High-Resolution Transcranial Ultrasound Neuromodulation at Large Scale

大规模高分辨率经颅超声神经调节

• 批准号: 2143557
• 负责人: Mehdi Kiani
• 金额: $ 45万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143557&HistoricalAwards=false
• 关键词: Resolution; Transcranial; Ultrasound; Neuromodulation; Large

High-Resolution Transcranial Ultrasound Neuromodulation at Large ScaleNeuromodulation has the potential to map neural functions; enhance our perceptual, motor, and cognitive capabilities; and restore sensory and motor functions lost through injury or disease. Despite decades of research and development, state-of-the-art noninvasive neuromodulation techniques still suffer from extremely poor spatial resolution (100-1000’s of mm3). This project includes scientific research that explores orthogonal crossed beams of ultrasound as a noninvasive transcranial means for unprecedented 0.1 mm3 spatial resolution neuromodulation at large scale. Compared to its noninvasive counterparts, this crossed-beam ultrasound neuromodulation technology has the potential to improve the spatial resolution (focal spot) by several orders of magnitude. Therefore, it will yield a unique building block for a comprehensive set of noninvasive neural interfaces. It will open new opportunities in neuroscience with significant improvements in spatial resolution and coverage of noninvasive neuromodulation of the brain, initially in animals. Ultimately, it will also have huge translational potential for many clinical applications in humans, such as the treatment of neurological and psychiatric disorders and brain-machine interfaces. Leveraging the multidisciplinary nature of the research, this project also includes a significant integrated outreach and educational component created around a “Machine-Learning-inspired Physical Troubleshooting” framework to impact K-12 teachers and students, minorities, and undergraduate and graduate students. The troubleshooting framework will stimulate the interest of K-12 students in electrical engineering to recruit more students (particularly women) to this major, will educate a broad audience from undergraduate students to K-12 teachers and their students (particularly pre-college female students) in the science and applications of this research, and will enhance teachers’ and students’ research skills through systematic troubleshooting and problem-solving activities. Graduate curriculum on circuits and optimization-based machine learning will also be transformed with multidisciplinary projects and guest lectures to educate graduate students in the design and applications of smart integrated systems.This project proposes and explores high-resolution transcranial ultrasound stimulation (HR-TUS) system, in which extracranial ultrasound transducer arrays electronically steer ≤ 1 MHz crossed focused ultrasound beams, guided by imaging and machine learning models, at different neural targets with ultrasound pressure focal spots of 0.1 mm3. Building on the investigators’ complementary expertise in integrated circuits, ultrasound-based systems, wireless neural interfaces, and machine learning for image analysis, this project will establish the fundamental basis for large-scale HR-TUS with orthogonal crossed ultrasound beams guided by imaging and machine learning models. This project will investigate fundamental limits of spatial resolution and coverage within a human brain volume in HR-TUS by developing numerical and computational models based on wave equations to explore the effects of different geometries, frequencies, and configurations of phased arrays and their interactions with the skull and brain tissue in the context of orthogonal crossed beams. This project will also explore imaging and machine learning models for accurate anatomical targeting, focusing, and beam crossing in the presence of skull/tissue effects on ultrasound beams and displacements in ambulatory subjects. To reduce the system complexity, size, and power consumption in three-dimensional stimulation of tissues at large scale, the novel solution of this project is a large two-dimensional array on a flexible substrate consisting of optimally arranged modular selectable linear arrays and their application-specific integrated circuits. A system-level demonstration at the end of this project will establish the feasibility of the HR-TUS. The image-guided HR-TUS system with machine learning model will provide a first-in-class platform for learning-based acoustically guided transcranial ultrasound neuromodulation (all acoustic) with high spatial resolution ( 0.1 mm3) at large scale (over the whole brain).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

高分辨率经颅超声大规模神经调节神经调节具有绘制神经功能的潜力；增强我们的感知、运动和认知能力；并恢复因受伤或疾病而丧失的感觉和运动功能。尽管几十年的研究和发展，最先进的无创神经调节技术仍然受到极低的空间分辨率（100-1000毫米3）的影响。该项目包括科学研究，探索正交交叉超声束作为一种无创经颅手段，用于前所未有的0.1 mm3空间分辨率的大规模神经调节。与非侵入性技术相比，这种交叉波束超声神经调节技术有可能将空间分辨率（焦点）提高几个数量级。因此，它将为一套全面的非侵入性神经接口提供一个独特的构建块。它将为神经科学开辟新的机会，在空间分辨率和覆盖大脑的非侵入性神经调节方面取得重大进展，最初是在动物身上。最终，它还将在人类的许多临床应用中具有巨大的转化潜力，例如神经和精神疾病的治疗以及脑机接口。利用研究的多学科性质，该项目还包括围绕“机器学习启发的物理故障排除”框架创建的重要综合外展和教育组件，以影响K-12教师和学生，少数民族以及本科生和研究生。故障排除框架将激发K-12电气工程专业学生的兴趣，以招收更多的学生（特别是女性）进入该专业，将教育广泛的受众，从本科生到K-12教师及其学生（特别是大学预科女生）了解本研究的科学和应用，并将通过系统的故障排除和解决问题的活动提高教师和学生的研究技能。电路和基于优化的机器学习的研究生课程也将转变为多学科项目和客座讲座，以教育研究生在智能集成系统的设计和应用方面。本项目提出并探索高分辨率经颅超声刺激（HR-TUS）系统，其中颅外超声换能器阵列在成像和机器学习模型的引导下，以电子方式引导≤1 MHz的交叉聚焦超声波束，针对不同的神经目标，超声压力焦点为0.1 mm3。基于研究人员在集成电路、基于超声波的系统、无线神经接口和用于图像分析的机器学习方面的互补专业知识，该项目将为成像和机器学习模型引导的正交交叉超声光束的大规模HR-TUS奠定基础。该项目将通过基于波动方程的数值和计算模型来研究HR-TUS中人脑体积的空间分辨率和覆盖范围的基本限制，以探索相控阵的不同几何形状、频率和配置的影响，以及它们在正交交叉光束背景下与头骨和脑组织的相互作用。该项目还将探索成像和机器学习模型，以便在颅骨/组织对超声光束和流动受试者位移的影响下进行精确的解剖定位、聚焦和光束交叉。为了减少大规模组织三维刺激的系统复杂性、尺寸和功耗，本项目的新解决方案是在柔性衬底上安装一个大型二维阵列，该阵列由优化排列的模块化可选线性阵列及其专用集成电路组成。项目结束时的系统级演示将确定HR-TUS的可行性。具有机器学习模型的图像引导HR-TUS系统将为大规模（全脑）的高空间分辨率（0.1 mm3）的基于学习的声学引导经颅超声神经调节（全声学）提供一流的平台。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Towards High-Resolution Ultrasound Neuromodulation With Crossed-Beam Phased Arrays

• DOI: 10.1109/tbcas.2023.3285724
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Biomedical Circuits and Systems
• 影响因子: 5.1
• 作者: S. Ilham;M. Kiani