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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 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。大脑的心理操作过于分布，无法在任何一个大脑区域或小组区域中由原始RT-FMRI信号表示。我们的目的是：1）使用计算机学习在RT-FMRI信号中快速检测到更好地表达认知状态的模式激活； 2）通过同时收集的脑电图（EEG）数据来增强这些数据； 3）开发一个大脑数据的地图集，该地图集使用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机器学习方法来探索RT-FMRI神经反馈。我们的方法是首先创建与成像相比快速匹配的快速算法，从而允许“实时”应用程序。为此，我们将从图像中选择表达状态之间差异的特征（从技术上讲，我们将使用称为独立组件分析的方法来降低数据维度。）我们将通过通过其在大脑中的位置进行研究，并检查其所包含的频率，从而通过研究它们的位置来凝结EEG特征。我们将对旨在帮助我们看到他们做出冲动选择的倾向的志愿者进行实验 - 众所周知，这与他们成为吸毒者的可能性有关，以及在控制自己的渴望时跟踪大脑变化的实验。对于这些研究，我们将研究大型香烟使用者。香烟本身是对国家的严重健康负担，它也是更普遍的成瘾模型，因为众所周知，它具有许多共同的神经特征，即使用其他滥用药物，例如可卡因和甲基苯丙胺。这是一个分阶段的创新建议。第一阶段将集中于RT-FMRI分析和仪器技术的发展。在成功完成基于特定里程碑的成功完成后，我们将与人类受试者进行更应用的工作。公共卫生相关性：我们的研究旨在通过使用磁共振成像和脑电图来发展和表征一种快速检测与成瘾研究相关的大脑状态的方法。我们对冲动性和素描的渴望的州和标记特别感兴趣。我们的目标最终是拥有一个可以在神经反馈背景下使用的工具，使人类或患者能够在其大脑中获得与这些状态相关的活动的指示，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。