激活，以更好地表达认知状态； 2）用同时收集的脑电图（EEG）数据来增强这些数据； 3) 开发大脑数据图集，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) 使用这种 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比成像更快的模式匹配快速算法，从而允许“实时”应用。为此，我们将从图像中选择能够简明地表达状态差异的特征（更技术地说，我们将使用一种称为独立成分分析的方法来降低数据维度。）我们将类似地通过以下方式研究脑电图特征来浓缩它们：通过检查它们所包含的频率来确定它们在大脑中的来源位置。我们将对志愿者进行实验，旨在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渴望冲动时大脑变化的实验。在这些研究中，我们将关注大量吸烟者。吸烟本身对国家来说是一个严重的健康负担，而且它也是更普遍的成瘾的一个很好的模型，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）有许多共同的神经特征。这是一个分阶段的创新提案。第一阶段将重点关注 rt-fMRI 分析和仪器技术的发展。在其成功完成后，基于具体的里程碑，我们将转向针对人类受试者的更多应用性工作。公共健康相关性：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。我们对冲动和吸烟渴望的状态和标志特别感兴趣。我们的最终目标是拥有一种可在神经反馈背景下使用的工具，使人类受试者或患者能够接收与这些状态相关的大脑活动的指示，并使他们能够学会通过以下方式控制这些认知/情感状态：控制大脑活动。