Temporally Adaptive fMRI

描述(由申请人提供)： 这项研究计划设想了功能磁共振成像(FMRI)的方法学进步，以允许直接从获取的图像数据获得自适应刺激呈现。刺激的适应将通过对fMRI建模来实现，以在图像重建过程中对大脑状态进行分类，并随后调制视觉显示。这种对基于图像的预测的重视构成了与传统方法的根本转变，即使用图像中的时间变化来检测空间“热点”。这项研究将为使用大脑状态预测的自适应功能磁共振实验带来重要的见解和能力的发展。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。相关的还有生物反馈康复、治疗性冥想、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分辨数据，补充正在进行的基于脑电的脑机接口(BCI)研究。 实验计划结合了一个建设性的进展，首先开发出一系列fMRI实验的常规预测算法，然后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括实时机器学习算法和响应性人类志愿者的系统。 长期目标：启动一项研究计划，通过允许根据图像数据对大脑状态进行时间分类，并为自适应功能磁共振实验提供生物反馈能力，来增强当前的空间映射研究。 具体目标： 1)刻画fMRI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、任务难度和建模精度之间的关系，以及建立准确预测模型所需的训练数据量。 2)分析运动学习任务的fMRI数据，研究受试者在行为上展示的学习如何与图像数据的变化相对应，以及这种影响是否可以使用预测模型直接观察到。 3)开发基于预测算法和对象适应之间的交互来执行刺激的实时反馈的能力。

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.