Direct MEG/EEG detection using a novel MRI approach

此R21补助金响应RFA-EY-17-001“脑倡议：新概念和早期研究， 神经系统中的大规模记录和调制. 甚至更早的发展阶段.包括在概念化的初始阶段的新的和未经检验的想法. 在没有初步数据的情况下”。脑电图（EEG）和脑磁图 (MEG)测量与神经元电流相关的磁场是唯一的工具 目前可用于非侵入性地测量人类大脑中的电活动。它们提供了丰富的信息 癫痫、痴呆、自闭症、抑郁症、中风和创伤的脑电活动， 诊断和手术计划。然而，EEG电极检测到的神经元电流受到以下因素的干扰： 介入脑，脑脊液，头骨和头皮组织，而MEG需要超导磁力计和一个 安静的磁场环境此外，EEG和MEG中的信号源本身不是空间编码的 并提出一个没有唯一解的逆问题。而磁共振成像（MRI）在 用纤维轨迹映射和功能性MRI（fMRI）方法在大脑倡议中的作用， 由于MRI对EEG/MEG信号敏感，因此迄今为止尝试使用MRI来测量神经元电活动 被证明是不确定的或者无效的。在癫痫峰期间，MEG信号的范围从10-15到10-12特斯拉（T），而 MRI对神经源的灵敏度估计仅为约10-10 T。 寻求开发一种全新的MRI方法来直接测量MEG调制MRI的支持 使用提供异常高的固有信噪比（SNR）的空间编码方案的信号 和速度.新的定位方法-光谱与线性代数建模或SLAM-使用一个 大大降低SNR优化的梯度编码集，直接定位来自小体积大脑的信号 可以在采集后从scout MRI中任意分割和调整大小的组织。测试表明，SLAM 可以提供100 - 105的SNR，对于约10 ml的脑水信号产生10-14-10- 15 T的计算灵敏度 约30 ms采集（约30 Hz带宽）中的体积。机械地，MRI信号的MEG/EEG调制 通过谱密度函数的零/低频偶极-偶极分量产生，其直接 与自旋-自旋弛豫速率1/T2成正比。计划是持续应用T2敏感SLAM MRI， ~30 Hz检测健康人在有意义的~ 30 Hz EEG范围内~10- 14 T信号的MEG/EEG调制 人类志愿者同步MRI兼容EEG将用作参考，以识别信号，检测 伪影并优化相关信号的检测。检测静息状态、视觉和听觉诱发 潜力是有计划的。如果成功的话，该基金将提供基于MRI的非侵入性定位和尺寸确定， 神经元信号源，然后可能直接与功能磁共振成像，纤维跟踪和其他MRI指标。

This R21 grant responds to RFA-EY-17-001 “BRAIN Initiative: New Concepts and Early-Stage Research for Large-Scale Recording and Modulation in the Nervous System …for unique and innovative technologies in an even earlier stage of development … including new and untested ideas in the initial stages of conceptualization… where preliminary data would not be available”. Electroencephalography (EEG) and magnetoencephalography (MEG) which measures the magnetic fields associated with the neuronal currents are the only tools currently available to noninvasively measure electrical activity in human brain. They provide a wealth of information on brain electrical activity in epilepsy, dementias, autism, depression, stroke, and trauma for clinical diagnosis and surgical planning. Yet the neuronal currents detected by EEG electrodes are confounded by intervening brain, CSF, skull and scalp tissues, while MEG requires superconducting magnetometers and a quiet magnetic environment. Moreover, the signal sources in EEG and MEG are not spatially-encoded per se and pose an inverse problem without a unique solution. While magnetic resonance imaging (MRI) plays a key role in The Brain Initiative with fiber track mapping and functional MRI (fMRI) methods and it aught to be sensitive to EEG/MEG signals, attempts to use MRI to measure neuronal electrical activity have thus far proved inconclusive or null. The MEG signals range from 10-15 to 10-12 Tesla(T) during epileptic spikes, while MRI’s sensitivity to neuronal sources is estimated at only about 10-10 T. Support is sought to develop an entirely new MRI approach to directly measure MEG-modulated MRI signals using a spatial encoding scheme that provides exceptionally high intrinsic signal-to-noise ratios (SNR) and speed. The new method of localization–spectroscopy with linear algebraic modeling or SLAM–uses a greatly-reduced SNR-optimized gradient encoding set to directly localize signals from small volumes of brain tissue that can be arbitrarily segmented and resized post-acquisition, from scout MRI. Tests show that SLAM can deliver an SNR of ≈105, yielding a calculated sensitivity of 10-14-10-15T for the brain water signal in ~10ml volumes in ~30ms acquisitions (~30Hz bandwidth). Mechanistically, MEG/EEG modulation of MRI signals arises via the zero/low-frequency dipole-dipole component of the spectral density function, which is directly proportional to the spin-spin relaxation rate, 1/T2. The plan is to continuously apply T2-sensitive SLAM MRI at ~30 Hz to detect MEG/EEG modulations of ~10-14T signals over a meaningful ~30Hz EEG range in healthy human volunteers. Simultaneous MRI-compatible EEG will be used for reference to identify signals, detect artifacts and optimize detection of relevant signals. Studies to detect resting-state, visual- and audio-evoked potentials are planned. If successful, the grant would deliver noninvasive MRI-based localization and sizing of neuronal signal sources which might then be correlated directly with fMRI, fiber-tracking and other MRI metrics.