EAGER: Numerical Simulation of Neural Current MR Imaging Experiments

这项EAGER奖资助了一项关于测量脑组织内神经电流的新技术的理论研究。该研究项目由加州大学圣地亚哥分校的Lawrence Frank和Terrence Sejnowski教授领导。这项研究项目是由这项拨款资助的，它关注的是理解和测量人脑中发生的电信号过程的新方法，这些电信号是大脑活动的信号。目前的技术包括功能性磁共振成像（FMRI），但这些方法只能通过触发大脑周围或大脑内部血液中的电变化来间接操作。因此，这种技术受到以下事实的限制：血流会导致与空间分离（功能磁共振成像检测之前血液流动的距离）有关的某种基本不准确性，以及将大脑中原始电活动的时间与其随后对周围血液的影响分开的时间滞后。相比之下，在这项研究中，PI将分析一种新技术，该技术可以通过直接关注脑组织内的神经电流来完全克服这些限制。这项新技术被称为“神经电流MRI”，PI将对这些神经电流的影响进行建模，并通过对这种大脑活动进行详细的数值模拟来检测它们的可能性。这样一个项目的含义和更广泛的影响是重大的。这样一项研究的结果可能有助于为一种全新的技术铺平道路，通过这种技术，我们可能能够以前所未有的精度对大脑进行成像。此外，由于本提案中的研究跨越了许多传统的边界（包括生物学，物理学和计算数学），因此具有高度的跨学科性。因此，这项研究的结果有可能使各种相关学科的研究受益。

This EAGER award funds a theoretical study of a proposed new technology for measuring neural currents within brain tissue. This research project is headed by Professors Lawrence Frank and Terrence Sejnowski at UC San Diego. The research program funded by this grant concerns new methods of understanding and measuring the electrical processes that occur within the human brain and which signal brain activity. Current techniques involve functional magnetic resonance imaging (FMRI), but these methods only operate indirectly by triggering on the electrical changes that occur in the blood surrounding the brain or within the brain. Such techniques are therefore limited by the fact that blood flow induces a certain fundamental inaccuracy having to do with spatial separation (the distance of blood flowing prior to FMRI detection) as well as a time lag which separates the time of the original electrical activity in the brain and its subsequent impact on the surrounding blood. In this study, by contrast, the PI's will analyze a new technology which might be able to completely overcome these limitations by focusing directly on the neural currents within the brain tissue itself. This new technology is referred to as "neural current MRI", and the PI's will model the effects of these neural currents and the possibilities for their detection by conducting a detailed numerical simulation of such brain activity. The implications and broader impacts of such a project are significant. The results of such a study might help to pave the way for a whole new technology by which we might be able to image the brain with a precision never previously achieved. Moreover, because the research in this proposal crosses many traditional boundaries (including biology, physics, and computational mathematics), it is highly interdisciplinary. The results of this study therefore have the potential to benefit research in a variety of related disciplines.