Integrated Circuits for Next-Generation Neural Implants

用于下一代神经植入物的集成电路

• 批准号: RGPIN-2022-04957
• 负责人: Liu, Xilin
• 金额: $ 1.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751623
• 关键词: Integrated; Circuits; Next; Generation; Neural

About 3.6 million Canadians are affected by neurological conditions, such as epilepsy and Parkinson's disease. Neural implants are emerging medical devices that can treat neurological conditions by providing therapeutic stimulations (similar to a heart pacemaker) to a patient in response to the real-time detection of irregular biomarkers in the neural signals (e.g., seizures). Neural implants can achieve superior clinical efficacy over conventional medications, especially in treating intractable symptoms. Integrated circuits (ICs) have been developed for integrating neural implants into miniature chips, which can greatly improve performance and reduce device size (thus reducing surgical invasiveness). Although IC designs for neural implants have advanced significantly in recent years, three major limitations exist. Firstly, neural interfacing in most designs is limited to electrical signals alone, missing key biomarkers of irregular neural states in chemical signals (e.g., dopamine levels). Secondly, neural signal processing algorithms are often oversimplified during IC integration, rendering poor performance in detecting biomarkers for stimulation. Although machine learning algorithms have shown advantages in offline studies, they are not adopted due to the lack of energy-efficient IC implementation. Thirdly, electrodes implanted in different brain regions are often connected to a centralized IC through long wires, causing signal contamination and risks of wire displacement. Our long-term goal is to develop ICs for next-generation neural implants with paradigm shifts in neural interfacing modalities (from electrical signal alone to multi-modal), signal processing capabilities (from coarse detection to machine learning), and system topology (from centralized to distributed). Specifically, we have three short-term objectives: (1) develop novel multi-modal neural interfacing ICs to capture comprehensive neural activities for improved diagnosis; (2) develop energy-efficient ICs to support high performance biomarker detection by machine learning; (3) develop a low-power, short-latency wireless network to enable a distributed neural implant system eliminating long wires. IC designs with fundamental innovations are proposed to achieve these goals. This research program will advance the performance, safety, and reliability of neural implants by novel IC designs. These ICs can be fabricated at low costs and enable treatments that can benefit a large patient population, especially those disadvantaged due to income and healthcare inequalities. The advanced IC design techniques can also be applied in a wide range of applications, providing great opportunities for entrepreneurship and technology transfer. Furthermore, this program will provide HQP with multi-disciplinary training, including IC designs for neural interfacing, machine learning, and wireless communication.

大约 360 万加拿大人患有癫痫和帕金森病等神经系统疾病。神经植入物是新兴的医疗设备，可以通过实时检测神经信号中不规则的生物标志物（例如癫痫发作）向患者提供治疗刺激（类似于心脏起搏器）来治疗神经系统疾病。神经植入物可以取得比传统药物更优越的临床疗效，特别是在治疗顽固性症状方面。集成电路（IC）已被开发用于将神经植入物集成到微型芯片中，这可以大大提高性能并减小设备尺寸（从而减少手术侵入性）。 尽管近年来神经植入物的 IC 设计取得了显着进步，但仍存在三个主要限制。首先，大多数设计中的神经接口仅限于电信号，缺少化学信号中不规则神经状态的关键生物标志物（例如多巴胺水平）。其次，神经信号处理算法在 IC 集成过程中往往过于简化，导致检测刺激生物标志物的性能较差。尽管机器学习算法在离线研究中显示出优势，但由于缺乏节能的 IC 实现而没有被采用。第三，植入不同大脑区域的电极通常通过长电线连接到中央IC，导致信号污染和电线移位的风险。 我们的长期目标是开发用于下一代神经植入物的 IC，在神经接口模式（从单独的电信号到多模态）、信号处理能力（从粗略检测到机器学习）和系统拓扑（从集中式到分布式）方面发生范式转变。具体来说，我们有三个短期目标：（1）开发新型多模式神经接口IC来捕获全面的神经活动以改进诊断； (2) 开发节能IC，支持机器学习的高性能生物标志物检测； (3) 开发低功耗、短延迟的无线网络，使分布式神经植入系统能够消除长电线。为了实现这些目标，提出了具有根本性创新的 IC 设计。 该研究计划将通过新颖的 IC 设计来提高神经植入物的性能、安全性和可靠性。这些 IC 可以低成本制造，并可实现使大量患者受益的治疗，特别是那些因收入和医疗保健不平等而处于不利地位的患者。先进的IC设计技术也可以得到广泛的应用，为创业和技术转移提供了巨大的机会。此外，该项目将为总部提供多学科培训，包括神经接口、机器学习和无线通信的 IC 设计。