Real-time fMRI using brain-state classification
Real-time fMRI using brain-state classification
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DOI：
10.1002/hbm.20326
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发表时间：
2007-10-01
影响因子：
4.8
通讯作者：
Hu, Xiaoping P.
中科院分区：
文献类型：
--
作者：
LaConte, Stephen M.;Peltier, Scott J.;Hu, Xiaoping P.
我们实现了基于多元分类的实时功能磁共振成像系统。这种方法与空间局部实时实现明显不同，因为它不需要关于功能定位和个体表现策略的事先假设，并且能够基于大脑状态的直观转换而不是局部波动提供反馈。因此，这种方法提供了一种新型实验设计的能力，其中刺激的实时反馈控制是可能的——而不是使用固定的范式，实验可以随着受试者接收大脑状态反馈而自适应地发展。在本报告中，我们描述了我们的实施并描述了其性能能力。我们使用全脑、模块设计、运动数据观察到类似 80% 的分类准确率。在左右运动任务条件下，任务切换（在快速按下左或右食指按钮之间变化）产生的初始瞬态期与持续活动期间的后续稳定期之间存在重要差异。进一步的分析表明，在稳定的任务期间可以实现非常高的准确度，并且分类器对任务条件变化的响应速度可以比信号时间达到峰值速率快得多。最后，我们证明了这种实现在行为任务方面的多功能性，表明我们的结果适用于一系列认知领域。除了基础研究之外，该技术还可以补充基于脑电图的脑机接口研究，并在生物反馈康复、测谎、学习研究、基于虚拟现实的训练和增强意识等领域具有潜在的应用。
We have implemented a real-time functional magnetic resonance imaging system based on multivariate classification. This approach is distinctly different from spatially localized real-time implementations, since it does not require prior assumptions about functional localization and individual performance strategies, and has the ability to provide feedback based on intuitive translations of brain state rather than localized fluctuations. Thus this approach provides the capability for a new class of experimental designs in which real-time feedback control of the stimulus is possible-rather than using a fixed paradigm, experiments can adaptively evolve as subjects receive brain-state feedback. In this report, we describe our implementation and characterize its performance capabilities. We observed similar to 80% classification accuracy using whole brain, block-design, motor data. Within both left and right motor task conditions, important differences exist between the initial transient period produced by task switching (changing between rapid left or right index finger button presses) and the subsequent stable period during sustained activity. Further analysis revealed that very high accuracy is achievable during stable task periods, and that the responsiveness of the classifier to changes in task condition can be much faster than signal time-to-peak rates. Finally, we demonstrate the versatility of this implementation with respect to behavioral task, suggesting that our results are applicable across a spectrum of cognitive domains. Beyond basic research, this technology can complement electroencephalography-based brain computer interface research, and has potential applications in the areas of biofeedback rehabilitation, lie detection, learning studies, virtual reality-based training, and enhanced conscious awareness.