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）： 该研究计划设想了功能性MRI（fMRI）中的方法论进步，以允许直接从获得的图像数据得出的自适应刺激呈现。刺激的适应性将通过对图像重建过程中的fMRI进行建模以对大脑状态进行分类，并随后调节视觉显示。对基于图像的预测的这种强调构成了从使用图像的时间变化来检测空间“热点”的常规方法的基本转变。这项研究将通过使用大脑状态的预测来产生重要的见解和能力来进行自适应fMRI实验。这有几个重要的应用。其中的主要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。还包括生物反馈康复，治疗冥想，学习研究，运动疗法或其他基于虚拟现实的培训以及谎言检测。此外，这种方法将提供空间解决的数据，以补充基于EEG的大脑计算机界面（BCI）研究。 实验计划结合了一个建设性的进步，该进步首先开发出一个范围，fMRI的吸收量的Offtine预测算法，其次是处理通过离线分析来表征的可测量人类学习的情况，并静静地利用这些初始研究来表征与响应的人类Volunewere ersevers coupples的实时机器学习Algorithm辅助的系统，以表征人类的实时机器学习。 长期目标：启动一项研究计划，该计划将通过图像数据和生物反馈功能对自适应fIMRI实验进行脑状态的时间分类，从而增强当前的空间映射研究。 具体目的： 1）表征FMRI任务的选择与选择预测技术之间的关系，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建立准确的预测模型所需的培训数据量。 2）分析来自运动学习任务的fMRI数据，以研究受试者的行为证明学习方式与图像数据的变化相对应，以及使用预测模型可以直接观察到这种效果。 3）根据预测算法和受试者适应性之间的相互作用，开发能够执行刺激实时反馈的能力。