Dense Array Image compatible EEG for enhanced neonatal care

新生儿脑病是中枢神经系统疾病，通常伴有癫痫发作。 癫痫发作是癫痫的独特临床表现之一，缺氧、分娩异常、睡眠 剥夺和压力。磁共振成像（MRI）在诊断和治疗中起着至关重要的作用。 了解新生儿癫痫。然而，新生儿MRI评价在评估整个 新生儿的神经系统状态，尤其是皮质功能。在这种情况下，连续 视频EEG可以是有用的，因为它提供关于频率，同步性， 分布和大脑皮层活动的其他特征。脑电图也是一个关键的模态， 从儿童早期开始理解发育障碍。最先进的EEG或密集阵列EEG （HD-EEG - 64或更多通道）已经实现了EEG作为神经成像工具的潜力 通过正常和病理性大脑活动和网络动态的源定位。但无论是 常规EEG和HD-EEG都是成像（MRI或CT）兼容的;因此，EEG电极通常 在任何成像研究之前删除，由于额外延迟对患者管理产生负面影响 和额外费用。 该R 01项目的目标是证明开发成像兼容的 HD-EEG网络用于跨模态新生儿神经监测，具有无伪影图像质量。拟议 新生儿HD-EEG网络或“NeoNet”将利用创新3D打印的专业知识进行设计 技术和薄膜沉积在A. A.马萨诸塞州总医院马蒂诺中心。严格 将使用Finite对特定吸收沉积速率和温度进行安全性评估 使用解剖学上精确的男性和女性2周龄新生儿进行有限元法（FEM）模拟 全身模型，将向公众发布。模拟将通过实际温度进行验证 使用佩戴NeoNet的新生儿幻影测量感应射频加热，并与 单独使用体模的黄金标准，以及使用传统铜构建的商业MR兼容网络 电线技术同样，将MRI数据质量与单独体模金标准的数据进行比较， 并与商业HD-EEG数据进行对比。还将评价CT数据完整性。 拟议的NeoNet将实现廉价，无创的HD-EEG，并克服当前的交叉模态安全性 和伪影问题，到目前为止，这些问题严重限制了同步HD-EEG/MRI的有效性， 研究人员和临床医生从MRI的高空间分辨率和MRI的高时间分辨率中受益。 HD-EEG此外，该技术将重量轻，体积小，利用先进的 制造技术。新的NeoNet将允许研究健康新生儿的脑功能， 自然环境，以及不同的新生儿神经病理学的理解，如癫痫。

Neonatal encephalopathies are central nervous system disorders that are often accompanied by seizures. Seizures are one of the distinctive clinical manifestations of epilepsy, hypoxia, abnormal delivery, sleep deprivation and stress. Magnetic Resonance Imaging (MRI) plays a crucial role in the diagnosis and understanding of neonatal seizures. However, neonatal MRI evaluation is incomplete in assessing the entire neonate’s neurologic status, especially in regards to cortical functioning. In such circumstances, continuous video EEG can be useful as it provides important information about changes in frequency, synchrony, distribution and other characteristics of cerebral cortical activity. EEG is also a key modality in the understanding of developmental disabilities from early childhood. State-of-the-art EEG or dense array EEG (HD-EEG – 64 or more channels) has enabled the realization of EEG’s potential as a neuroimaging tool through source localization of normal and pathological brain activity and network dynamics. However, neither conventional EEG nor HD-EEG are imaging (MRI or CT) compatible; hence, EEG electrodes are typically removed prior to any imaging study, with negative impacts on patient management because of extra delays and additional costs. The goal of this R01 project is to demonstrate the feasibility and safety of developing an imaging-compatible HD-EEG net for cross-modal neonatal neural monitoring with artifact-free image quality. The proposed neonatal HD-EEG net or “NeoNet” will be designed by leveraging expertise in innovative 3D printing technology and thin film deposition at the A. A. Martinos Center, Massachusetts General Hospital. Rigorous safety assessment of specific absorption deposition rate and temperature will be performed using Finite Elements Method (FEM) simulations employing anatomically accurate male and female 2-week-old neonatal whole body models, which will be released to the public. Simulations will be validated by actual temperature measurements of induced RF heating using neonate phantoms wearing the NeoNet and compared against the gold standard of the phantom alone and against a commercial MR-compatible net built with traditional copper wire technology. Similarly, MRI data quality will be compared to data from the phantom-alone gold standard, and against data from the commercial HD-EEG. CT data integrity will also be evaluated. The proposed NeoNet will enable inexpensive, noninvasive HD-EEG and overcome current cross-modal safety and artifact issues that have so far severely limited the effectiveness of simultaneous HD-EEG/MRI allowing researchers and clinicians to benefit from the high spatial resolution of MRI and the high temporal resolution of HD-EEG. Furthermore, the technology will be light weight and small in size, taking advantage of advanced manufacturing technologies. The novel NeoNet will allow the study of brain function in healthy neonates in natural settings, as well as the understanding of different neonatal neural pathologies, such as epilepsy.