Temporally Adaptive fMRI

时间自适应功能磁共振成像

• 批准号: 7001247
• 负责人: STEPHEN M LACONTE
• 金额: $ 17.27万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7001247
• 关键词: behavior prediction; behavior test; behavioral /social science research tag; biofeedback; bioimaging /biomedical imaging; brain mapping; choice; clinical research; functional magnetic resonance imaging; human subject; learning; mathematical model; model design /development; preference; psychological models; statistics /biometry; stimulus /response; time resolved data

DESCRIPTION (provided by applicant): This research plan envisages a methodological advance in functional MRI (fMRI) to allow for adaptive stimulus presentation derived directly from the acquired image data. Adaptation of stimuli will be accomplished by modeling fMRI to classify brain states during the image reconstruction process, and subsequently modulating a visual display. This emphasis on image-based prediction constitutes a fundamental shift from the conventional approach of using temporal changes in images to detect spatial "hot spots". This research will generate significant insights and development of capabilities for adaptive fMRI experiments using prediction of brain states. This has several significant applications. Primary among these is the potential contribution to designing much more flexible experiments to enhance our basic understanding of brain function. Also relevant are biofeedback rehabilitation, therapeutic meditation, learning studies, sports therapy or other virtual reality-based training, and lie-detection. Moreover this approach will provide spatially resolved data that complements ongoing EEG-based brain computer interface (BCI) research. The experimental plan incorporates a constructive progression that first develops offtine predictive algorithms to a range, of fMRI experminents, secondly treats the case of measurable human learning characterized by offline analysis, and ifinally utilizes these initial studies to characterize system comprising a real-time machine learning algorithm coupled with a responsive human volunteer. Long-term goal: Initiate a research program that will enhance current spatial mapping studies by allowing for temporal classification of brain states based on image data and biofeedback capabilities for adaptive fMRI experiments. Specific Aims: 1) Characterize the relationship between choice of fMRI task and choice of predictive technique to examine the importance of the particular predictive model, the connection between task difficulty and modeling accuracy, and the amount of training data required to build accurate predictive models. 2) Analyze fMRi data from a motor-learning task to study how behaviorally demonstrated learning by a subject corresponds with changes in the image data, and if this effect is directly observable using predictive models. 3) Develop capabilities to perform real-time feedback of stimulus based on interaction between a predictive algorithm and subject adaptation.

描述（由申请人提供）： 该研究计划设想在功能性 MRI (fMRI) 方面取得方法上的进步，以允许直接从所采集的图像数据导出自适应刺激呈现。刺激的适应将通过功能磁共振成像建模来对图像重建过程中的大脑状态进行分类，然后调制视觉显示来完成。这种对基于图像的预测的强调构成了与使用图像中的时间变化来检测空间“热点”的传统方法的根本转变。这项研究将为使用大脑状态预测的自适应功能磁共振成像实验产生重要的见解和能力发展。这有几个重要的应用。其中最主要的是设计更灵活的实验以增强我们对大脑功能的基本理解的潜在贡献。同样相关的还有生物反馈康复、治疗冥想、学习研究、运动疗法或其他基于虚拟现实的训练以及测谎。此外，这种方法将提供空间解析数据，补充正在进行的基于脑电图的脑机接口（BCI）研究。 该实验计划包含一个建设性的进展，首先为一系列功能磁共振成像实验开发离线预测算法，其次处理以离线分析为特征的可测量人类学习的情况，最后利用这些初步研究来表征包含实时机器学习的系统算法与反应灵敏的人类志愿者相结合。 长期目标：启动一项研究计划，通过基于图像数据和自适应功能磁共振实验的生物反馈功能对大脑状态进行时间分类，增强当前的空间映射研究。 具体目标： 1) 表征功能磁共振成像任务选择和预测技术选择之间的关系，以检查特定预测模型的重要性、任务难度和建模准确性之间的联系，以及构建准确预测模型所需的训练数据量。 2) 分析来自运动学习任务的 fMRI 数据，以研究受试者的行为表现学习如何与图像数据的变化相对应，以及是否可以使用预测模型直接观察到这种效果。 3）开发基于预测算法和受试者适应之间的相互作用执行刺激实时反馈的能力。